6th International Workshop on Breast Densitometry and Breast ...

6 th <strong>International</strong> <strong>Workshop</strong> on 

Breast Densitometry and Breast 

Cancer Risk Assessment 

San Francisco 2013 

SPONSORED BY 

SUPPORTED BY 

Support is being provided by the American Cancer Society, through The 

Longaberger Company, a direct selling company offering home products 

including handcrafted baskets made in Ohio, and the Longaberger 

Horizon of Hope Campaign, which provided a grant to the American 

Cancer Society for breast cancer research and education. 

Hologic, Inc. 

Matakina <strong>International</strong> Limited 

U-Systems – A GE Healthcare Company 

VuCOMP, Inc.

TABLE OF CONTENTS 

6 th <strong>International</strong> <strong>Workshop</strong> on Breast Densitometry 

and Breast Cancer Risk Assessment 

Sponsors .................................................................................................................................................. 2 

Page 

Program Thursday, June 6 th .................................................................................................................. 3 

Program Friday, June 7 th ....................................................................................................................... 4 

Speakers Abstracts................................................................................................................................. 6-25 

Poster Abstracts...................................................................................................................................... 26-83 

Participants ............................................................................................................................................ 84-89 

Notes ........................................................................................................................................................ 90-93 

Feedback Form....................................................................................................................................... 94 

TABLE OF CONTENTS 

1



Sponsored by the 

Daniel and Phyllis Da Costa Funds of the CPMC Foundation 

Sutter Health at California Pacific Medical Center 

UCSF Department of Epidemiology & Biostatistics 

UCSF Department of Medicine 

UCSF Department of Radiology and Bio Medical Imaging 

SPONSORS 

Supported by 

California Breast Cancer Research Program 

American Cancer Society 

Support is being provided by the American Cancer Society, through The Longaberger Company, a direct selling 

company offering home products including handcrafted baskets made in Ohio, and the Longaberger Horizon of Hope 

Campaign, which provided a grant to the American Cancer Society for breast cancer research and education. 

Hologic, Inc. 

Matakina <strong>International</strong> Limited 

U-Systems – A GE Healthcare Company 

VuCOMP, Inc. 

2

PROGRAM 

Thursday, June <strong>6th</strong> 

8:00 - 9:00 Breakfast and Registration 

3 



9:00 - 9:10 Introduction 

9:10 - 9:20 Breast Density - What is a Woman to do NOW" 

Deborah Collyar 

The Politics and Practicality of Reporting Breast Density 

9:20 - 9:45 Caution – Kitty Litter, Silly Putty & Dense Breasts: Using State, Federal and 

Regulatory Efforts to Standardize the Communication of Dense Breast Tissue to 

Women 

Nancy M. Cappello, PhD - President & Founder, Are You Dense, Inc. 

9:45 - 10:10 Discussion of the New Legislation Mandating the Reporting of Breast Density, 

Perspectives from Breast Cancer Advocates Supporting the Legislation, Discussion 

of the Benefits and Practical Limitations of the Breast Density Reporting in a 

Clinical Mammography Setting. 

Joe Simitian, Santa Clara County Supervisor and former California State Senator 

10:10 - 10:50 Application of California SB 1538 Breast Density Notification Law in Clinical 

Practice (Panel) 

Meg Durbin, MD (Palo Alto Medical Foundation); Bonnie Joe, MD, PhD (UCSF); 

Debra Ikeda, MD (Stanford); Ed Sickles, MD (UCSF) 

10:50 - 11:05 Break (15 minutes) 

11:05 - 11:20 ABSTRACT: CIRRUS: A Fully-Automated Software Platform for Breast Cancer 

Risk Prediction from Mammograms 

John Hopper, PhD - The University of Melbourne 

11:20 - 11:35 ABSTRACT: Several Mammographic Texture Measures, Including MTR, Add 

Simultaneously to Percentage Density Risk Segregation 

Mads Nielsen, PhD - University of Copenhagen 

11:35 - 12:30 Lunch (45 minutes) 

Comparative Measurement of Breast Density 

12:30 - 1:10 Comparison of Alternative Methods for Estimating Breast Density 

Isabella dos Santos Silva, MD, MSc, PhD - London School of Hygiene & Trophical 

Medicine 

1:10 - 1:50 Breast Cancer Risk: Is it Just About Breast Density 

Elizabeth Morris, MD, FACR - Memorial Sloan-Kettering Cancer Center 

1:50 - 2:30 Next Generation Measurements of Breast Density - Tomosynthesis 

Despina Kontos, PhD - University of Pennsylvania 

2:30 - 2:45 Break (15 minutes) 

Breast Density and Biology 

2:45 - 3:25 Molecular Microenvironments of Mammographically Dense Breast Tissue 

Melissa Troester, PhD, MPH - UNC School of Public Health 

3:25 - 4:05 Collagen Alignment Surrounding Breast Tumors Facilitates Tumor Spread and 

Metastasis 

Suzanne Ponik, PhD - University of Wisconsin 

4:05 - 4:45 Summary of Day One Panel Discussion 

6:00 Speakers Dinner: Café Majestic in the Hotel Majestic 

PROGRAM

PROGRAM 



PROGRAM 

Friday, June 7th 

8:30 - 9:00 Breakfast 

9:00 - 9:05 Introduction 

Genetics of Breast Density and Breast Cancer Risk 

9:05 - 9:45 Genetic Ancestry and Mammographic Density 

Elad Ziv, MD - UCSF 

9:45 - 10:25 The Relative Contributions of BI-RADS Density and Genetics to Breast Cancer 

Risk 

Celine Vachon, PhD - Mayo Clinic 

10:25 - 

10:45 

10:45 - 

11:25 

11:25 - 

12:05 

Break (15 minutes) 

Risk Modeling: Putting It All Together 

Breast Cancer Risk Prediction and Individualized Screening Based on Common 

Genetic Variations and Breast Density Measurements 

Hatef Darabi, PhD - Karolinska Inst 

Benign Breast Disease and Breast Density 

Jeffrey A. Tice, MD - UCSF 

12:05 - 1:00 Lunch (55 minutes) 

1:00 - 1:15 ABSTRACT: Determinants of Breast Tissue Composition in 15-18 year Old 

Girls 

Norman Boyd, MD, DSc, FRCPC - Ontario Cancer Institute 

1:15 - 1:30 ABSTRACT: The Effect of Weight Change on Changes in Breast Measures Over 

Menopause 

Hanneke Wanders, MSc -Julius Ctr for Health Sciences & Primary Care, Epidemiology 

UMC Utrecht 

Breast Density and Risk Assessment in Clinical Practice 

1:30 - 2:10 Awareness of Breast Density and Its Relationship to Mammography Sensitivity 

and Breast Cancer Risk Among U.S. Women: Results of a National Survey 

Deb Rhodes, MD - Mayo Clinic 

2:10 - 2:50 BreastCARE: A primary Care Clinic-Based RCT to Increase Breast Cancer 

Knowledge and Discussion of Risk and Lifestyle Behaviors 

Celia Kaplan, DrPH - UCSF 

2:50 - 3:05 Break (15 minutes) 

3:10 - 3:50 Impact of The Transition to Digital Mammography on the Benefits, Harms and 

Costs of Screening for Breast Cancer in the US 

Natasha K. Stout, PhD - Harvard 

3:50 - 4:30 SUMMARY AND CLOSING: Panel Discussion 

4



ABSTRACTS 

FOR 

SPEAKERS 

5

BREAST DENSITY – WHAT IS A WOMAN TO DO NOW 



Deborah Collyar 

925.260.1006 

Deborah@tumortime.com 

UCSF and Patient Advocates in Research (PAIR) 

ABSTRACTS FOR SPEAKERS 

The good news… breast density seems to help explain more of the previously unknown risk factors for 

breast cancer. Unfortunately, all known risk factors only account for ~50% of breast cancers cases. 

The bad news… the cart has been placed before the horse, so to speak. Legislation that requires doctors 

to inform women that they have high breast density sounds good, but without consistent measurements, 

standards, and plain-language principles, this may cause even more confusion and fear for women. 

Breast density shares similar conundrums that surfaced with BRCA1 and BRCA2 testing during its 

impetus. What options (let alone treatments) do doctors talk about with women who have high breast 

density What standard tool is being used to accurately assess breast density How do we explain the 

absolute risk of quadrant 3 and 4 (instead of relative risk), and how does that translate to the number of 

women in each category who actually get breast cancer each year 

Most importantly, who is going to put the information in context for each woman so that she can not only 

consider the options, but can also determine which ones fit her unique lifestyle or decision-making 

process for important life decisions 

Breast density is also clouded by the confusing controversies surrounding the timing and accuracy of 

mammograms. Younger women a higher likelihood of dense breasts, which equates to less accurate 

mammograms, and yet they are told to have annual mammograms because of their higher risk. How can 

this be explained clearly so that it actually makes sense 

For instance, if a woman is at higher risk for breast cancer because of high breast density, what type of 

breast cancer might she get Is it more or less aggressive (e.g. what subtype is most common) Will 

additional mammograms, ultrasounds, MRIs or tomosynthesis (3-D mammograms) help find it at an 

earlier stage that will actually change its course, or just find more false positives that have their own 

physical, financial, and emotional consequences for women and their families 

Standards and plain language are the most critical elements to ensure clear and useful information for 

women. Unfortunately, the genie is out of the bottle in California and other states without incorporating 

these elements. 

Women don’t need more uncertainty and anxiety, or unnecessary biopsies with subsequent over-treatment. 

We’ve already created this situation with Ductal Carcinoma In Situ (DCIS). Let’s not repeat this 

unfortunate reality with breast density. 

This situation establishes a moral imperative to quickly work together to create the standards that should 

have been established before the legislation became active. This call to action includes everyone who 

regulates, studies, screens, detects, explains, and/or treats breast cancer. 

6



CAUTION – KITTY LITTER, SILLY PUTTY & DENSE BREASTS: USING STATE, FEDERAL 

AND REGULATORY EFFORTS TO STANDARDIZE THE COMMUNICATION OF DENSE 

BREAST TISSUE TO WOMEN 

Nancy M. Cappello, Ph.D. 

Founder, Are You Dense Inc., Are You Dense Advocacy, Inc. 

203.232.9570 

nancy@areyoudense.org 

My radiologist knew I had dense breasts, my gynecologist knew I had dense breasts – the only one who 

did not know was me - the woman with the dense breasts. 

When I was diagnosed in 2004 with advanced stage breast cancer which metastasized to 13 lymph nodes, 

I was baffled. After all, I received my “normal” mammography report, “the Happy Gram” six weeks 

prior, in addition to a decade of normal mammography reports preceding this devastating news. How 

could my mammogram not find the “suspicious” 3 cm lesion the same day that the ultrasound discovered 

it I questioned my doctors; after all, isn’t this why I go for yearly screenings For the first time, I was 

told that my “dense breast tissue” prevented my mammograms from finding my cancer. 

Searching for information in lay publications, online cancer and medical organizations, I found nothing 

about this “dense” condition that harmed me greatly. I turned to the medical literature and, in 2004, 

during one of the most vulnerable times in my life, I uncovered over a decade of scientific studies which 

concluded: 

 

 

 

 

 

 

40% of women have dense breast tissue; 

Breast Density is the strongest predictor of the failure of mammography screening to detect cancer; 

There is a direct correlation between tumor size at discovery and long term survivability; 

Women with the densest breasts are at a greater risk of having an interval cancer; 

There are additional tests, when added to mammography, that can significantly detect early-stage 

invasive cancers in dense breasts; and 

Dense breast tissue is a well-established predictor of breast cancer risk 

Armed with knowledge, I shared the scientific evidence with my physicians. I asked them to consider informing 

patients about their dense tissue. Each of their responses was “No, it is not the standard of care.” 

I then turned to the Connecticut legislature and in 2005, Connecticut became the first state in the nation to 

legislate insurance coverage for ultrasound screening as a supplement to the mammogram for women 

with dense breast tissue. In 2009, Connecticut enacted another landmark state law which standardized the 

communication of dense breast tissue through the mammography report. 

Using state, federal and regulatory efforts and the birth of two nonprofit organizations, Are You Dense, 

Inc. and Are You Dense Advocacy, Inc., our mission has fueled a grassroots movement with impactful 

accomplishments. This is a testament to the fact that there is no shortage of women, with recent “normal” 

mammography reports and subsequent advanced cancer diagnoses, because of their dense breast tissue. 

Legislators, medical professionals and citizens have joined our mission to ensure that women with dense 

breast tissue are informed participants in discussions with their health-care providers about their personal 

breast screening surveillance. 


7




APPLICATION OF CALIFORNIA SB 1538 BREAST DENSITY NOTIFICATION LAW IN 

CLINICAL PRACTICE 

Meg Durbin (Palo Alto Medical Foundation), Debra M. Ikeda (Stanford University), Bonnie N. Joe 

(UCSF), Edward A Sickles (UCSF) 

On September 22, 2012 California State Senate Bill (SB 1538) (1) was signed into law mandating that 

facilities who perform mammograms notify women identified with heterogeneously dense or dense breast 

tissue (2) on mammograms “based on the Breast Imaging Reporting and Data System established by the 

American College of Radiology”, “include in the summary of the written report that is sent to the patient, 

as required by federal law, the following notice”: "Your mammogram shows that your breast tissue is 

dense. Dense breast tissue is common and is not abnormal. However, dense breast tissue can make it 

harder to evaluate the results of your mammogram and may also be associated with an increased risk of 

breast cancer. This information about the results of your mammogram is given to you to raise your 

awareness and to inform your conversations with your doctor. Together, you can decide which screening 

options are right for you. A report of your results was sent to your physician." This statement was to be 

included within the lay letter informing women of their mammogram result under the federal 

Mammography Quality Standards Act (42 U.S.C. Sec. 263b). The law further states “Nothing in this 

section shall be construed to create or impose liability on a health care facility for failing to comply with 

the requirements of this section prior to April 1, 2013. Nothing in this section shall be deemed to create a 

duty of care or other legal obligation beyond the duty to provide notice as set forth in this section.” In 

California, mandatory reporting requirements took effect on April 1, 2013 and is to remain in effect until 

January 1, 2019. 

Breast density notification laws are being considered in Florida, North Carolina, Oregon, and Utah 

(3), while California, Connecticut, New York, Texas, Virginia and Hawaii have passed laws. Connecticut 

screening breast ultrasound studies were reported recently (4-6), covered by insurance as directed by 

Connecticut law. However, the issue in California was how to frame policy to reconcile the legislative 

intent of SB1538 notification with realistic practice patterns since SB1538 provided no guidance on 

supplemental screening guidelines, and, unlike Connecticut, provided no funding for supplemental 

screening. In California, an academic and community-based working group of breast imagers and breast 

cancer risk specialists, the California Breast Density Information Group (CBDIG), formed in October 

2012 to investigate the literature related to issues raised by SB 1538 to provide an evidence-based 

response to provide information for women on breast density and help facilities prepare policies. The 

result was a free, institution-neutral website breastdensity.info, instantly translatable into multiple 

languages with frequently asked questions and evidenced-based recommendations for supplemental 

screening of women identified with dense breast tissue, based on breast cancer risk assessment from 

nationally recognized guidelines. This presentation will show data indicating that approximately 50% of 

women have heterogeneously dense or dense tissue on mammography (7) and report the results of the 

CBDIG website. However, we will also illustrate the impact of SB 1538 on facilities to prepare to notify 

and educate the approximately 2 million women with dense breast tissue, and their healthcare providers. 

Furthermore we will discuss gaps in information on how best to measure and report breast density, 

controversies on the risk of dense breast tissue, lack of validated models incorporating breast density into 

risk assessment, lack large trial data on supplemental screening in intermediate or average risk women, 

and preliminary data on tomosynthesis. 

1. http://www.leginfo.ca.gov/pub/11-12/bill/sen/sb_1501-1550/sb_1538_bill_20120922_chaptered.html (accessed May 3, 2013) 

2 D'Orsi C, Mendelson E, Ikeda D, al. e. Breast Imaging Reporting and Data System: ACR BI-RADS -- Breast Imaging Atlas. Reston, VA: 

American College of Radiology; 2003. 

3. Lee CI, Bassett LW, Lehman CD. Breast density legislations and opportunities for patient-centered outcomes research. Radiology 2012 

Vol 264 No 3: 632-636 

4 Weigert J, Steenbergen S. The connecticut experiment: the role of ultrasound in the screening of women with dense breasts. Breast J. 

2012;18(6):517-22. 

5. Parris T, Wakefield D, Frimmer H. Real world performance of screening breast ultrasound following enactment of Connecticut Bill 458. 

Breast J. 2013;19(1):64-70. 

6. Hooley RJ, Greenberg KL, Stackhouse RM, Geisel JL, Butler RS, Philpotts LE. Screening US in patients with mammographically dense 

breasts: initial experience with Connecticut Public Act 09-41. Radiology. 2012;265(1):59-69. 

7. D'orsi C, Mendelson E, Morris E, al e. ACR BI-RADS: Breast Imaging Reporting and Data System. 5th ed ed. Reston, VA: American 

College of Radiology; 2013 (in press). 

8



CIRRUS: A FULLY-AUTOMATED SOFTWARE PLATFORM FOR BREAST CANCER RISK 

PREDICTION FROM MAMMOGRAMS 

Daniel F. Schmidt, Enes Makalic, Tuong L. Nguyen, Laura Baglietto, Kavitha Krishnan, Jennifer Stone, 

Carmel Apicella, Melissa C. Southey, Dallas R. English, Graham G. Giles, John L. Hopper 

Centre for Molecular, Environmental, Genetic and Analytic Epidemiology, The University of Melbourne, 

Carlton VIC 3053, Australia 

Background: There is information in mammograms, apart from the detection and masking of existing 

lesions, that predicts breast cancer risk. This concept is currently referred to as ‘mammographic density’, 

operationally defined by the total area of the mammographic image that appears light or white. The most 

commonly used software tool for measuring mammographic density is CUMULUS, a computer-assisted 

technique that derives the pixels in a digitised mammogram which correspond to user-determined 

‘mammographically dense regions’. CUMULUS measures of mammographic density, adjusted for age 

and body mass index, have been shown to predict breast cancer risk even after adjusting for other 

measured breast cancer risk factors. 

We have developed a fully-automated predictor of breast cancer risk from digitised mammographic 

images, which we call CIRRUS, that does not try to measure ‘mammographic density’ per se, but instead 

tries to identify agnostically the features in a mammographic image that best predict breast cancer risk. 

Methods: CIRRUS is based on image processing and machine learning techniques. It includes fully 

automated image cropping procedures which remove irrelevant features (e.g. pectoral muscle). In addition 

to the brightness of the pixels, CIRRUS uses information from other features in the mammogram, such as 

patterns and texture. 

CIRRUS was trained using 2,597 mammograms (608 cases and 1,989 controls) collected by the 

Melbourne Collaborative Cohort Study. Bayesian logistic regression with the ridge penalty was used to 

combine all the extracted image features into the final CIRRUS measure, which was compared with dense 

area and percent dense area measurements obtained using CUMULUS. The bootstrap technique was used 

to estimate the confidence intervals for the area under the receiver operator curve (AUC). We also 

measured CIRRUS for 544 monozygotic twin pairs (MZ), 339 dizygotic twin pairs (DZ) and 1,556 of 

their sisters from the Australian Mammographic Density Twins and Sisters Study, and analysed familial 

aspects under a multivariate normal model. 

Results: In terms of breast cancer risk prediction, for both dense area and percent dense area measured by 

CUMULUS the mean AUC was 0.58 (CI 95%: 0.57 – 0.61). For the CIRRUS measure the AUC was 0.62 

(CI 95%: 0.58 - 0.64). CIRRUS measures were not markedly associated with age or any other measured 

breast cancer risk factor, which explained



SEVERAL MAMMOGRAPHIC TEXTURE MEASURES, INCLUDING MTR, ADD 

SIMULTANEOUSLY TO PERCENTAGE DENSITY RISK SEGREGATION 

J Marques 1 , M Lillholm 1 , DR Jørgensen 1 , P Diao 2 , K Petersen 2 , N Karssemeijer 3 , M Nielsen 1,2 

1 University of Copenhagen, Dept. of Comp. Science, 2 Biomediq A/S, 3 Radboud University, Nijmegen 

Medical Centre 


Purpose: Mammographic percentage density (PD) is established as a strong risk factor for breast cancer. 

PD, however, relies on limited aspects of mammographic intensity appearance and variation – several 

other measures have been suggested to cover more structural aspects of mammographic parenchymal 

texture. We evaluate a comprehensive list of such measures in terms of their breast cancer risk 

segregation. Furthermore, we evaluate to which extent a machine-learning based texture measure, 

mammographic texture resemblance (MTR), and automated PD measurements add information. 

Materials and Methods: The study was based on a 495 women case-control cohort from the Dutch 

screening program (age 58.0 ± 5.7 years). Cases and controls were screened as cancer free at baseline; the 

250 controls remained healthy 4 years later whereas the 245 cases were diagnosed 2-4 year after the 

initial mammography. 

All baseline mammograms were analyzed to derive the following measures of mammographic texture: i) 

PD scores were assessed by a trained radiologist using a Cumulus-like tool and automatically using a 

software tool, ii) Lacunarity, Markovian, Laws, Run Length, Fourier, Power Law, Wavelet, Variation, and 

Fractal Dimension as described in the papers by Manduca 1 , Heine 2 , and Giger 3 , and iii) the MTR measure 

using a software tool by Biomediq 4 . 

Left and right MLO views were processed separately and odds ratio (OR) and area under the ROC curve 

(AUC) for separation between cases and controls were calculated for each measure. 

Two logistic regression combination models were constructed: i) using all the measures described above 

and ii) the same except the radiologist PD were excluded. Both models iteratively excluded any measures 

that did not add significantly to the model. 

Results: The risk segregations for each texture measure (left/right MLO) are given in Table 1. The 

logistic regression model based gave an OR of 3.36 (p



Table 1 The risk segregation of the included texture measures as odds ratio and AUC for left and right 

MLO views respectively. Stars indicate level of significance: * p



COMPARISON OF ALTERNATIVE METHODS FOR ESTIMATING BREAST DENSITY 

Isabel dos Santos Silva 1 , Steve Allen 2 , Amanda Eng 1 , Zoe Garland 1 , Sarah Vinnicombe 3 , Valerie 

McCormack 4 , JA Shepherd 5 , Mitch Dowsett 2 

1 London School of Hygiene and Tropical Medicine, London, England; 2 Royal Marsden Hospital, UK; 

3 University of Dundee, UK; 4 <strong>International</strong> Agency for Research on Cancer, France; 5 University of 

California 


Mammographic density (MD), a trait which closely reflects the amount of radio-dense (non-fatty) tissue of 

the breast, is one of the strongest known risk factors for breast cancer and a major determinant of 

sensitivity of mammographic screening. In many US states it is now mandatory for doctors to inform 

women if their breasts are dense (BI-RADS: 3 or 4), albeit it is unclear whether they will benefit from 

more intensive screening (e.g. shorter screening intervals, combination of mammography with ultrasound 

or MRI). 

Currently, there are several approaches to measuring MD in screen-film mammography. Qualitative 

assessments, such as BI-RADS, classify density into categories based on the subjective analysis of 

patterns observed on the mammogram while quantitative measurements determine the proportion of the 

projected breast area that is composed of radio-dense tissue on a digitised screen-film. The quantitative 

semi-automatic threshold method, as implemented by the Cumulus software, is regarded as the “gold 

standard” for screen-film mammography. However, this method has limitations: it is subjective (i.e. 

observer-dependent); its area-based nature may be less efficient than a volume-based approach; and it is a 

rather time-consuming and labour-intensive method. The latter, in particular, have precluded its 

incorporation into routine clinical/screening practice. Fully automated methods have been developed 

which attempt to use volumetric approaches to quantify more precisely breast density on digitised screenfilms. 

But despite their alleged higher precision, such volumetric methods do not appear to predict breast 

cancer risk more accurately than the area-based approaches. 

As screen-film mammography is being replaced by full-field digital mammography (FFDM), new fullyautomated 

volumetric methods have recently been developed for assessment of density from digital 

images. Evaluation of the performance of these methods have been mainly limited to establishing the 

correlations of their measurements with those from more established methods (e.g. BIRADS or Cumulus) 

and/or evaluating the extent to which their measurements are associated with known breast cancer risk 

factors. Ultimately, the best method for measuring breast density from digital images is the one that yields 

the strongest association with breast cancer risk. 

The development of a valid universal standard tool for measuring MD is vital for accurate risk prediction 

(on a population and individual level) and to tailor preventive measures, including breast screening, 

according to a woman’s risk. In this talk, I will present data from our on-going work on the evaluation of 

alternative methods of measuring breast density on digital images. These include three area-based methods: 

(i) visual qualitative assessment using the BI-RADS classification; (ii) a semi-automated assessment using 

a thresholding technique (Cumulus); and (iii) an automated ImageJ-based approach which mimics 

Cumulus (the latter two optimised for FFDM). In addition, we have evaluated three automated volumetric 

approaches: (iv) single X-ray absorptiometry (SXA) with calibration phantoms; (v) Volpara TM ; and (vi) 

Quantra TM . Overall, the findings showed that the density estimates yielded by the automated volumetric 

methods were at least as strongly associated with breast cancer risk as those produced by the area-based 

approaches. Thus, these results support the use of volumetric-based approaches in screening/clinical 

settings and in large-scale studies. 

12



BREAST CANCER RISK: IS IT JUST ABOUT BREAST DENSITY 

Elizabeth Morris MD FACR 

Chief, Breast Imaging Service, Department of Radiology, Memorial Sloan-Kettering Cancer Center 

Breast parenchyma depicted on mammography has been shown to provide valuable information about 

breast cancer risk. Breast tissue consists primarily of fibroglandular tissue and fat. Fibroglandular tissue 

is responsible for the density seen on mammography. The risk of breast cancer and women with 

mammographically dense breasts has been shown in multiple studies to be higher than in women with 

predominantly mammographically fatty breasts. 

Breast MRI is a standard part of the imaging workup in high risk women and in patients with known 

breast cancer. On breast MRI examinations, the amount of fibroglandular tissue can be seen and this 

likely corresponds to breast density seen on mammography. MRI however demonstrates additional 

information about the underlying fibroglandular tissue. As intravenous contrast material is injected 

routinely for breast MRI examinations, it is been observed that native fibroglandular tissue will 

demonstrate variable enhancement patterns and levels of enhancement. The enhancement of the existing 

underlying fibroglandular tissue has been termed background parenchymal enhancement. 

As background parenchymal enhancement is related to vascular flow, it has been proposed that this may 

represent an imaging biomarker of the underlying proliferation of fibroglandular tissue. Investigations 

have shown that there is an extremely strong association between BPE and risk of breast cancer, at least a 

strong as the association between mammographic density and breast cancer. 

There has been observed that background parenchymal enhancement is also affected by treatment 

changes and hormonal manipulation. For example, tamoxifen therapy and aromatase inhibitors may 

decrease the intrinsic background parenchymal enhancement, demonstrating an imaging treatment 

response. It has been shown that background parenchymal enhancement may fluctuate during the 

menstrual cycle and that hormone replacement therapy can cause an increase in background parenchymal 

enhancement. Radiation therapy to the breast causes marked reduction in background parenchymal 

enhancement. 

As with breast density and distribution of breast parenchyma on mammography, it appears that the 

background parenchymal enhancement of breast MRI is also extremely variable and women have 

different patterns and intensity of background parenchymal enhancement. In fact, it has been observed 

that not all mammographically dense breasts demonstrate increased background parenchymal 

enhancement. Therefore, it is possible that MRI can further stratify women at high risk for developing 

breast cancer on the basis of background parenchymal enhancement. 

1. King V, Brooks JD, Bernstein JL, Reiner AS, Pike MC, Morris EA. Background parenchymal 

enhancement at breast MR imaging and breast cancer risk. Radiology 2011; 260(1):50-60. 

PMID: 21493794 

2. King V, Goldfarb S, Brooks J, Sung JS, Pike MC, Nulsen B, Jozefara J, Dickler M, Morris EA. 

Effect of aromatase inhibitors on background parenchymal enhancement and amount of 

fibroglandular tissue on Breast MRI. Radiology. 2012 Sep; 264(3):670-8. PMID: 22771878. 

3. King V, Kaplan J, Pike MC, Liberman L, Dershaw DD, Lee CH, Brooks JD, Morris EA. Impact 

of tamoxifen on amount of fibroglandular tissue, background parenchymal enhancement, and 

cysts on breast magnetic resonance imaging. Breast J. 2012 Nov; 18(6):527-34. PMID: 

23002953. 

4. King V, Gu Y, Kaplan JB, Brooks JD, Pike MC, Morris EA. Impact of menopausal status on 

background parenchymal enhancement and fibroglandular tissue on breast MRI. European 

Radiology. 2012 Dec; 22(12):2641-7. PMID: 22752463. 


13



NEXT GENERATION MEASUREMENTS OF BREAST DENSITY – TOMOSYNTHESIS 

Despina Kontos, PhD 

Department of Radiology, University of Pennsylvania 


Growing evidence suggests that breast density is an independent risk factor for breast cancer. Currently, 

the most widely used methods to estimate breast density rely on semi-automated thresholding of 

mammograms to estimate the percent of the dense tissue area versus the entire area of the breast. 

Although useful for breast cancer risk estimation, these methods have limitations. Mammography is a 

projection imaging technique that visualizes the admixture of superimposed breast tissues. 

Therefore, such thresholding methods do not allow for estimating volumetric density, but instead provide 

an area-based estimate measured from the projection image of the breast. Digital breast tomosynthesis 

(DBT) is an emerging 3D x-ray imaging modality in which tomographic breast images are reconstructed 

from multiple low-dose x-ray source projections. Measures of volumetric breast density from DBT 

images could provide more accurate measures of the dense breast tissue and ultimately result in more 

accurate measures of risk. 

This talk will provide an overview of the state-of-the art in the current research on developing volumetric 

breast density estimation techniques for DBT, including both semi-automated and fully-automated 

approaches, and new approaches to characterize the volumetric complexity of the breast tissue using DBT 

parenchymal texture analysis. DBT breast density measures will be compared to other volumetric and 

area-based multi-modality breast density estimation approaches, including Digital Mammography and 

Breast MRI, and implications will be discussed for breast cancer risk estimation. 

We envision a unique setting in which breast cancer risk assessment and patient education can be 

combined to empower women with knowledge about their personal risk and provide a fully automated 

risk assessment tool for physicians. The tomosynthesis FDA approval coupled with a rapidly evolving 

technology and a potential for superior clinical performance will soon determine the emerging role of 

DBT in clinical practice. 

Early trials suggest a significant benefit from DBT in reducing unnecessary recalls for women in the 

screening setting, including a potential for improving cancer detection. Fully-automated approaches for 

estimating volumetric breast density in DBT could provide a tool for incorporating quantitative measures 

of volumetric breast density in clinical breast cancer risk assessment. 

14



MOLECULAR MICROENVIRONMENTS OF MAMMOGRAPHICALLY DENSE BREAST 

TISSUE 

Melissa A. Troester 1,2,3† 

1 Department of Epidemiology, 2 Department of Pathology and Laboratory Medicine, 3 Lineberger 

Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA 

Previous studies of breast tissue gene expression have demonstrated that the extratumoral 

microenvironment has substantial variability across individuals, some of which can be attributed to 

epidemiologic factors. To evaluate how mammographic density (MD) and breast tissue composition 

relate to extratumoral microenvironment gene expression, we used data on 121 breast cancer patients 

from the population-based Polish Women’s Breast Cancer Study. 

Breast cancer cases were classified into two groups based on previously reported, biologically-defined 

extratumoral gene expression: the Active subtype, which is associated with high expression of genes 

related to fibrosis and wound response, and the Inactive subtype, which has high expression of cellular 

adhesion genes. MD was assessed using a quantitative, reliable computer-assisted thresholding method. 

Breast tissue composition was evaluated based on digital image analysis of tissue sections. 

The Inactive extratumoral subtype was associated with significantly higher percentage mammographic 

density (PD) and dense area (DA) in univariate analysis (PD: p=0.001; DA: p=0.049) and in multivariable 

analyses adjusted for age and body mass index (PD: p=0.004; DA: p=0.049). Inactive/higher-MD tissue 

was characterized by a significantly higher percentage of stroma and a significantly lower percentage of 

adipose tissue, with no significant change in epithelial content. 

Analysis of published gene expression signatures suggested that Inactive/higher-MD tissue expressed 

increased estrogen response and decreased TGF-β signaling. By linking novel molecular phenotypes with 

MD, our results indicate that MD reflects broad transcriptional changes, including changes in both 

epithelia- and stroma-derived signaling. 


15



COLLAGEN ALIGNMENT SURROUNDING BREAST TUMORS FACILITATES TUMOR 

SPREAD AND METASTASIS 

Suzanne M. Ponik 1 , Kristin M. Riching 1 , Kevin W. Eliceiri 1 , Paolo P. Provenzano 2 , Patricia J. Keely 1 


1 Laboratory for Cellular and Molecular Biology and UW Carbone Cancer Center, University of 

Wisconsin, Madison, 53706; 2 Department of Biomedical Engineering and UMCCC, University of 

Minnesota, Minneapolis, 55455 

While mammographic density has been correlated to increased risk of breast cancer in several studies, 

relatively little is known regarding the mechanisms by which mammographic density contributes to risk. 

Using mouse models of increased collagen in the connective tissue stroma, we find a causal link between 

increased stromal collagen and increased formation of mammary tumors. Tumors that arise in dense 

collagen are more invasive and demonstrate dramatic changes in gene expression compared to tumors that 

arise in non-dense mammary tissue. At the structural level, we find that mammary tissue has non-aligned, 

curly collagen fibers that are altered during tumor progression. 

These changes, termed Tumor Associated Collagen Signatures (TACS) manifest as a consistent 

progression from increased unaligned collagen (TACS-1), to straight collagen fibers (TACS-2) to straight 

collagen fibers aligned perpendicular to the tumor/stromal boundary (TACS-3). Mechanistically, TACS-3 

collagen provides a “highway” on which tumor cells are able to migrate out from the tumor and invade 

into distal sites. TACS-3 signatures correlate to increased metastases in mouse models. In mouse models, 

similar rearrangements of collagen are also observed at the sites of lung metastasis, suggesting the 

collagen rearrangements facilitate not only spread, but also survival in metastatic sites. Importantly, 

collagen structural changes are also found in human breast cancer: analysis of 203 archived samples from 

patients with invasive breast cancer demonstrates that the presence of TACS-3 collagen significantly 

predicts increased relapse. 

Thus, TACS-3 collagen represents a potential biomarker. Current work is investigating ways that 

collagen changes can be targeted to better treat breast cancer in women with dense breast tissue. 

16



GENETIC MARKERS OF BREAST DENSITY-ETHNIC ASSOCIATION TO BREAST DENSITY 

Elad Ziv, MD 

Department of Medicine, University of California San Francisco 

Introduction: Percent mammographic density (PMD) adjusted for age and BMI is one of the strongest 

risk factors for breast cancer and is known to be approximately 60 percent heritable. Here we report a 

finding of an association between genetic ancestry and adjusted PMD. 

Methods: We selected self-identified Caucasian women in the California Pacific Medical Center 

Research Institute Cohort whose screening mammograms placed them in the top or bottom quintiles of 

age- and body mass index-adjusted PMD. Our final data set included 474 women with the highest 

adjusted PMD and 469 with the lowest genotyped on the Illumina 1M platform. Principal component 

analysis (PCA) and identity-by-descent (IBD) analyses allowed us to infer the women's genetic 

ancestry and correlate it with adjusted PMD. 

Results: Women of Ashkenazi Jewish ancestry, as defined by the first principal component (PC1) of 

PCA and identity-by-descent analyses, represented approximately 15 percent of the sample. Ashkenazi 

Jewish ancestry, defined by PC1, was associated with higher adjusted PMD (p = 0.004). Using 

multivariate regression to adjust for epidemiologic factors associated with PMD, including age at parity 

and use of postmenopausal hormone therapy, did not attenuate the association. 

Conclusion: Women of Ashkenazi Jewish ancestry based on genetic analysis are more likely to have 

high age- and BMI-adjusted PMD. Ashkenazi Jews may have a unique set of genetic variants or 

environmental risk factors that increase mammographic density. 


17



THE RELATIVE CONTRIBUTIONS OF BI-RADS DENSITY AND GENETICS TO BREAST 

CANCER RISK 

Celine M. Vachon 1 , Peter Fasching 2 , Fergus Couch 1,3 , Christopher Scott 1 , Matthew Jensen 1 , Dana 

Whaley 4 , Kathleen R. Brandt 4 , Elad Ziv 5 , Karla Kerlikowske 5 , V. Shane Pankratz 1 


1 Department of Health Sciences Research, Division of Epidemiology, Mayo Clinic College of Medicine, 

Rochester, MN; 2 Department of Gynecology and Obstetrics, University Hospital Erlangen Friedrich- 

Alexander-University Erlangen-Nuremberg Erlangen, Germany; 3 Division of Experimental Pathology, 

Department of Laboratory Medicine and Pathology , Mayo Clinic College of Medicine, Rochester, MN; 

4 Divison of Breast Imaging, Department of Radiology, Mayo Clinic College of Medicine, Rochester, MN; 

5 Departments of Epidemiology and Biostatistics and General Internal Medicine Section, Department of 

Veterans Affairs, University of California, San Francisco, CA 

Background: Breast density is one of the strongest risk factors for breast cancer (BC), with a high 

population attributable risk. Recent genetic studies have identified 77 common susceptibility loci for BC 

which together explain 14% of familial risk. Only one study to date has examined the contribution of a 

subset of these variants (18 SNPs) to risk models that include breast density. We examine whether the 77 

genetic variants provide additional discrimination of BC risk relative to age- and BMI- adjusted BI- 

RADS density. Further, we extend the BCSC risk model that incorporates Breast Imaging Reporting and 

Data System (BI-RADS) density and clinical factors to examine the additional contribution of the 77 

SNPs to BC risk. 

Methods: The sample consisted of 1643 cases and 2497 controls from three clinic-based studies with 

clinical risk factors, including BI-RADS density, and genotype information on the 77 loci. We formulated 

a polygenic SNP score (77-SNP) from the reported per-SNP odds ratios, and used quartiles of this score in 

analyses. We evaluated whether BI-RADS density and the 77-SNP score were independent risk factors for 

BC, adjusted for age, BMI and study, and assessed whether the BIRADS-BC association differed by SNP 

quartiles. AUC values were compared between models with and without the 77-SNP score, using 

DeLong’s method, to evaluate whether inclusion of the 77-SNP score significantly improved the 

discriminatory ability of the model. We extended the BCSC risk model by incorporating into it the odds 

ratios (OR) for the 77-SNP groups, and estimated 5-year risks for the subjects in one of the three studies 

with 456 cases, 1166 controls. Five-year risks from the BCSC risk model were compared to those from 

the BCSC+77-SNP model by computing net reclassification improvement (NRI) for cases and controls 

using published 5-year risk groupings (4.0). 

Results: BI-RADS density, adjusted for age, BMI and study, was a significant risk factor for BC (OR 

(95% CI): 0.6 (0.4-0.7), 1.3 (1.1-1.5) and 1.8 (1.4-2.3) for BI-RADS 1, 3 and 4 vs. 2; AUC=0.66). The 

77-SNP score (adjusted for age, BMI and study) was similarly associated with BC (OR (95% CI): 0.6 

(0.5-0.7); 1.5 (1.3-1.8); 1.7 (1.4-2.0) for Quartiles 1, 3 and 4 vs. 2; AUC=0.675). BI-RADS density and 

77-SNP score were independent risk factors for BC, and together showed greater discrimination of risk 

compared to the model with BI-RADS alone (adjusted for age and BMI) (AUC=0.69; UC=0.028, 

p



BREAST CANCER RISK PREDICTION AND INDIVIDUALIZED SCREENING BASED ON 

COMMON GENETIC VARIATIONS AND BREAST DENSITY MEASUREMENTS 

Hatef Darabi 

Karolinska Institutet, Stockholm, Sweden 

Over the last decade several breast cancer risk alleles have been identified which has led to an increased 

interest in individualised risk prediction for clinical purposes. In the present work we examine several 

models for predicting absolute risk, in particular we examine the performance of 18 breast cancer risk 

single-nucleotide polymorphisms (SNPs), together with mammographic percentage density (PD), body 

mass index (BMI) and clinical risk factors in predicting absolute risk of breast cancer. 

Adding mammographic PD, BMI and all 18 SNPs to a baseline Swedish Gail model improved the 

discriminatory accuracy (the AUC statistic) from 55% to 62%. The net reclassification improvement 

(NRI) was used to assess improvement in classification of women into low, intermediate, and high 

categories of 5-year risk, where significant positive reclassification was observed (NRI= 0.170). 

Researchers participating in the Collaborative Oncological Gene-environment Study (COGS) have now 

identified more than 40 new SNPs associated with breast cancer. In addition to the discovery of new 

susceptibility loci, the findings reported can be applied to derive estimates for disease risk based on an 

individual’s genetic profile, and it is expected to improve the ability to predict which women are at 

greater risk of developing breast cancer. 


19



BENIGN BREAST DISEASE, MAMMOGRAPHIC BREAST DENSITY AND THE RISK OF 

BREAST CANCER 

Jeffrey A. Tice, MD; Ellen S. O’Meara, PhD; Donald L. Weaver, MD; Celine Vachon, PhD; Rachel 

Ballard-Barbash, MD, MPH; Karla Kerlikowske, MD 


Division of General Internal Medicine, Department of Medicine; General Internal Medicine Section, 

Department of Veteran Affairs and Departments of Medicine and Epidemiology and Biostatistics (Drs. 

Kerlikowske); University of California, San Francisco, San Francisco, CA. Group Health Research 

Institute (Dr. O’Meara); Department of Pathology, University of Vermont College of Medicine and 

Vermont Cancer Center (Dr. Weaver). Applied Research Program, Division of Cancer Control and 

Population Sciences, National Cancer Institute (Dr. Ballard-Barbash). 

Background: Benign breast disease and high breast density are prevalent, strong risk factors for breast 

cancer. Women with both risk factors may be at very high risk. 

Methods: We included 42,818 women participating in the Breast Cancer Surveillance Consortium 

(BCSC) from 1994 through 2009 who had no prior diagnosis of breast cancer and had undergone at least 

one benign breast biopsy and mammogram; 1,359 women developed incident breast cancer in 6.1 years of 

follow-up (78% invasive, 22% DCIS). Community radiologists rated breast density using Breast Imaging 

Reporting and Data System categories: almost entirely fat (low density); scattered fibroglandular densities 

(average density); heterogeneously dense (high density); extremely dense (very high density). 

Results: In Cox regression models, benign breast disease and breast density were independently 

associated with breast cancer. Atypical hyperplasia and very high density was uncommon (0.6% of 

biopsies), but associated with the highest risk [hazard ratio (HR); 5.3, 95% confidence interval (CI) 3.5- 

8.1] compared to non-proliferative changes and average density. Proliferative disease without atypia (26% 

of biopsies) was associated with elevated risk that varied little across levels of density: average (HR 1.4, 

95% CI 1.1-1.7), high (HR 2.0, 95% CI 1.7-2.4), or very high (HR 2.0, 95% CI 1.5-2.7). Low breast 

density (4.5% of biopsies) was associated with low risk (HRs


DETERMINANTS OF BREAST TISSUE COMPOSITION IN 15-18 YEAR OLD GIRLS 


Norman F. Boyd*, Ella Huszti, Greg Stanisz, Sofia Chavez, Qing Li, Linda Linton, Monica Taylor, Sara 

Hennessy, Lisa J. Martin, Salomon Minkin 

Campbell Institute for Breast Cancer Research Ontario Cancer Institute 

Background. Breast tissue is especially susceptible to carcinogenic influences in early life and several 

variables in early life, including birth weight, growth in adolescence and attained height, influence risk of 

breast cancer in later life. We examine here the association of these risk factors in early life with 

variations in breast tissue composition in 15-18 year old girls. 

Methods. We recruited 437 girls aged 15-18, and used magnetic resonance scans to measure the water 

and fat content of the breast. All scans were obtained in the follicular phase of the menstrual cycle. We 

also made anthropometric measurements. We obtained from mothers by questionnaire a history of their 

daughter’s birth weight, breast-feeding and growth during childhood, and of maternal health during 

pregnancy with the daughter. 

Results. At ages 15-18 years, percent water in the breast was positively associated with: greater birth 

weight (adjusted for age and duration of breast feeding; p=0.06); longer duration of breast-feeding 

(adjusted for age and birth weight; p=0.02); and greater increase in height between ages 9 and 12 years 

(adjusted for age, weight and relative height at age 9; p=0.0007). Growth between ages 12 and 15 

(adjusted for age, weight and relative height at age 12; p=0.6) was not associated with percent breast 

water at ages 15-18. 

Greater birth weight (p



THE EFFECT OF WEIGHT CHANGE ON CHANGES IN BREAST MEASURES OVER 

MENOPAUSE 

J.O.P. Wanders 1 , M.F. Bakker 1 , P.H.M. Peeters 1,2 , C.H. van Gils 1 


1 Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, POBox 85500, 

3508 GA Utrecht, The Netherlands; 2 Department of Epidemiology, Public Health and Primary Care, 

Faculty of Medicine, Imperial College, Norfolk Place, London W2 1PG, UK 

Introduction: Higher BMI and weight have been related to lower percent density in cross-sectional 

studies. However, little is known on how weight change over time is longitudinally related to changes in 

mammographic density. Here we investigate, in a population going through menopause, how dense area, 

non-dense area and percent density change and to what extent this is influenced by weight change over 

menopause. 

Methods: 652 women, aged 49 to 61 years at recruitment, who participated in the Prospect-EPIC cohort 

and went through menopause on average within 5 years after recruitment, were included in this study to 

examine the effect of weight change on mammographic measures over menopause. All women had a preand 

post-menopausal mammogram and information about their weight at the time of these mammograms 

was available. A quantitative computer-assisted method (Cumulus) was used for breast measure 

assessment. Women were divided into 3 groups according to their percentage weight change over 

menopause: 1) weight loss (more than 1.5% weight loss), 2) stable weight (not more than 1.5% weight 

loss or weight gain) and 3) weight gain (more than 1.5% weight gain). 

Results: Percentage weight change ranged from -20.3% to +125.2% . 190 (29%) women lost more than 

1.5% of their body weight at recruitment, 138 (21%) women remained weight stable and 324 (50%) 

women gained more than 1.5% of their body weight at recruitment. The mean absolute change in nondense 

area in the weight loss, stable weight and weight gain group was -3.7 cm 2 (95% CI: -7.8 ; 0.5), -4.6 

cm 2 (95% CI: -8.9 ; -0.3) and 4.5 cm 2 (95% CI: 1.8 ; 7.2), respectively (p-value for a linear trend, p-trend 

< 0.001). The mean absolute change in dense area was -16.2 cm 2 (95% CI: -19.6 ; -12.8), -15.0 cm 2 (95% 

CI: -18.0 ; -12.0) and -16.3 cm 2 (95% CI: -18.1 ; -14.4), respectively (p-trend=0.87). Finally, the mean 

absolute change in percentage breast density was -5.7% (95% CI: -7.7 ; -3.7), -5.0% (95% CI: -7.2 ; - 

2.8) and -9.3% (95% CI: -10.7 ; -7.8), respectively (p-trend=0.001). 

Conclusion: Women who go through menopause on average show a decrease in both dense area and 

percent density. The decrease in dense area is independent of weight change. The decrease in percent 

density is larger in women who gain weight than in women with stable weight or weight loss. This is due 

to an increase in the non-dense (fatty) tissue in this group. The role of the fatty breast tissue in breast 

cancer risk is subject of further investigation. 

22



AWARENESS OF BREAST DENSITY AND ITS RELATIONSHIP TO MAMMOGRAPHY 

SENSITIVITY AND BREAST CANCER RISK AMONG U.S. WOMEN: RESULTS OF A 

NATIONAL SURVEY 

Rhodes DJ, Radecki-Breitkopf C, Ziegenfuss J*, Jenkins SM, Vachon CM. 

Mayo Clinic, Rochester MN (*HealthPartners, Minneapolis MN) 

Background: Recent legislation mandating breast density (BD) notification assumes a knowledge deficit 

among screening-aged women, but little is known about women’s awareness of BD or its impact on 

mammography sensitivity and breast cancer risk. 

Methods: Using a probability-based web panel (KnowledgePanel) representative of the United States 

population, we surveyed women aged 40 -74 years regarding their awareness of BD and knowledge of the 

impact of BD on mammography sensitivity and breast cancer risk. 

Results: Of 2,311 women surveyed, 1,506 completed questionnaires. Overall, 58% of women had heard 

of BD. Factors significantly correlated with a higher likelihood of having heard of BD include: age older 

than 50 years (p < 0.001); White race/ethnicity (p < 0.0001); higher income and educational attainment (p 

< 0.0001); self-described health status as “very good” or “excellent”(p < 0.01); use of hormone therapy (p 

< 0.01); prior mammography (p



BREASTCARE: A PRIMARY CARE CLINIC-BASED RCT TO INCREASE BREAST CANCER 

KNOWLEDGE AND DISCUSSION OF RISK AND LIFESTYLE BEHAVIORS 

Celia P. Kaplan, DrPH, MA, Jennifer Livaudais-Toman, PhD, Steven Gregorich, PhD, Jeffrey Tice, MD, 

Karla Kerlikowske, MD, Rena Pasick, PhD, Alice Chen, MD, Eliseo J. Perez-Stable, MD, Leah S. 

Karliner, MD 


Background. Despite the availability of breast cancer risk assessment tools and interventions for risk 

reduction, these tools are not well integrated into clinical practice. As a result, many women do not 

engage in a discussion of their breast cancer risk with their physician, which may lead to underuse of 

effective risk reduction interventions. 

Methods. We conducted a randomized controlled trial that compared usual care to a tablet-personal 

computer (PC) based breast cancer risk assessment and education intervention (BreastCARE) delivered in 

a primary care setting. We enrolled women aged 40-74 years with no personal breast cancer history prior 

to their scheduled primary care visits at two clinics (one academic medical center, one safety-net) 

between June 2011 and August 2012, and randomized them to Intervention or Usual Care (UC) arms, 

stratified by race/ethnicity. The Intervention group completed the BreastCARE intervention at the clinic 

just prior to their clinic visit and women and physicians received a tailored risk report. The UC group 

completed a telephone-based risk assessment after their clinic visit. We categorized women as high or 

average risk based on family history using the Referral Screening Tool (RST) or breast cancer risk factors 

using the Gail/Breast Cancer Surveillance Consortium (BCSC) risk models. We contacted all women for 

a follow-up telephone survey one week after risk assessment. We used generalized estimating equations to 

account for clustering by physician, and to estimate differences at follow-up between Intervention and UC 

groups in above average knowledge of breast cancer risk factors (cut-point based on mean knowledge 

score from sample), and discussion of breast cancer risk and lifestyle behaviors (exercise and weight) 

with physician. 

Results. A total of 1,278 women completed risk assessments and signed consent forms (596 Intervention 

and 665 UC) and 1,235 (97%) completed follow-up interviews (580 Intervention and 655 UC). The mean 

sample age was 56 years (SD=9) with 35% non-Latina White, 23% Latina, 22% African American, 18% 

Asian/Pacific Islander and 2% Native American/other. Demographic characteristics and breast cancer risk 

distributions were well-balanced between Intervention and UC groups. Nine percent of women qualified 

for high-risk referral based on RST score and 16% qualified based on Gail/BCSC models. Compared to 

women in the UC group, those in the Intervention group reported greater knowledge of breast cancer risk 

factors (69% vs. 55%), and more discussion of breast cancer risk (41% vs. 14%), exercise (75% vs. 61%) 

and weight (67% vs. 57%) with physician. Discussion of risk was greatest among women at the highest 

risk for breast cancer who received the intervention (51% Intervention vs. 18% UC). Multivariable 

analysis results are presented in Table 1. 

Table 1. Multivariable analysis at follow-up* 

Above average 

knowledge of breast 

cancer risk factors 

24 

Discussed breast 

cancer risk with 

physician 

Discussed regular 

exercise with 

physician 

Discussed weight 

with physician 

OR (95% CI) OR (95% CI) OR (95% CI) OR (95% CI) 

(ref=UC) Intervention group 1.78 (1.37-2.31)° 3.94 (2.90-5.37)° 1.95 (1.51-2.51)° 1.60 (1.26-2.02)° 

*Analyses account for clustering of observations by physician and are adjusted for clinic site and objective breast cancer risk 

°p



IMPACT OF THE TRANSITION TO DIGITAL MAMMOGRAPHY ON THE BENEFITS, 

HARMS AND COSTS OF SCREENING FOR BREAST CANCER IN THE US 

Natasha K. Stout, PhD 1 ; Sandra J. Lee, DSc 2 ; Clyde B. Schechter, MD MA 3 ; Oguzhan Alagoz, PhD 4,5 ; 

Donald Berry, PhD 6 ; Diana S.M. Buist, PhD 7 ; Mucahit Cevik 4 ; Gary Chisholm 6 ; Harry J. de Koning, MD 

PhD 8 ; Hui Huang, MS 2 ; Rebecca Hubbard PhD 7 ; Karla Kerlikowske, MD 9 ; Mark Munsell, MS 6 ; Anna N. 

A. Tosteson, ScD 10 ; Amy Trentham-Dietz, PhD 5 ; Nicolien T. van Ravesteyn, MSc 8 ; Diana L. Miglioretti, 

PhD 11 ; Jeanne S. Mandelblatt, MD MPH 12 

1 Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care 

Institute, Boston, MA; 2 Dana-Farber Cancer Institute, Boston, MA; 3 Departments of Family & Social 

Medicine and Epidemiology & Population Health, Albert Einstein College of Medicine, Bronx, New York; 

4 Department of Industrial and Systems Engineering, University of Wisconsin, Madison, WI; 5 Department 

of Population Health Sciences and Carbone Cancer Center, University of Wisconsin, Madison, WI; 6 The 

University of Texas M.D. Anderson Cancer Center, Houston, TX; 7 Group Health Research Institute, 

Seattle, WA; 8 Department of Public Health, Erasmus MC, Rotterdam, The Netherlands; 9 Departments of 

Epidemiology and Biostatistics, and General Internal Medicine Section, Department of Veterans Affairs, 

University of California, San Francisco, CA; 10 The Dartmouth Institute for Health Policy and Clinical 

Practice, Geisel School of Medicine at Dartmouth, Lebanon, NH; 11 Department of Public Health 

Sciences, School of Medicine, University of California, Davis; 12 Department of Oncology, Georgetown 

University Medical Center and Cancer Prevention and Control Program, Lombardi Comprehensive 

Cancer Center 

Fueled in part by the 2005 Digital Mammographic Imaging Trial results showing improved sensitivity for 

selected subgroups of women, digital mammography has quickly replaced plain film mammography for 

routine breast cancer screening. Use in community settings supports the modest improvements in 

sensitivity for younger women and women with radiographically dense breasts seen in the trial, however, 

specificity is reduced. Further, digital has higher cost compared with plain film mammography. The 

impact of the switch in technology from film to digital mammography on women’s health and economic 

costs is unclear. 

Therefore, we will 1) assess the health effects and monetary costs of screening mammography using 

digital mammography instead of film according the 2009 US Preventive Services Task Force breast 

cancer screening guidelines, and 2) examine alternative screening schedules using digital mammography 

that extend screening to younger women or women with dense breast tissue, two subgroups where digital 

has been shown to perform better than film mammography. This analysis represents a collaboration 

between researchers from the National Cancer Institute’s Cancer Intervention and Surveillance Modeling 

Network (CISNET) and Breast Cancer Surveillance Consortium (BCSC). We will use a comparative 

modeling approach with five independent CISNET computer models of U.S. breast cancer epidemiology. 

The models account for differential risk breast cancer risk and mammography performance by breast 

density using inputs based on data from the BCSC. 

Model outcomes will include life years, quality-adjusted life years, costs, breast cancer mortality and false 

positives per screening scenario. We will compute incremental cost-effectiveness ratios for the digital 

screening strategies both within each model and across the five models to understand the value of 

alternative screening scenarios. The comparative modeling approach using five models will allow us to 

better account for uncertainty in breast cancer natural history on analysis conclusions about the benefits 

and harms of screening. 


25



POSTER 

ABSTRACTS 

26



Poster 

# 

P1 

P2 

P3 

P4 

P5 

P6 

P7 

P8 

P9 

P10 

P11 

P12 

P13 

P14 

P15 

P16 

P17 

P18 

P19 

P20 

P21 

P22 

Poster Title 

EXPLORING A RELATIONSHIP BETWEEN EXTRACELLULAR MATRIX 

STIFFNESS AND MAMMOGRAPHIC DENSITY IN THE HUMAN BREAST 

COMPARISON OF BREAST DENSITY MEASUREMENTS WITH A 

MAMMOGRAPHIC VOLUMETRIC AND AREA ALGORITHM AND 

MAGNETIC RESONANCE IMAGING 

COMPARISON OF CUMULUS AREA DENSITY WITH TWO AUTOMATED 

VOLUMETRIC BREAST DENSITY ALGORITHMS 

COMPARISON OF THREE METHODS FOR DENSITY ASSESSMENT AND 

THEIR ASSOCIATION TO BREAST CANCER RISK 

MEASUREMENT OF COMPRESSED BREAST THICKNESS FOR BREAST 

DENSITY ASSESSMENT USING A GAMES CONSOLE INPUT DEVICE 

ASSOCIATIONS OF MAMMOGRAPHIC DENSE AND NON-DENSE AREA 

AND BODY MASS INDEX WITH RISK OF BREAST CANCER 

PREDICTION OF MAMMOGRAPHIC DENSITY USING REPRODUCTIVE 

AND LIFESTYLE CHARACTERISTICS 

MAMMOGRAPHIC DESNITY AND RISK OF BREAST CANCER BY AGE 

AND TUMOR CHARACTERISTICS 

MAMMOGRAPHIC PIXEL VARIANCE AND RISK OF BREAST CANCER IN 

A NESTED CASE-CONTROL STUDY 

VALIDATION OF A FULLY AUTOMATED, HIGH THROUGHPUT 

MAMMOGRAPHIC DENSITY MEASUREMENT TOOL FOR USE WITH 

PROCESSED DIGITAL MAMMOGRAMS 

RESULTS OF SUPPLEMENTAL ULTRASOUND SCREENING IN WOMEN 

WITH AND WITHOUT DENSE BREAST TISSUE 

COMPARISON OF CUMULUS MAMMOGRAPHIC DENSITY READINGS 

BETWEEN FUJIFILM COMPUTED RADIOGRAPHY DIGITAL 

MAMMOGRAPHIC IMAGES IN RAW AND DISPLAY FORMATS 

RISK ASSOCIATION OF SUBREGIONAL MEASUREMENTS OF BREAST 

DENSITY 

THE ASSOCIATION BETWEEN VITAMIN D INTAKE, SEASON AND 

MAMMOGRAPHIC DENSITY IN NORWEGIAN POSTMENOPAUSAL 

WOMEN 

THE ASSOCIATION BETWEEN GEOGRAPHIC STATUS AND 

MAMMOGRAPHIC DENSITY 

INTER-SCAN REPRODUCIBILITY OF MAMMOGRAPHIC DENSITY 

MEASUREMENTS 

MULTIPLE METABOLIC RISK FACTORS AND MAMMOGRAPHIC BREAST 

DENSITY IN THE NEW YORK CITY MULTIETHNIC BREAST CANCER 

PROJECT 

GENETIC TESTING FOR BREAST CANCER RISK ESTIMATION – A COST - 

EFFECTIVENESS ANALYSIS 

ADIPOSITY AND MAMMOGRAPHIC BREAST DENSITY IN 

PREMENOPAUSAL CHILEAN WOMEN 

RELATIONSHIP OF SERUM ESTROGEN LEVELS WITH AREA AND VOLUME 

MEASURES OF MAMMOGRAPHIC DENSITY AMONG WOMEN 

UNDERGOING BREAST BIOPSY FOR CLINICAL INDICATIONS 

MAMMOGRAPHIC DENSITY PHENOTYPES AND RISK OF BREAST CANCER: 

A META-ANALYSIS 

MAMMOGRAPHIC DENSITY AND EARLY MAMMOGRAPHIC SCREENING 

PERFORMANCE MEASURES IN THE NORWEGIAN BREAST CANCER 

SCREENING PROGRAM 

Author 

Acerbi 

Alonzo 

Alonzo 

Assi 

Astley 

Baglietto (Hopper) 

Bakker 

Bertrand 

Brentnall 

Couwenberg 

Dean 

Dickens 

Duewer 

Ellingjord-Dale 

Emaus 

Emaus 

Engmann 

Folse 

Garmendia 

Gierach 

Graff 

Hofvind 

27



P23 

P24 

P25 

P26 

P27 

P28 

P29 

P30 

P31 

P32 

P33 

CHILDHOOD AND ADOLESCENT GROWTH PREDICTS MAMMOGRAPHIC 

DENSITY MEASURES ASSOCIATED WITH BREAST CANCER RISK IN MID- 

LIFE: A PROSPECTIVE STUDY 

BACKGROUND PARENCHYMAL UPTAKE OF Tc-99m SESTAMIBI ON 

MOLECULAR BREAST IMAGING IN WOMEN WITH DENSE BREASTS 

IMPACT OF CALIFORNIA BREAST DENSITY NOTIFICATION LAW SB 1538 

ON CALIFORNIA WOMEN AND THEIR HEALTH CARE PROVIDERS 

CHALLENGES IN DE-IDENTIFYING FILM-SCREEN MAMMOGRAMS PRIOR 

TO BREAST DENSITY ASSESSMENT FOR THE NOVICE PRACTITIONER 

ASSOCIATION BETWEEN AUTOMATED, VOLUMETRIC BREAST DENSITY 

MEASURES AND BREAST CANCER IN A LARGE SCREENING POPULATION 

LONGITUDINAL TRACKING WITH TIME OF MAMMOGRAPHIC DENSITY 

MEASURES THAT PREDICT BREAST CANCER 

MULTI-VENDOR VALIDATION OF A FULL-AUTOMATED BREAST DENSITY 

ESTIMATION METHOD APPLICATION TO BOTH RAW AND PROCESSED 

DIGITAL MAMMOGRAMS 

GENETIC DETERMINANTS OF FIBROGLANDULAR BREAST TISSUE: 

IMPLICATIONS ON USING MAMMOGRAPHIC DENSITY MEASURES IN 

BREAST CANCER RISK ASSESSMENT 

FINE-MAPPING OF THREE MAMMOGRAPHIC DENSITY LOCI USING NEXT- 

GENERATION SEQUENCING 

APPLICATION OF SHAPE AND APPEARANCE MODEL APPROACH TO 

MAMMOGRAMS 

METHODS TO ASSESS AND REPRESENT MAMMOGRAPHIC DENSITY: AN 

ANALYSIS OF FOUR CASE-CONTROL STUDIES 

28 

Hopper 

Hruska 

Ikeda 

(Hargreaves/Fellow) 

Jobling (Forbes) 

Kallenberg 

Kavitha (Hopper) 

Kontos 

Kontos 

Lindstrom 

Malkov 

Maskarinec 

P34 INTERNATIONAL POOLING PROJECT OF MAMMOGRAPHIC DENSITY McCormack 

AN APPROACH TO THE STANDARDIZATION OF PERCENT 

P35 MAMMOGRAPHIC DENSITY TO ENABLE JOINT ANALYSIS OF DATA 

ACROSS MULTIPLE CENTERS 

Pankratz 

P36 

BREAST DEVELOPMENT AT DIFFERENT BREAST TANNER STAGES: 

EVIDENCE FROM THE GROWTH OBESITY CHILEAN COHORT STUDY 

Pereira 

P37 

DUAL X-RAY ABSORPTIOMETRY AN ALTERNATIVE METHOD TO MEASURE 

VOLUMETRIC BREAST DENSITY 

Pereira 

P38 

AUTOMATED DENSITY RISK SCORING WITH LEARNED HIERARCHICAL 

FEATURES 

Petersen 

P39 

ASSURE - ADAPTING BREAST CANCER SCREENING STRATEGY USING 

PERSONALISED RISK ESTIMATION 

Platel 

P40 BREAST DENSITY INFORM: NEW YORK STATE AND FEDERAL INITIATIVES Pushkin 

CIRCULATING INSULIN-LIKE GROWTH FACTOR-1, INSULIN-LIKE GROWTH 

P41 

FACTOR BINDING PROTEIN-3, GENETIC POLYMORPHISMS, AND 

Rice 

MAMMOGRAPHIC DENSITY IN PREMENOPAUSAL MEXICAN WOMEN: 

RESULTS FROM THE ESMAESTRAS COHORT 

P42 

P43 

P44 

P45 

P46 

P47 

ADJUSTING FOR INTER-OBSERVER VARIATION IN VISUAL ASSESSMENT 

OF PERCENTAGE BREAST DENSITY ON A CONTINUOUS SCALE AND 

DETERMINING WOMEN AT HIGH RISK OF DEVELOPING BREAST CANCER 

RISK 

VISUAL ANALOGUE SCALE ASSESSMENT OF PERCENTAGE BREAST 

DENSITY: A STUDY OF AGREEMENT BETWEEN OBSERVERS AND WITH 

INTERACTIVE THRESHOLDING 

LARGE INTERNATIONAL STUDY REVEALS NOVEL ASSOCIATIONS 

BETWEEN COMMON BREAST CANCER SUSCEPTIBILITY VARIANTS AND 

RISK-PREDICTING MAMMOGRAPHIC DENSITY MEASURES 

META-ANALYSIS OF TWELVE GENOME-WIDE ASSOCIATION STUDIES 

(GWAS) IDENTIFIES NOVEL GENETIC LOCI ASSOCIATED WITH 

MAMMOGRAPHIC DENSITY PHENOTYPES 

NON-INVASIVE OPTICAL ASSESSMENT OF BREAST DENSITY AND 

IDENTIFICATION OF HIGH-RISK SUBJECTS 

RETROSPECTIVE ANALYSIS OF BREAST DENSITY IN TRIPLE NEGATIVE 

BREAST CANCERS 

Sergeant (Astley) 

Sergeant (Astley) 

Stone 

Tamimi 

Taroni 

Vijayaraghavan



P48 

A FULLY-AUTOMATED METHOD FOR QUANTIFYING FIBROGLANDULAR 

TISSUE AND BACKGROUND PARENCHYMAL ENHANCEMENT IN BREAST 

MRI 

Wu 

29

P1 



EXPLORING A RELATIONSHIP BETWEEN EXTRACELLULAR MATRIX STIFFNESS 

AND MAMMOGRAPHIC DENSITY IN THE HUMAN BREAST 

Irene Acerbi 1,2 , Alfred Au 2 , Ori Maller 1,2 , Quanming Shi 2,3 , Jan Liphardt 2,3 , Yunn-yi Chen 4 , Shelley 

Hwang 5 , Valerie M. Weaver 1,2,6,7,8 

1 Department of Surgery and Center for Bioengineering and Tissue Regeneration, University of California, 

San Francisco, CA, USA; 2 Bay Area Physical Sciences in Oncology Center, Berkeley, CA, USA 

3 Department of Physics, University of California, Berkeley, CA, USA; 4 UCSF Carol Buck Breast Cancer 

Center, San Francisco, CA, USA; 5 Department of Pathology, University of California, San Francisco, CA, 

USA; 6 Department of Surgery, Duke University, Durham, NC, USA; 7 Institute for Regeneration Medicine, 

University of California San Francisco, CA, USA; 8 Department of Bioengineering and Therapeutic 

Sciences, University of California, San Francisco, CA, USA; 9 Department of Anatomy, University of 

California, San Francisco, CA, USA 

Mammographic density (MD) is associated with high collagen content in the extracellular matrix 

(ECM) and greater risk to malignancy. Data from our group and others have highlighted the importance 

of mechanical cues from the ECM in breast tissue homeostasis and tumor progression [Paszek et al., 

Cancer Cell 2005; Levental et al., Cell, 2009; reviewed in Yu et al., Trends Cell Biol, 2010]. 

Nevertheless, a role for ECM stiffness in tumor cell initiation is unknown. In this study, we hypothesize 

that high MD correlates with stiffer breast ECM and enhances women’s risk for breast cancer compared 

to low MD. 

To test this hypothesis, we studied breast tissues obtained through prophylactic mastectomy from 

women with high MD (BIRADS 4) versus low (BIRADS 1). From each surgically excised breast, 

samples of 0.5cm x 0.5cm x 1cm dimension were removed from the retroareolar region and from 4 

peripheral quadrants. Sample sections were subjected to biophysical, morphological and biochemical 

analysis. Biophysical analysis included the application of Atomic Force Microscopy (AFM) to obtain an 

extensive force map of distinct anatomical regions of the ECM associated with the intra-lobular and interlobular 

ECM. ECM architecture has been analyzed using two photons and SIM-POL imaging coupled 

with picrosirius staining, polarized light imaging and image quantification. Biochemical and 

morphological analysis consisted of immunohistochemistry for markers that detect mechano-signaling in 

the epithelium and stromal fibroblasts, and H&E to visualize cellular and ECM organization. 

We studied samples from the different 5 quadrants of a same breast. We found that the ECM 

associated with the upper outer region, was in tendency stiffer (AFM) and had higher tension (SIMPOL 

birefringence) than the other peripheral quadrants. We show heterogeneity within multiple regions of the 

same breast, with the upper outer region, where most tumors occur, contained linearized collagen fibrils. 

Most invasive breast cancers arise from the ductal epithelium. Therefore, we studied the ECM 

associated with the Terminal Ductal Lobular Units (TDLU): inter-lobular ECM (among the TLDU), and 

intra-lobular ECM (within the TDLU). The inter-lobular ECM of the breast contained linearized collagen 

fibrils and was relatively stiffer. In contrast, we found that the intra-lobular ECM associated with the 

TDLU in the breast contained less linearized collagen fibrils and was compliant. 

In a small cohort, breast tissues from of the upper outer region were examine to directly explore 

the relationship between extracellular matrix stiffness and high MD across a patient population. Our 

preliminary data suggest an increase in collagen abundance and higher content of linearized collagen 

fibers adjacent to TLDUs were associated with high MD. Our future efforts will focus on increasing the 

size of the cohort to further establish a correlative relationship between MD and ECM stiffness. We will 

also examine the significance of this relationship in tumor cell initiation. 

ABSTRACTS 

Acknowledgements: supported by W81XWH-05-1-0330 and R01 CA138818-01A1 to VMW, 1U01 ES019458-01 

to VMW and ZW, and P50 CA 58207 to JG, VW, SH and LC, U54CA143836-01 to JL and VW, and Susan G. 

Komen for the Cure PSF12230246 to IA. 

30



P2 COMPARISON OF BREAST DENSITY MEASUREMENTS WITH A 

MAMMOGRAPHIC VOLUMETRIC AND AREA ALGORITHM AND MAGNETIC 

RESONANCE IMAGING 

O Alonzo-Proulx 1 , J Mainprize 1 , E. Warner 1 , J. Harvey 2 , M. Yaffe 1 

1 Sunnybrook Health Sciences Centre, 2075 Bayview Ave., Toronto, Ontario Canada, M4N 3M5 ; 

2 University of Virginia Health System, Charlottesville, Virginia 

Introduction: Area and volumetric breast density (ABD and VBD) is typically determined from twodimensional 

x-ray projection mammograms. In this study we compare the values of ABD, VBD, dense 

volume and total breast volume estimated in this way, with measurements obtained directly from a threedimensional 

breast MRI image dataset. 

ABSTRACTS 

Materials and Methods: Our VBD algorithm, CUMULUS V, has been described earlier [1,2]. A 

calibration surface relating the imaging signal to the composition and thickness of breast phantoms is 

obtained experimentally, and corrected using simulation to compensate for the attenuation difference 

between the phantom materials and breast tissue. The response of the thickness readout of the 

mammography machine is characterized to allow for an accurate estimate of the breast thickness under 

compression. The ABD measurements were done using the CUMULUS area algorithm. The MR images 

were obtained from a study of high-risk women [3]. The images were segmented in four steps. 1) the 

inhomogeneities arising from the coil sensitivity were corrected semi-automatically in each slice; 2) a 

user delineated the chest wall and air boundaries; 3) a user manually selected the signal peaks due to fatty 

and fibroglandular tissue; 4) a soft-threshold function was applied between the peaks to transform the 

image into fractional density content. Images from 80 patients (left and right breast) were analyzed in this 

study. 

Results: The digital mammograms were obtained, on average, 3 years after the MR exam, when the mean 

age of the women was 45 years. Figure 1 and Table 1 compare the mammography and MR measurements. 

The mean VBD and volumes were 32.3 % and 677 cm 3 , respectively, for the mammography measurement 

versus 32.3 % and 645 cm 3 for the MR measurement. The comparison with ABD has not been completed 

at this point. 

Figure 1: comparison between the mammographic density algorithm and MRI for the VBD (left) and breast 

volume (right). 

31



RMS diff. Pearson Slope Intercept 

correlation 

VBD 10.1 % 0.801 1.05 -1.6 % 

Volume 157 cm 3 0.934 1.14 -55 cm 3 

Dense volume 66 cm 3 0.733 0.81 33 cm 3 

Table 1: Root mean square (RMS) difference, correlation, and linear least square fits between the MR and 

mammography measurements. 

Discussion and conclusion: There was good correlation and agreement between the MR and 

mammography measurements. The accuracies of the VBD algorithm and MR segmentation are 3-5 and 2- 

4 percentage points, respectively, thus we can expect an error of 4-7 % between the two. Further errors 

are expected to occur due to the time difference between the exams, and from differences in the imaged 

field-of-view. To the best of our knowledge, only one other study has investigated the relation between 

MR and mammography density measurements [4]. 

References: 

1. Alonzo-Proulx O et al. Phys. Med. Biol. 2010 55 3027-44 

2. Alonzo-Proulx O et al. Phys. Med. Biol. 2012 57 7443-57. 

3. Warner E, et al. J Clin Oncol. 2011 29(13) 1664-9. 

4. Van Engeland S et al. IEEE Trans. Med. Imaging 2006 25 273-82 

ABSTRACTS 

32



P3 COMPARISON OF CUMULUS AREA DENSITY WITH TWO AUTOMATED 

VOLUMETRIC BREAST DENSITY ALGORITHMS 

Olivier Alonzo-Proulx 1 , Martin Yaffe 1 , Jennifer Harvey 2 . 

1 Sunnybrook Health Sciences Centre, 2075 Bayview Ave., Toronto, Ontario Canada, M4N 3M5; 

2 University of Virginia Health System, Charlottesville, Virginia 

Purpose: Measurements of breast density made with the Cumulus interactive threshold algorithm have 

been shown to be moderately associated with breast cancer risk. In this work we compare two volumetric 

density (VBD) measures (the volume proportion of fibroglandular tissue), with Cumulus area percent 

density (PD) to validate the volumetric algorithms. 

ABSTRACTS 

Methods and materials: 310 women who had both film-screen (FS) and digital mammograms as part of 

the ACRIN DMIST were analyzed for PD (left CC, FS) and VBD (left and right CC, digital). One VBD 

algorithm, ‘Cumulus Volume’ is based on a prior calibration of the imaging signal versus tissue thickness 

and composition. The second algorithm, ‘Volpara’, finds a pixel where only fatty tissue is present, whose 

signal intensity is then used as a reference to determine the density on the image. PD and both VBD 

values were compared, and density color maps were visually reviewed. The relation between the VBD of 

the left-right pairs was investigated, as well as the relation between the two volumetric algorithms. 

Results: The correlation between Cumulus PD with Cumulus VBD and Volpara VBD yielded good 

results (R 2 correlation of 0.74 and 0.76 respectively, using a 2 nd order polynomial fit). Outliers between 

Cumulus PD and Cumulus VBD were due to thin breasts, which have a higher volume density because of 

the skin contribution, to thick breasts where the thickness estimation was erroneous, or from poorly 

exposed images. Outliers between Cumulus PD and Volpara VBD were due to the algorithm being unable 

to find an adequate reference fatty pixel on dense breasts. The linear correlation between Volpara and 

Cumulus VBD was very good (R 2 =0.82), and Volpara VBD overall had lower values since it excludes the 

effect of skin in the volume density. The linear correlation between the left and right breast using 

Cumulus VBD was excellent (R 2 =0.94). 

Conclusion: Values obtained using either volumetric algorithms showed good correlation to the area 

method, with only a few outliers, indicating that VBD can potentially be used in breast cancer risk 

models. The algorithms were strongly correlated with each other. The left and right Cumulus VBD were 

very strongly correlated, an indication of the algorithm’s reliability. 

Clinical relevance: Volumetric breast density measurements are automated, perfectly reproducible, 

correlate well with standard area density methods, and can thus be used to develop breast cancer risk 

models. 

Figure: 

33

P4 



COMPARISON OF THREE METHODS FOR DENSITY ASSESSMENT AND THEIR 

ASSOCIATION TO BREAST CANCER RISK 

V. Assi 1 , J. Warwick 2 , N. Perry 3 , K. Pinker-Domenig 4 , S. Duffy 1 

1 Centre for Cancer Prevention, Wolfson Institute of Preventive Medicine, Queen Mary University of 

London, London, UK; 2 Imperial Clinical Trials Unit, School of Public Health, Faculty of Medicine, 

Imperial College London, London, UK; 3 The Princess Grace Hospital, The London Breast Institute, 

London, UK; 4 Medical University Vienna, Department of Radiology, Vienna, Austria 

Introduction Despite mammographic density being recognised as a strong risk factor for breast cancer, 

its application in risk-prediction contexts is currently limited. This is partly due to difficulties in obtaining 

reliable density readings. New volumetric and fully-automated approaches, e.g. Quantra and Volpara, are 

expected to ensure a better performance: eliminating inter- and intra- reader variability, allowing the 

assessment of density in large numbers of women, and improving the risk-prediction by estimating more 

directly the amount of dense tissue. 

Purpose Evaluation of the estimates of mammographic density provided by Quantra and Volpara, 

compared to BIRADS, a well-established visual assessment method, as an indicator of breast cancer risk. 

Methods Quantra and Volpara are two software applications that, from full-field digital mammograms 

(FFDM), estimate the total volume (cm 3 ) of the breast and of the dense tissue (absolute density). From 

these, percent density and non-dense volume can be computed. The performance of BIRADS, Quantra 

and Volpara, as an indicator of breast cancer risk, was assessed using standard logistic regression adjusted 

for age on data from a retrospective case-control study, comprising 200 cases and 200 controls. 

Relationship between the volumetric methods and Birads was also investigated. 

Results Of the Quantra measures only absolute density had a significant association with risk, OR 1.04 

per 10 cm 3 [1.00 – 1.07], after age adjustment. Likewise, using Volpara, density in absolute terms was the 

most predictive, OR 1.07 per 10 cm 3 [1.00 – 1.13]. Standardising the odds ratios demonstrates that the 

two volumetric methods give very similar results: an increase of one standard deviation in absolute 

density was associated with age-adjusted ORs of 1.25 [1.02 - 1.53] and 1.26 [1.01 - 1.56], respectively for 

Quantra and Volpara. Absolute densities from these two methods were strongly correlated (82%), 

although neither Quantra nor Volpara were associated with BIRADS classification. Women in the highest 

Birads dense category were twice as likely to develop cancer as women in the least dense BIRADS 

category, which is somewhat weaker than expected and may indicate poorer predictive ability of this 

assessment method when performed on digital images. This could also partially explain why Birads and 

the two volumetric methods appear unrelated. 

ABSTRACTS 

Conclusions Both volumetric methods had a significant propensity in discriminating cases from controls. 

Absolute density was the most predictive. For digital images, absolute density measured with Quantra and 

Volpara may be a better indicator of breast cancer risk than BIRADS. 

34

P5 



MEASUREMENT OF COMPRESSED BREAST THICKNESS FOR BREAST DENSITY 

ASSESSMENT USING A GAMES CONSOLE INPUT DEVICE 

Jeremy Hewes *1 , Andrew Williamson *1 , Philip Noonan 2 , Jamie C. Sergeant 2 , Tina Dunn 3 , 

Claire Mercer 3 , Miriam Griffiths 3 , Sam Haste 2 , Alan Hufton 2 , Sue Astley 2 

1 School of Physics and Astronomy, University of Manchester, Manchester, UK ; 2 Centre for Imaging 

Sciences, Institute for Population Health, University of Manchester, Manchester, UK; 3 Nightingale 

Centre and Genesis Prevention Centre, University Hospital of South Manchester, Manchester, UK 

* joint first authors 

Introduction: Knowledge of compressed breast thickness is essential for accurate density assessment, 

but it has been shown that mammography system thickness outputs are inaccurate and compression plates 

tilt and bend for patient comfort. Furthermore, at the breast periphery the breast loses contact with the 

compression plate, and models or estimates of thickness must be used. Our aim is to design and evaluate a 

measurement method to overcome these limitations. 

ABSTRACTS 

The Microsoft Kinect games console input device provides a low-cost range finder that is suitable for use 

in medical applications. It projects an infra-red speckle pattern onto the object; this is observed by an 

infra-red detector. Depth is measured by analysing distortions in the received image. Here we assess the 

suitability of the Kinect for measuring the thickness of the compressed breast including the peripheral 

region. We have investigated the following properties: ability to image skin through the compression 

plate; impact of ambient lighting; accuracy; angular and distance dependence. 

Method: We used a Kinect with open-source Point Cloud Library and Microsoft’s Kinect for Windows 

SDK including Kinect Fusion; this can compare and combine successive frames streamed from the 

Kinect, enabling the creation of a 360 o degree model of an object and improving the accuracy of 

measurement. 

Experiments to investigate Kinect imaging of different materials, the impact of ambient lighting, 

dependence on distance and the angular response of the device were conducted in laboratory conditions. 

Further imaging was carried out using clinical mammography systems and a breast phantom. 

Distance dependence was investigated using a flat surface at distances from 50-150cm, in 10cm 

increments (50-100cm) and 25cm increments (>100cm). At each distance 20 frames were acquired and 

averaged. A region of interest was selected in the resulting image, and corrected for tilt using plane fitting. 

The standard deviation of depth information of all points described along the surface yielded a 

measurement of the Kinect’s precision. The degree of averaging necessary was investigated by capturing 

150 frames at distances from 60-130cm and analysing the precision as the number of frames averaged 

was reduced. Angular response was investigated by moving the Kinect to angles from 0 to 45 degrees and 

analysing the Kinect output. Investigations of dependence on material and ambient lighting were also 

undertaken. 

Results: Precision was 0.8mm at 50cm, 1.7mm at 100cm, and 11.1mm at 150cm; 20-frame averaging 

was found to be optimal. The Kinect’s response is highly material-dependent, and was reduced in 

environments with a high intensity of infra-red light. For reflective materials viewed head-on, such as the 

breast support platform, the reflection of the Kinect’s laser projector can interfere with depth-sensing 

capabilities. 

Conclusions: The Kinect provides a fast, cheap and effective method for measuring depth information to 

millimetre accuracy at distances typical of digital mammography units. Furthermore, Kinect Fusion 

promises to provide a good model for the compressed breast thickness in 3-D. 

35



Figure: A Kinect structured light image of a breast phantom compressed in a mammography system. 

ABSTRACTS 

36

P6 



ASSOCIATIONS OF MAMMOGRAPHIC DENSE AND NON-DENSE AREA AND BODY 

MASS INDEX WITH RISK OF BREAST CANCER 

Laura Baglietto 1,2 ; Kavitha Krishnan 1,2 ; Jennifer Stone 2 ; Carmel Apicella 2 ; Melissa C. Southey 1,3 ; Dallas 

R. English 1,2 ; John L. Hopper 2 ; Graham G. Giles 1,2,4 . 

1 Cancer Epidemiology Centre, Cancer Council Victoria, Melbourne, Australia (2) Centre for Molecular, 

Environmental, Genetic and Analytical Epidemiology, The University of Melbourne, Australia (3) 

Department of Pathology, The University of Melbourne, Australia; (4) Department of Epidemiology and 

Preventive Medicine, Monash University, Melbourne, Australia 

Mammographic density measures are associated with risk of breast cancer. Traditionally mammographic 

density has been quantified as percent mammographic density (PMD), and few studies have investigated 

the concurrent roles of absolute dense and non-dense mammographic areas, body mass index (BMI), age 

at mammogram and diagnosis on breast cancer risk. 

ABSTRACTS 

We conducted a matched case-control study nested within the Melbourne Collaborative Cohort Study 

(MCCS) to estimate the associations of dense area, non-dense area, and BMI with breast cancer risk under 

alternative causal models. 

Dense area was positively associated with risk and the strength of this association was only slightly 

influenced by the choice of the causal model. The association between non-dense area and breast cancer 

risk was negative under a direct causal relation and null otherwise. The choice of causal model affected 

the individual risk predictions, especially for women with extreme values of non-dense area. It also 

impacted on the estimates of the associations, interpretations, and the risk predictions which might vary 

considerably whether it is assumed that the risk factor is PMD or a linear combination of dense and nondense 

area. 

An alternative explanation for the negative association observed between non-dense area and breast 

cancer risk could be the intrinsic collinearity between dense and non-dense area, whose sum is 

constrained to be equal to the total breast area. The presence of two negatively correlated predictors in the 

same model could lead to biased estimates of their associations, resulting in a spurious negative 

association of non-dense area and an under-estimate of the association of dense-area. 

With our data we have not been able to choose whether a model based on PMD or on a linear 

combination of dense and non-dense areas provides a better fit. We have shown that, for non-dense area 

close to the population median, the two models are similar in their predictions, whereas for more extreme 

values of non-dense area they provide quite different predictions. A validation study conducted on a 

sample of women with more extreme values of non-dense area might help to determine which of these 

two models provide a better fit to the data. 

37

P7 



PREDICTION OF MAMMOGRAPHIC DENSITY USING REPRODUCTIVE AND 

LIFESTYLE CHARACTERISTICS 

Mariëtte Lokate 1, Linda M. Peelen 1, Marije F. Bakker 1, Petra H.M. Peeters 1, Carla H. van Gils 1 

1 Julius Center for Health Sciences and Primary Care, University Medical Center, PO Box 85500, 3508 

GA Utrecht, the Netherlands 

Introduction: Breast density is one of the strongest breast cancer risk factors, and is most commonly 

assessed using mammography. Since most mammographic breast cancer screening programs start at the 

age of 50 years, knowledge of this risk factor becomes available relatively late in life. Earlier information 

on this risk factor could help in planning preventive or early detection strategies in these high-risk 

women. In this study, we investigated if we can predict mammographic density using characteristics that 

are easy to obtain earlier in a woman’s life. 

Methods: Within the EPIC-NL cohort, we assessed mammographic density of 871 women aged 50 or 51 

years at mammographic examination. We constructed prediction models with mammographic density as 

continuous, categorical (

P8 



MAMMOGRAPHIC DENSITY AND RISK OF BREAST CANCER BY AGE AND 

TUMOR CHARACTERISTICS 

Kimberly A. Bertrand 1,2 , Rulla M. Tamimi 1,2 , Christopher G. Scott 3 , Matthew R. Jensen 3 , V. Shane 

Pankratz 3 , Daniel Visscher 4 , Aaron Norman 5 , Fergus Couch 5,6, , John Shepherd 7 , Bo Fan 7 , Yunn-Yi Chen 8 , 

Lin Ma 9 , Andrew H. Beck, 10 Walter C. Willett, 1,2,11 Steven R. Cummings, 12 Karla Kerlikowske 13 , Celine 

M. Vachon 5 

ABSTRACTS 

1 Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital and 

Harvard Medical School, Boston, MA; 2 Department of Epidemiology, Harvard School of Public Health, 

Boston, MA; 3 Division of Biomedical Statistics and Informatics, Mayo Clinic College of Medicine, 

Rochester, MN; 4 Department of Anatomic Pathology, Mayo Clinic College of Medicine, Rochester, MN; 

5 Department of Health Sciences Research, Division of Epidemiology, Mayo Clinic College of Medicine, 

Rochester, MN; 6 Department of Experimental Pathology and Laboratory Medicine, Mayo Clinic College 

of Medicine, Rochester, MN; 7 Department of Radiology, University of California, San Francisco, CA; 

8 Department of Pathology, University of California, San Francisco, CA; 9 Department of Medicine, 

University of California, San Francisco, CA; 10 Department of Pathology, Beth Israel Deaconess Medical 

Center and Harvard Medical School, Boston, MA; 11 Department of Nutrition, Harvard School of Public 

Health, Boston, MA; 12 San Francisco Coordinating Center, California Pacific Medical Center Research 

Institute; 13 Departments of Epidemiology and Biostatistics and General Internal Medicine Section, 

Department of Veterans Affairs, University of California, San Francisco, CA 

Background: Evidence on the association between mammographic density (MD) and breast cancer 

according to age and morphologic/histologic characteristics of tumors is mixed. Understanding whether 

MD is associated with all breast tumor subtypes and whether the strength of association varies by age is 

important prior to utilizing MD in risk models. 

Methods: Data were pooled from six cohort or case-control studies including 3414 women with breast 

cancer and 7199 without who underwent screening mammography. Percent MD was assessed from 

digitized film-screen mammograms using a computer-assisted threshold technique. We used polytomous 

logistic regression to calculate the odds of breast cancer according to tumor type, histopathological 

characteristics and receptor (ER, PR, HER2) status by age (51%) vs. average density (11-25%). Women ages 2.1 cm) vs. smaller (0.1-1 cm and 1.1-2 cm) tumor size 

and positive vs. negative lymph node status (P trend



Table. Pooled associations of categorical percent MD with breast cancer by ER status and 

age. 

Ages

P9 



MAMMOGRAPHIC PIXEL VARIANCE AND RISK OF BREAST CANCER IN A 

NESTED CASE-CONTROL STUDY 

Adam R. Brentnall 1 , Jane Warwick 2 , Elizabeth Pinney 1 , Jennifer Stone 3 , Ruth M.L. Warren 4 , Anthony 

Howell 5 , Jack Cuzick 1 

1 Centre for Cancer Prevention, Wolfson Institute of Preventive Medicine, Queen Mary University of 

London, London, UK; 2 Imperial Clinical Trials Unit, School of Public Health, Faculty of Medicine, 

Imperial College London, London, UK; 3 Centre for Molecular, Environmental, Genetic and Analytic 

(MEGA) Epidemiology, The University of Melbourne, Melbourne, Australia; 4 Cambridge Breast Unit, 

Addenbrooke's Hospital, Cambridge, UK; 5 Genesis Breast Cancer Prevention Centre, University 

Hospital of South Manchester, Manchester, UK 

A nested case-control study within the <strong>International</strong> Breast Cancer Intervention Study (IBIS-I) was used 

to assess a recent proposal from Heine et al (2012) to measure breast density automatically as the standard 

deviation of pixel intensity within the breast. The performance of our implementation of the method was 

compared with two percent density measures. 

ABSTRACTS 

Women were enrolled to the placebo arm, and had around twice the population risk of developing breast 

cancer. Case subjects were 49 women diagnosed with breast cancer at or after their first follow-up 

mammogram, and control subjects were 468 women from the same eight UK centres without breast 

cancer. The original mammograms were taken using a variety of different machines and scanned at an 8- 

bit greyscale resolution, but no further information is known about them, so controls were also matched 

within centre by year of mammogram. The mean time between baseline mammogram and diagnosis was 

6.1 years (inter-quartile range, IQR, 4.0 - 8.6). Mean age at baseline was 50.6 years (IQR 46.4 - 54.5), 

mean body mass index (BMI) was 26.7 kg/m2 (IQR 23.4 - 29.0) and 52% were pre-menopausal. 

Mammographic density was assessed at baseline by three methods: visually on an x-ray film illuminator 

by a single reader (RW) in 5% intervals, semi-automatically by a single reader (JS) as a continuous 

percent measure using computer software (cumulus 3.0), and a fully automatic estimate of the standard 

deviation of pixels within the breast (V measure). The V measure was calculated by an algorithm to (1) 

remove the background and pectoral muscle, (2) trim 25% of pixels from the breast edge, and (3) 

calculate the sample standard deviation of the remaining pixels. It used MLO views, taking the mean 

when both views were available (530 women). 

Linear regression was used to adjust percentage density for age and BMI. The different density measures 

were standardised by dividing by their sample standard deviation (SD) to facilitate comparison between 

measures. Pearson's correlation coefficient quantified association between the density measures. Odds 

ratios were obtained by conditional logistic regression. 

Pearson correlation of the V measure between left and right MLO views was 0.88 (95% CI 0.86 - 0.90). 

The V measure was moderately correlated with percentage density when assessed visually (Pearson 

correlation 0.25, 95% CI 0.17 - 0.32) and semi-automatically (Pearson correlation 0.34, 95% CI 0.27 - 

0.42), but the percentage measures were more strongly correlated with each other (Pearson correlation 

0.85, 95% CI 0.83 - 0.88). The V measure was at least as predictive (standardised measure odds ratio 

(SOR) 1.63 per SD, 95% CI 1.13 - 2.36) as visual percent density (SOR 1.39, 95% CI 1.00 - 1.92) and 

cumulus (SOR 1.28, 95% CI 0.93 - 1.77). This was also found when visual percent density was adjusted 

for age and BMI (SOR 1.53, 95% CI 1.15 - 2.04). 

Conclusion: The automated V measure appeared to be as predictive of future breast cancer as visuallyassessed 

and semi-automatic percentage density in this case-control study. 

41

P10 



VALIDATION OF A FULLY AUTOMATED, HIGH THROUGHPUT MAMMOGRAPHIC 

DENSITY MEASUREMENT TOOL FOR USE WITH PROCESSED DIGITAL 

MAMMOGRAMS 

AM Couwenberg, CH van Gils, J Li, R Pijnappel, RK Charaghvandi, M Hartman, HM Verkooijen 

Introduction: Recently, a public domain image-processing software (ImageJ) tool was developed to 

mimic Cumulus mammographic density readings. This fully-automated method has been validated for 

digitized film screen mammograms (JM Li et al. 2012). i Here we validate the Image J density reading tool 

in processed full field digital mammograms (FFDM), which are currently the most commonly available 

image types. We also correlate Image J readings with the clinically widely-used BIRADS density 

classification. 

Methods: This study includes Dutch women who underwent a FFDM mammography between 2001 and 

2011 at the University Medical Center Utrecht, the Netherlands. Only processed images were available. 

We composed a training set for model building (n=170 women, 331 images MLO and CC view of the 

contralateral breast) and a test set for model assessment (530 women, 4,429 images MLO and CC view of 

the contralateral breast). The training set was read with Cumulus by one trained reader and used to build 

the automated Image J mammographic density model. This model was used for the density assessment in 

the test set. These automated density readings were compared with BIRADS density readings by a 

dedicated breast radiologist. We used Pearson product-moment correlation coefficient (r) to compare 

Cumulus measurements with the automated measurements in the training set. Correlation and of the 

automated method with BIRADS density readings in the test set was estimated using Spearman 

correlation coefficient. As a further validation, we investigated the association between breast cancer risk 

factors and the automated density measurements for the patients in our test set, using generalized linear 

models. 

Results: We observed a high correlation between the automated method and Cumulus (r=0.90 95% Cl: 

0.86 – 0.93). We also found a high correlation between the automated measure and the BIRADS density 

classification in the test set (spearman r= 0.862). Women with a higher density as measured with the 

automated method, were significantly younger, more often premenopausal, had a lower number of 

children, and more often a benign breast lesions or a family history of breast cancer. 

Conclusion: We demonstrated that the high-throughput automated density assessment tool, based on the 

analysis of the established method of Cumulus and previously validated on digitized film-screen 

mammograms, can be used on processed digital mammograms. Its results also strongly correlate with the 

clinically widely-used BIRADS density readings and with established breast cancer risk factors. 

ABSTRACTS 

• 

1 Li et al. High-throughput mammographic-density measurement: a tool for risk prediction of breast cancer. 

Breast Cancer Research 2012, 14:R114. 

42

P11 



RESULTS OF SUPPLEMENTAL ULTRASOUND SCREENING IN WOMEN WITH AND 

WITHOUT DENSE BREAST TISSUE 

Judy C Dean MD 

Recent awareness of the increased risk of breast cancer, and higher rate of interval cancer that escapes 

detection with mammography have led to recommendations for supplemental screening with ultrasound, 

yet little is known about the diagnostic yield of these examinations. 

Since November, 2005 I have performed almost 10,000 automated whole breast ultrasound studies with 

digital mammograms, primarily in women with either increased breast cancer risk or increased breast 

density. Stratification by breast density (as analyzed visually using the Bi-Rads classification system) 

shows significantly more cancers detected in women with increased breast density. 

Cancer Detections by Imaging Method: 

ABSTRACTS 

50 

40 

30 

20 

10 

0 

Mam only Both ABUS 

only 

Either 

The table above shows that half of all cancers detected were identified with automated breast ultrasound only, and 

not imaged by mammography. Only one of the additional cancers detected by ultrasound was DCIS, and all others 

were invasive cancers. 

43



Cancers detected per 1000 women screened, by Bi-Rads Density Score: 

20 

15 

10 

5 

0 

In women with Bi-Rads density 4, per 1000 women screened 17 cancers were detected. For density 3, 10.5 per 

1000, and for density 1 and 2, 4 per 1000. 

Cancers detected by ultrasound only per 1000 women screened by Bi-Rads density: 

18 

16 

14 

12 

10 

8 

6 

4 

2 

0 

1 & 2 3 4 

1 & 2 3 4 

ABSTRACTS 

Additional cancers detected with ultrasound only and not visualized with mammography were approximately 12 per 

1000 for density 4, almost 4 per 1000 for density 3, and only 1 per 1000 for density 1 and 2. 

This data confirms that ultrasound will detect significantly more cancers than mammography alone in women with 

dense breast tissue. Since nearly all the additional cancers detected are invasive tumors, these are likely to be more 

clinically relevant than cancers detected with mammography alone, which are 30 to 50% non-invasive. 

44

P12 



COMPARISON OF CUMULUS MAMMOGRAPHIC DENSITY READINGS BETWEEN 

FUJIFILM COMPUTED RADIOGRAPHY DIGITAL MAMMOGRAPHIC IMAGES IN 

RAW AND DISPLAY FORMATS 

Caroline Dickens, Valerie McCormack 

Section of Environment and Radiation, <strong>International</strong> Agency for Research on Cancer, Lyon 69008, 

FRANCE. 

Background: The processing of digital mammograms from their raw to processed or display formats in 

order to improve tumor detection involves manipulation of the image contrast by means of proprietary 

image processing algorithms, which differ between vendors. It has been recommended that raw images 

be used for estimation of mammographic density as they maintain image contrast, but in some instances 

they are not available. Recent studies have shown a good correlation in percent breast density (PD) 

between raw and processed images acquired using the General Electric Senographe FFDM 

mammography units. We wished to see if similar comparability exists between PD readings performed 

on raw and processed images obtained using the Fujifilm Computed Radiography (CR) imaging system. 

ABSTRACTS 

Methods: Bilateral raw and processed craniocaudal (CC) Fujifilm CR images were obtained for 100 

women. Images were processed using the Fujifilm Image Intelligence algorithm. 400 images (100 

women x 2 breasts x 2 formats) were anonymized and randomly sorted into 5 batches of 80 prior to 

reading. PD was estimated by a trained observer (VM) using an interactive-threshold method (Cumulus3). 

Comparisons of PD and absolute dense area between the raw and processed images were conducted using 

Spearman’s rank correlation coefficient (r), t-tests for mean differences and kappa statistics for the 

agreement of categories. Differences according to level of density were also examined. 

Results: Minimum-maximum ranges (0-55% for raw and 0-58% for processed images) were similar in 

the two image output formats, but processed images had a larger standard deviation (14.8 vs 12.8). Mean 

PD was 22.8% (95% confidence interval (CI): 21.0, 24.6) for raw images and 25.9 (23.8, 27.9) for 

processed images. Processed images had a mean PD 3.1 (2.0, 4.1) absolute percentage points higher than 

in the corresponding raw image. For raw images with PD in the range

P13 



RISK ASSOCIATION OF SUBREGIONAL MEASUREMENTS OF BREAST 

DENSITY 

Duewer, Fred 1 , Fan, Bo 1 , Karla, Kerlikowske 2,3 , Ma, Lin 2,3 , Malkov, Serghei 1 , and Shepherd, John 1 

University of California, San Francisco, Department of Radiology 1 , Department of Epidemiology and 

Biostatistics 2 , and Department of Medicine 3 

Although breast density is, excepting age, the strongest known risk factor for breast cancer, little is known 

about the spatial variation in breast density. Our hypothesis is that the association of local fibroglandular 

volume with breast cancer in a given subregion of the breast varies based on the position of that subregion 

relative to the nipple and breast outline. 

We used single x-ray absorptiometry to obtain pixel-by-pixel maps of breast density and spatially 

normalized each breast density map using estimated midplane position and the breast outline on casecontrol 

data set consisting of 278 cases and 834 controls matched by age and body mass index. We 

measured the absolute fibroglandular volume in 100 subregions for each image. We compared average 

fibroglandular volume in subregions and the absolute left-right difference in each subregion (asymmetry) 

depending on case-control status. We found that average fibroglandular volume was higher in cases than 

controls for all subregions and that left-right fibroglandular volume asymmetry was also higher for cases 

than for controls. 

We tested for regional differences in breast cancer risk association between cases and controls and 

according to age and body mass index by measuring the P-value of the t-test between cases and controls 

for each subregion. We found that subregions closer to the nipple differed more significantly between 

cases and controls. In addition, we used principal component analysis on the average fibroglandular 

volume in the 100 subregions and identified three principal components that contributed significantly to 

breast cancer risk (P



P14 THE ASSOCIATION BETWEEN VITAMIN D INTAKE, SEASON AND 

MAMMOGRAPHIC DENSITY IN NORWEGIAN POSTMENOPAUSAL WOMEN 

Merete Ellingjord-Dale¹, Isabel dos Santos Silva ² , Tom Grotmol³, Solveig Hofvind³, Lene Frost 

Andersen¹, Samera Azeem Qureshi¹, Marianne Skov Markussen¹, Elisabeth Couto⁵, Giske Ursin 1,3,4 

1 University of Oslo, Norway, 2 London School of Hygiene and Tropical Medicine, UK , 3 Cancer Registry of 

Norway, Norway, 4 University of Southern California, USA, ⁵Norwegian Knowledge Centre for the Health 

Services, Norway 

ABSTRACTS 

Background: It is unclear whether vitamin D intake is associated with breast cancer risk. There is some, 

albeit inconsistent, evidence that mammographic density (MD) varies by season of the year or by vitamin 

D intake. We evaluated the association of MD with seasonality and vitamin D intake among a subset of 

women who participated in the Norwegian Breast Cancer Screening Program (NBCSP) in 2004 or in 

2008. 

Methods: We conducted a cross-sectional study to examine the association of MD with vitamin D intake 

and seasonality among 2662 postmenopausal women aged 50-69 years. Vitamin D intake (estimated with 

and without taking into account supplement use) was ascertained through a self-administered 

questionnaire. Density on digitized analogue films was measured using a computer assisted method 

(Madena, University of Southern California). Linear regression methods were used to determine the 

association of MD with vitamin D intake and the season in which the mammogram was obtained. 

Specifically we estimated least square means of MD across categories of the exposure variables, while 

adjusting for potential confounders. 

Results: There was no association between intake of vitamin D and MD, across quartiles of vitamin D 

intake (0-5, 6-8, 9-12 and 13-49 mcg per day, estimated with supplements). Mean MD was 18.5% 

(SD=15.3%) in women with the lowest, and 18.9% (SD=15.6%) in women with the highest intake of 

vitamin D (p for trend=0.73). There was no association between vitamin D intake and MD in women who 

had their mammogram during January-May, i.e. at the time when vitamin D levels are presumed to be at 

the lowest (p for trend= 0.10). 

Similarly, there was no statistical significant association between month of mammography and MD in 

either the 2004 or the 2008 data. 

Conclusion: Our cross-sectional study found no evidence of an association of MD with vitamin D intake 

or month/season of mammography. 

47

P15 



THE ASSOCIATION BETWEEN GEOGRAPHIC STATUS AND MAMMOGRAPHIC 

DENSITY 

Marleen J. Emaus 1 , Marije F. Bakker 1 , Petra H.M. Peeters 1, 2 , Carla H. van Gils 1 


GA Utrecht, the Netherlands; 2 Department of Epidemiology, Public Health and Primary Care, Faculty of 

Medicine, Imperial College, Norfolk Place, London W2 1PG, UK 

Introduction: It is frequently observed that mammographic density varies depending upon geographic 

area. It is unclear whether these differences are related to the degree of urbanization (as proxy variable for 

environmental factors such as exposure to traffic emissions) or to differences in social economic status 

(SES) (as proxy variable for lifestyle and reproductive characteristics). In the present study we try to 

unravel this issue within the Dutch screening population. 

Methods: The study was performed within the Prospect-EPIC cohort. Prospect-EPIC participants were 

recruited through the breast cancer screening program in Utrecht and vicinity between 1993 and 1997. 

Urbanization was categorized according to the number of addresses per kilometer squared using the 5- 

group classification established by Statistics Netherlands. SES was measured at neighborhood level 

(Statusscore) using data from Statistics Netherlands as well as at individual level (educational level of the 

participant). General linear models were used to study the association between the degree of urbanization 

and SES on the one hand and mammographic density on the other hand (i.e. percent density and dense 

area). The models were adjusted for age and BMI. Analyses with additional adjustment for lifestyle and 

hormonal risk factors for breast density will be presented at the conference. 

Results: We included 2,805 women with a mean age of 57.3 years (SD: 5.9 years). Women living in 

urban areas showed higher percent density than women in rural areas (adjusted mean percent density urban = 

21.9%, 95%CI 21.1-22.8 versus adjusted mean percent density rural = 15.1, 95% CI 12.8-17.7, p- 

trend

P16 

INTER-SCAN REPRODUCIBILITY OF MAMMOGRAPHIC DENSITY 

MEASUREMENTS 



Mariëtte Lokate 1 , Marleen J. Emaus 1 , Michiel Kallenberg 2,3 , Nico Karssemeijer 2 , Wouter B. Veldhuis 4 , 

Petra H.M. Peeters 1 , Carla H. van Gils 1 


GA Utrecht, the Netherlands; 2 Department of Radiology, Radboud University Nijmegen Medical Center, 

Geert Grooteplein Zuid 18, 6525 GA Nijmegen, the Netherlands; 3 Matakina Technology Ltd, PO Box 

24404, Manners Street Central, Wellington, 6142, New Zealand; 4 Department of Radiology, University 

Medical Center, PO Box 85500, 3508 GA Utrecht, the Netherlands 

Introduction: Changes in mammographic density are frequently used as an intermediate marker to 

evaluate the effect of interventions for reducing breast cancer risk. We investigated the robustness of 

measurement of changes in mammographic density. We assessed consistency of mammographic density 

measurements between pairs of mammograms. They were made within a short time interval, in which no 

actual change in mammographic density can be expected. 

ABSTRACTS 

Methods: We included 170 women who underwent two mammographic examinations within a median 

follow-up period of 12 days (maximum 6 months) : 106 had two Full-Field Digital Mammograms 

(FFDM), 12 had two Film-Screen Mammograms (FSM) and 52 had one FSM and one FFDM. 

Mammographic density was assessed by a single reader using a computer assisted threshold method 

(Cumulus). The correlation between percent mammographic density measurements was estimated using 

intraclass correlation coefficients and we also calculated mean absolute difference in density measures 

between both mammograms. Linear regression was performed to adjust for differences in acquisition 

parameters between mammograms. 

Results: The intraclass correlation between percent density measurements on two FFDMs was 0.93 

(95%CI 0.91–0.95), between two FSMs 0.94 (95% CI 0.84-0.98), and between FFDM and FSM 0.72 

(0.61-0.80). The mean absolute difference in percent density between two FFDMs of the same woman 

was 4.3% (95% CI 3.8% to 4.9%) and between two FSMs 6.5% (95% CI 3.4% to 9.6%). On average, 

FSM showed a higher percent density compared to FFDM in the same women (difference 12.9% (95% CI 

11% to 14.8%). Adjustment for differences in exposure, peak kilo voltage, compression force and breast 

thickness between mammograms did not alter the results. 

Conclusion: When studying changes in mammographic density, it is important to compare mammograms 

of the same type (i.e. either all FSM or all FFDM). Although intraclass correlation between density 

measured on mammograms of the same type (either FSM or FFDM) is high, the absolute differences in 

percent density are of a magnitude that may influence detectability of real changes in mammographic 

density. 

49

P17 



MULTIPLE METABOLIC RISK FACTORS AND MAMMOGRAPHIC BREAST 

DENSITY IN THE NEW YORK CITY MULTIETHNIC BREAST CANCER PROJECT 

Parisa Tehranifar, 1,2,3 Diane Reynolds, 4 Xiaozhou Fan, 1 Bernadette Boden-Albala, 5 Natalie Engmann, 1 

Julie D. Flom, 1 Mary Beth Terry 1,2 

1 Department of Epidemiology, Columbia University Mailman School of Public Health, New York, NY ; 

2 Herbert Irving Comprehensive Cancer Center, Columbia University Medical Center, New York, NY ; 

3 The Center for the Study of Social Inequalities and Health, Columbia University Mailman School of 

Public Health, New York,, NY; 4 School of Nursing, Long Island University, Brooklyn Campus, Brooklyn, 

NY; 5 Division of Social Epidemiology, Department of Health Evidence and Policy, Department of 

Neurology, ICAHN School of Medicine at Mount Sinai, New York, NY 

Acknowledgement: We would like to thank the study participants for contributing data to this analysis and 

the following research staff for assisting with data collection and recruitment activities: Diane Levy, 

Wendy Lewis, Gladys Rivera, Zoe Quandt, Joy White, Jessica Cabildo and Renata Khanis. This research 

was funded by grants from the National Cancer Institute (grant numbers: U54 CA101598; K07 

CA151777), Susan B. Komen Foundation Career Catalyst award (grant number KG110331) and the 

National Institute of Environmental Health Sciences Center Support (grant number ES009089). 

Background: Metabolic abnormalities have been individually associated with breast cancer risk. More 

recently, studies have also shown modest positive associations between the presence of multiple 

metabolic conditions, as frequently defined by the metabolic syndrome (MetS), and breast cancer risk in 

postmenopausal women. Few studies have examined whether the presence of multiple metabolic risk 

factors are similarly associated with breast density, an intermediate marker of breast cancer risk. We 

examined whether obesity, diabetes, hypertension, and elevated cholesterol, individually and in 

combination, are associated with breast density. 

Methods: We digitized film mammograms and measured percent density and dense area using a 

computer-assisted method in a sample of 191 women, recruited from a single radiology facility during 

their screening mammography appointment (age range=40-61 years; 42% African American, 22% African 

Caribbean, 22% White, 11% Hispanic, 3% other ethnicities). We used in-person computer assisted 

interviews to collect detailed epidemiologic data at the time of recruitment. We used linear regression 

models to examine the associations of each metabolic condition and the number of co-occurring 

metabolic conditions (0, 1, 2, and 3 or 4 conditions) with percent density and dense breast area. 

ABSTRACTS 

Results: In age adjusted models of individual metabolic conditions, hypertension and obese body mass 

index (≥30 kg/m 2 ) were inversely associated with percent density, and high blood cholesterol was 

inversely associated with dense area. The presence of multiple metabolic conditions was associated with 

lower percent density and dense area in multivariable models (2 conditions and 3 or 4 conditions vs. 0 

conditions were associated with 9.4% (95% CI:-13.2, -5.5) and 10.7% (95% CI:-15.2, -6.2) reduction in 

percent density and with 5.5 cm 2 (95% CI: -10.8, -0.3) and 8.4 cm 2 (95% CI: -14.5, -2.4) smaller dense 

area). 

Conclusions: The presence of multiple metabolic conditions was associated with lower levels of relative 

and absolute measures of breast density in a predominantly racial minority sample of women. The 

reduction in breast density associated with multiple metabolic conditions was greater than the influence of 

any single condition on breast density. Our results suggest that the positive association between 

metabolic abnormalities and breast cancer may not involve pathways related to breast density, and may be 

stronger after accounting for the influence of metabolic conditions on breast density. 

50



P18 GENETIC TESTING FOR BREAST CANCER RISK ESTIMATION – A COST - 

EFFECTIVENESS ANALYSIS 

Henri Folse 1 (corresponding and presenting author), Tuan Dinh 1 , Richard Allman 2 

1 Archimedes, Inc., 2 Genetic Technologies, Ltd. 

Genetic testing based on seven single-nucleotide polymorphisms (7SNP) can improve individualized 

estimates of lifetime risk of breast cancer relative to the Gail risk test alone, for the purpose of 

recommending MRI screening for women at high risk. 

ABSTRACTS 

An individual-based, continuous-time simulation model of breast cancer and health care processes was 

used to simulate women in a virtual trial comparing the use of the 7SNP test to the Gail risk test alone to 

categorize patients as either low risk or high risk. Low risk patients received annual mammogram, while 

high risk patients received annual MRI. Cancer incidence was based on Surveillance, Epidemiology, and 

End Results (SEER) data and validated to the Cancer Prevention Study II (CPS-II) Nutrition Cohort data 

set. Risk factors are drawn from the National Health and Nutrition Examination Survey (NHANES-4) and 

Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial (PLCO) data sets. Mammogram 

characteristics were derived from the Breast Cancer Surveillance Consortium (BCSC) dataset. 

Other parameters were derived from published literature. The 7SNP test (vs Gail alone) saved 0.00734 

quality-adjusted life-years (QALYs) per person at a cost of $1,971 per person ($268,386 per QALY). 

Limiting the 7SNP test to only those patients with a lifetime Gail risk of 16 – 28% resulted in a cost of 

$163,264 per QALY. These results were sensitive to the age at which the test is given, the discount rate, 

and the costs of the genetic test and MRI. The cost-effectiveness of using the 7SNP test for patients with 

intermediate Gail risk is similar to that of other recommended strategies, including annual MRI for 

patients with a lifetime risk greater than 20% or BRCA1/2 mutations. 

51

P19 



ADIPOSITY AND MAMMOGRAPHIC BREAST DENSITY IN PREMENOPAUSAL 

CHILEAN WOMEN 

Maria Luisa Garmendia 1 , Ana Pereira 1 , John Shepherd 2 , Ralph Highnam 3 , Camila Corvalán 1 , 

1 Institute of Nutrition and Food Technology, University of Chile, Chile; 2 Department of Radiology and 

Biomedical Imaging University of California at San Francisco, United States; 3 Mātakina Technology 

Limited, Wellington, New Zealand 

Background: Breast density is one of the strongest risk factors for breast cancer. In premenopausal 

women, obesity decreases the risk of breast cancer, but the relationship between breast dense tissue and 

obesity remains unclear. 

Objectives: To assess the relationship between adiposity indicators and absolute breast dense volume 

(fibroglandular tissue volume) and to evaluate metabolic and hormonal factors as potential mediators in 

these relationships. 

Methods: 

Design: This is a cross-sectional study of the mothers of the girls participating in the Growth and Obesity 

Chilean cohort Study (GOCS) participants. GOCS is a population-based ambispective cohort of 1196 

children born in 2002 in six low and middle-income counties from the South-area of Santiago, Chile. The 

study sample is representative of the population that receives care at the National Health Services System 

(70% of the entire population). In 2011-2012, 400 premenopausal mothers of the GOCS girls (mean age = 

36.9 years, SD= 6.4) were included in a longitudinal study of breast cancer risk factors (DERCAM 

cohort). 

Procedures: Weight, height, body mass index (BMI= weight (kg)/ height(m) 2 ), waist circumference 

(WC), and waist-to-height ratio (WHR) were measured by trained nutritionists. Percentage total fat was 

estimate by bioimpedance (Tanita). All women underwent digital mammography during the follicular 

phase of their menstrual cycle. Volumetric measures of breast density (absolute fibroglandular volume, 

adipose volume, and percentage fibroglandular volume) were determined using an automated method 

(Volpara®, Matakina Technology, Wellington, New Zealand). Age, education and parity were selfreported 

by questionnaire. Fasting insulin, glycaemia, insulin growth factor-1 (IGF-1), estrone, estradiol, 

progesterone, androstenedione, follicle-stimulating hormone (FSH) and sex hormone binding globulin 

(SHBG) were measured in the first week of the follicular phase of the menstrual cycle. 

ABSTRACTS 

Statistical analyses: We used crude and adjusted linear regression models to quantify associations 

between adiposity indicators and breast dense volume (log-transformed). 

Results: 39% of the women were overweight and 31% obese. All adiposity indicators were positively and 

similarly associated with log-dense volume [ie, BMI, β standardized regression coefficient: 0.02 (95% CI: 

0.01, 0.03); WC, β standardized regression coefficient: 0.01 (95% CI: 0.01, 0.13)]; these associations 

remained significant after adjusting by sociodemographic and metabolic and hormonal factors. 

Conclusion: In premenopausal Chilean women adiposity markers, either measured by BMI, WC, WHR 

and percentage of total fat, are positively associated with breast dense volume. This association is only 

partially explained by metabolic and breast cell proliferative hormonal factors, suggesting that other 

pathways may also be involved. Longitudinal studies are needed to confirm our findings and to better 

understand the links between obesity, breast dense tissue and breast cancer risk, in premenopausal 

women. 

52

P20 



RELATIONSHIP OF SERUM ESTROGEN LEVELS WITH AREA AND VOLUME 

MEASURES OF MAMMOGRAPHIC DENSITY AMONG WOMEN UNDERGOING 

BREAST BIOPSY FOR CLINICAL INDICATIONS 

Gretchen Gierach 1 , Deesha Patel 1 , Roni Falk 1 , Ruth Pfeiffer 1 , Berta Geller 2 , Pamela Vacek 2 , Donald 

Weaver 2 , Rachael Chicoine 2 , John Shepherd 3 , Jeff Wang 3 , Bo Fan 3 , Xia Xu 5 , Timothy Veenstra 5 , Barbara 

Fuhrman 4 , Louise Brinton 1 , and Mark Sherman 1 

1 

National Cancer Institute, National Institutes of Health, Rockville, MD, USA; 2 University of Vermont, 

Burlington, VT, USA; 3 University of California, San Francisco, San Francisco, CA, USA; 4 University of 

Arkansas for Medical Sciences, Little Rock, AR, USA; 5 Frederick National Laboratory for Cancer 

Research, SAIC-Frederick, Inc., Frederick, MD, USA 

ABSTRACTS 

Background: Mammographic density (MD), assessed as the area of non-fatty appearing tissue divided by 

the total breast area (MD-A), is an established breast cancer risk factor among pre- and postmenopausal 

women. Although increased exposure to estrogens is hypothesized to increase both MD and breast cancer 

risk, data relating serum estrogen levels to MD are inconsistent. These inconsistencies may reflect 

differences in methods of measuring estrogens, the specific estrogens assessed, and variable techniques 

for measuring MD. To elucidate these relationships, we examined the associations of a panel of serum 

estrogens and estrogen metabolites (EM), as measured by a newly developed assay, with both area (MD- 

A) and volume (MD-V) density measures in a cross-sectional study of women undergoing breast biopsy. 

Methods: Pre- and postmenopausal women (n=194), ages 40-65, undergoing a diagnostic image-guided 

breast biopsy at a facility of the Vermont Breast Cancer Surveillance System were enrolled (2007-2010). 

Serum parent estrogens, estrone (E 1 ) and estradiol (E 2 ), and their 2-, 4-, and 16-hydroxylated metabolites 

(conjugated and unconjugated forms, the latter of which are thought to be more biologically available) 

were measured using a validated, sensitive liquid chromatography tandem mass spectrometry assay. 

Laboratory coefficients of variation were less than 3% for all analytes. MD-A and MD-V were assessed 

in craniocaudal views of the breast contralateral to the biopsy target in digital mammograms using 

computerized thresholding software (cm 2 ) and single X-ray absorptiometry (mL). Linear regression 

models were used to estimate associations of estrogens with square-root transformed MD-A and MD-V, 

expressed as absolute values and as percentages of breast area or volume, adjusted for the critical 

confounders of age and body mass index. Analyses were stratified by menopausal status and, in 

premenopausal women, by menstrual cycle phase. 

Results: Among premenopausal women (n=106), circulating levels of most individual EM were not 

associated with MD-A or MD-V, although positive associations between percent MD-A and levels of 

unconjugated E 1 and unconjugated E 2 were suggested for luteal phase women (E 1 : p=0.08; E 2 : p=0.07). 

Among postmenopausal women (n=88), higher circulating levels of unconjugated E 1 showed an 

association with increased percent MD-V (p=0.049), but not with MD-A. Higher unconjugated E 2 levels 

showed non-significant positive relationships with absolute and percent MD-A and MD-V measures (pvalues 

ranged from 0.07-0.11). Levels of the 2-, 4- and 16-hydroxylation pathway EM were positively 

associated with MD-A measures (percent MD-A: p=0.009, p=0.02, p=0.02; absolute MD-A: p=0.05, 

p=0.09, p=0.03, respectively), but not with MD-V. 

Conclusions: Positive associations between serum estrogen profiles and MD were apparent among 

women undergoing breast biopsy for clinical indications. Further, our findings suggest that different 

measures of MD may provide different information about the biologic underpinnings of breast tissue 

composition. Future studies assessing relationships of estrogen metabolites and MD may provide clues 

about the etiology of breast cancer. 

53

P21 



MAMMOGRAPHIC DENSITY PHENOTYPES AND RISK OF BREAST CANCER: A 

META-ANALYSIS 

Andreas Pettersson 1 , Rebecca E. Graff 1 , Laura Baglietto 2, 3 , Marije F. Bakker 4 , Norman Boyd 5 , KeeSeng 

Chia 6 , Kamila Czene 7 , Isabel dos Santos Silva 8 , Louise Eriksson 7 , Graham G. Giles 2, 3, 9 , Per Hall 7 , Mikael 

Hartman 6, 7, 9 , John L. Hopper 3 , Kavitha Krishnan 2, 3 , Jingmei Li 11 , Qing Li 5 , Gertraud Maskarinec 12 , 

Valerie McCormack 13 , Ian Pagano 12 , Bernard A. Rosner 14, 15 , Christopher Scott 16 , Jennifer Stone 3 , Giske 

Ursin 17, 18 , Celine Vachon 16 , Carla H. van Gils 4 , Chia Siong Wong 6 1, 14 

, Rulla M. Tamimi 

1) Department of Epidemiology, Harvard School of Public Health, Boston, MA, USA; 2) Cancer 

Epidemiology Centre, Cancer Council Victoria, Melbourne, Australia; 3) Centre for Molecular, 

Environmental, Genetic and Analytical Epidemiology, The University of Melbourne, Australia; 4) Julius 

Center for Health Sciences and Primary Care, University Medical Center, Utrecht, The Netherlands; 

5) Campbell Family Institute for Breast Cancer Research, Ontario Cancer Institute, Toronto, Ontario, 

Canada; 6) Saw Swee Hock School of Public Health, National University of Singapore, National 

University Health System, Singapore; 7) Department of Medical Epidemiology and Biostatistics, 

Karolinska Institutet, Stockholm, Sweden; 8) Department of Non-Communicable Disease Epidemiology, 

London School of Hygiene and Tropical Medicine, London, United Kingdom; 9) Department of 

Epidemiology and Preventive Medicine, Monash University, Melbourne, Australia; 10) Department of 

Surgery, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; 11) Human 

Genetics Division, Genome Institute of Singapore, Singapore; 12) Department of Epidemiology, University 

of Hawaii Cancer Center, Honolulu, HI, USA; 13) Section of Environment and Radiation, <strong>International</strong> 

Agency for Research on Cancer, Lyon, France; 14) Channing Division of Network Medicine, Department 

of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA; 

15) Department of Biostatistics, Harvard School of Public Health, Boston, MA, USA; 16) Department of 

Health Sciences Research, Mayo Clinic College of Medicine, Rochester, MN, USA; 17) Department of 

Nutrition, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway; 18) Department of 

Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA 

Background: Fibroglandular breast tissue appears dense on mammogram; fat appears nondense. We 

investigated whether absolute or percent dense area more strongly predicts breast cancer risk, and whether 

absolute nondense area is independently associated with risk. 

Methods: We conducted a meta-analysis of 13 case-control studies, each having provided parameter 

estimates and standard errors from logistic regressions for associations between one standard deviation 

(SD) increments in mammographic density phenotypes and breast cancer risk. We used random-effects 

models to calculate pooled odds ratios (ORs) and 95% confidence intervals (CIs). 

ABSTRACTS 

Results: Among premenopausal women (1,776 cases; 2,834 controls), summary ORs for one SD 

increments were 1.37 (95% CI: 1.29-1.47) for absolute dense area, 0.78 (95% CI: 0.71-0.86) for absolute 

nondense area, and 1.52 (95% CI: 1.39-1.66) for percent dense area when pooling estimates adjusted for 

age, BMI and parity. Corresponding ORs among postmenopausal women (6,643 cases; 11,187 controls) 

were 1.38 (95% CI: 1.31-1.44), 0.79 (95% CI: 0.73-0.85), and 1.53 (95% CI: 1.44-1.64). After additional 

adjustment for absolute dense area, associations between absolute nondense area and breast cancer 

became attenuated or null in several studies and summary ORs became 0.82 (95% CI: 0.71-0.94; P- 

heterogeneity: 0.02) for premenopausal and 0.85 (95% CI: 0.75-0.96; P-heterogeneity:



P22 MAMMOGRAPHIC DENSITY AND EARLY MAMMOGRAPHIC SCREENING 

PERFORMANCE MEASURES IN THE NORWEGIAN BREAST CANCER SCREENING 

PROGRAM 

Solveig Hofvind 1,2* Isabel dos-Santos-Silva 3 , Merete Ellingjord-Dale 4 , Sofie Sebuødegård 1 , Giske Ursin 1,5 

1 Cancer Registry of Norway, Oslo, Norway; 3 Department of Non-Communicable Disease Epidemiology, 

London School of Hygiene and Tropical Medicine, London, UK ; 4 Institute of Basic Medical Sciences, 

University of Oslo, Oslo, Norway ; 2 Oslo and Akershus University College of Applied Sciences, Oslo, 

Norway; 5 University of Southern California, Los Angeles, CA 

ABSTRACTS 

Background: It is well known that mammographic density (MD) increases the risk of breast cancer and 

reduces sensitivity of mammographic screening. However, less is known on how MD may affect early 

performance measures in mammographic screening among those recalled for further assessments. We 

used data collected by the population-based Norwegian Breast Cancer Screening Program (NBCSP) to 

investigate associations of MD and the percentage of cancers detected among women recalled for further 

assessment (positive predictive value one, PPV-1), as well as percentage of cancers detected among the 

women who underwent a biopsy (fine needle aspiration cytology or core needle biopsy)(PPV-2) and MD 

associations with prognostic histopathological tumor characteristics. 

Methods: We analyzed data from women screened in the NBCSP during 1996-2010 who were 

subsequently recalled for further assessments. Visual assessment of MD was routinely performed on 

analogue (80%) or digital mammograms by radiologists with expertise in mammography using an inhouse 

visual 3-category scale (low: MD



P23 CHILDHOOD AND ADOLESCENT GROWTH PREDICTS MAMMOGRAPHIC 

DENSITY MEASURES ASSOCIATED WITH BREAST CANCER RISK IN MID-LIFE: A 

PROSPECTIVE STUDY 

John L. Hopper 1 , Jennifer Stone 1 , Carmel Apicella 1 , Linh T. Nguyen 1 , Quang M. Bui 1 , Gillian S. Dite 1 , 

Daniel F. Schmidt 1 , Enes Makalic 1 , Melanie C Matheson 1 , Shyamali Dharmage 1 , Graham G. Giles 2 

1 Centre for Molecular, Environmental, Genetic and Analytic Epidemiology, The University of Melbourne, 

Carlton VIC 3053, Australia; 2 Cancer Epidemiology Centre, Cancer Council Victoria, Carlton VIC 3053, 

Australia 

Background: Mammographic density (MD) adjusted for age and body mass index (BMI) is associated 

with breast cancer risk. We conducted a prospective study to determine if growth in childhood and 

adolescence predicts risk-associated MD. 

Methods: In 1968, the Tasmanian Asthma Study measured height and weight for almost all school 

children born in 1961. We obtained measured heights and weights for this cohort from annual school 

records for 1969 to 1977. Between 2009 and 2010, for 215 of these women we administered a 

questionnaire, including height and weight, and digitised at least one mammogram. Absolute and percent 

MD were measured using the computer-assisted method Cumulus. Preece-Baines growth curves were 

fitted and missing data imputed under a multivariate normal model. We used linear regression to assess 

the association between square root MD and covariates. 

Results: Both absolute and percent MD adjusted for age and current BMI were negatively associated with 

log BMI at each of ages 7 to 15, and positively associated with age at menarche. When fitted together, log 

BMI at age 15 remained negatively associated (p < 0.00001), explaining about 15% of the variance (cf. 

P24 



BACKGROUND PARENCHYMAL UPTAKE OF Tc-99m SESTAMIBI ON MOLECULAR 

BREAST IMAGING IN WOMEN WITH DENSE BREASTS 

Hruska CB 1 , Conners AL 1 , Jones KN 1 , Lingineni R 2 , Carter R 2 , Shah S 3 , Visscher D 3 , Rhodes DJ 4 , 

Vachon C 2 

1 Departments of Radiology, 2 Health Sciences Research, 3 Laboratory Medicine and 4 Pathology, and 

Medicine, Mayo Clinic, Rochester, MN 

Background: While evaluating molecular breast imaging (MBI) for adjunct screening in women with 

dense breasts, various presentations of background parenchymal uptake (BPU), or uptake of Tc-99m 

sestamibi in normal fibroglandular tissue (FT), were observed. We describe the prevalence of BPU in 

dense breasts and examine clinical and histologic risk factors associated with BPU. 

ABSTRACTS 

Methods: Screening MBI exams performed between April 2010 and March 2012 at Mayo Clinic in 

women with dense breasts (BI-RADS D2 - scattered densities; D3 - heterogeneously dense, or D4 - 

extremely dense) were reviewed. Participants with breast implants or cancer diagnosed at screening were 

excluded. BPU intensity was subjectively categorized by two radiologists as photopenic (uptake in FT < 

subcutaneous fat), mild (uptake in FT = fat), moderate (uptake in FT up to 2x fat), or marked (uptake in 

FT > 2x fat). Association of BPU with age, BI-RADS mammographic density, percent density measured 

by Cumulus, menopausal status, and use of hormonal medications was examined using Kruskal-Wallis 

and Chi-Square analyses. To examine association of histological factors with BPU, prospective coreneedle 

breast biopsy of 23 postmenopausal women was performed in regions of mammographically dense 

tissue demonstrating either photopenic (18 women) or marked (5 women) BPU. 

Results: In 1274 MBI exams, BPU was photopenic in 273 (21%), mild in 826 (65%), and moderate or 

marked in 175 (14%). Significant differences in distribution of BPU were observed with age, BI-RADS 

density, percent density, menopausal status, and postmenopausal hormone use (all p75% involuted). 

Conclusion: Moderate/marked BPU occurred more often in denser breasts and in women who were 

younger, pre- or perimenopausal, or using exogenous hormones. In each density category, substantial 

proportions of both moderate/marked and photopenic BPU were observed, establishing that similarappearing 

FT on mammography can demonstrate considerable differences in BPU on MBI. 

57



Photopenic 

Marked 

Figure 1. In these 3 patients, who were all postmenopausal 

and not using any exogenous hormones, the anatomic 

appearance of fibroglandular tissue is similar on digital 

mammography (top row). However, the background 

parenchymal uptake of Tc-99m sestamibi on MBI (bottom 

row) demonstrates differing behavior of the dense tissue, 

which may indicate differences in individual risk. 

Figure 2. Core biopsy specimens obtained from two 

postmenopausal patients – a 58 year-old woman with 

photopenic background uptake and a 62 year-old 

woman with marked background uptake on MBI. The 

tissue with marked uptake showed higher Ki-67 index, 

less lobular involution, and a greater proportion of 

epithelium versus stroma compared to the tissue with 

photopenic uptake. 

ABSTRACTS 

58

P25 



Impact of California Breast Density Notification Law SB 1538 on California Women and 

Their Health Care Providers 

Debra M. Ikeda, Jonathan Hargreaves, Elissa Price, Jafi Lipson, Bonnie N. Joe, Karen Lindfors, R. James 

Brenner, Stephen Feig, Lawrence Bassett, Jessica Leung, Haydee Ojeda-Fournier, Allison Kurian, Elyse 

Love, Donna Waldenbach, Bruce L. Daniel, Edward A. Sickles 

Objective: To describe the impact of California Breast Density Notification SB 1538 legislation on 

California medical facilities, with emphasis on development and implementation of policy 

ABSTRACTS 

Background: As of April 1, 2013, California State Senate Bill 1538 (SB 1538) (1) requires healthcare 

providers to send notification to all women identified with heterogeneously dense or extremely dense 

breast tissue on mammograms containing the following language: “Your mammogram shows that your 

breast tissue is dense. Dense tissue is common and is not abnormal. However, dense tissue can make it 

harder to evaluate the results of your mammogram and may also be associated with an increased risk of 

breast cancer. This information about the results of your mammogram is given to you to raise your 

awareness and to inform your conversations with your doctor. Together, you can decide which screening 

options are right for you. A report of your results was sent to your physician.” Women with non-dense 

tissue on mammograms receive no notification. While SB 1538 does provide limited information to 

patients, the bill provides no funding for supplemental screening, and no clear guidance on the 

implications of density for breast cancer risk or recommendations of when and what types of screening 

modalities should be recommended to patients. Figure 1 compares the screening decision tree in the pre- 

SB 1538 era (on the left) to the increasingly complex decision tree in the post-SB 1538 era (on the 

right). 

Connecticut legislation (Public Act 09–41) also mandated notification, but included funding for 

screening breast ultrasound (US). Following implementation of the bill, screening US uptake rose in 16% 

of eligible women (2), but was accompanied by high false positive (FP) imaging and biopsy rates with 

low positive predictive values (PPV) ranging from 5.6% to 6.7% for the 3-4 cancers/1000 women 

detected by US (2,3,4). US was shown in other studies to have a similarly low PPV of 8.8% (5). As 

indicated by the required text, the letter implies that having dense breast tissue: (a) increases breast cancer 

risk; (b) potentially “masks” cancers; and (c) may lead to doctors’ recommending supplemental screening 

tests. However, the information necessary to interpret these implications is missing, leaving patients and 

providers to ask: How much does breast cancer risk increase Who should undergo more screening How 

should breast cancer risk be calculated for an individual patient Prior to implementation of SB 1538, 

there was no institution-neutral, scientifically based website to guide women and healthcare providers 

regarding dense breast tissue notification legislation. The California Breast Density Information Group 

(CBDIG) formed to respond to California SB 1538 legislation, to review existing scientific evidence and 

identify gaps in information to provide scientifically-based recommendations for guiding health care 

providers and women regarding supplemental screening after mammography that identifies 

59



heterogeneously dense and dense breast tissue, and to formulate recommendations when no science was 

available to guide policy. 

Materials and Methods: The CBDIG task was to review existing scientific literature to provide 

evidence-based recommendations for guiding health care providers and women regarding supplemental 

screening after notification, and to formulate recommendations when no science was available to guide 

policy. Each facility then developed their own policy to respond to SB 1538, which was implemented in 

California on April 1, 2013. 

Results: After breast density notification, Figure 2: NCCN, ACS and ACR Guidelines CBDIG 

recommended women and health care for Breast Cancer Screening after Normal 

providers to consider supplemental breast Screening Mammography 

cancer 

screening based on breast cancer risk 

assessment (6) using family history models (7,8) 

based on recommendations from nationally 

High risk (> 20% lifetime) – MRI* 

Intermediate risk (15-20% lifetime ) – possible MRI** 

recognized guidelines (7,8,9) (Figure 2). Average Risk (

P26 



CHALLENGES IN DE-IDENTIFYING FILM-SCREEN MAMMOGRAMS PRIOR TO BREAST 

DENSITY ASSESSMENT FOR THE NOVICE PRACTITIONER 

J Jobling, C D’Este, JF Forbes 

School of Medicine and Public Health, University of Newcastle, NSW Australia 

Introduction: Measurement of breast density on mammograms presents many challenges. Film-screen 

mammograms require conversion to digital format prior to assessment with computer software, but 

contain burned-in areas of identifying information that should be removed to protect participant privacy. 

We are undertaking a retrospective linkage study in at least 4,000 women between the lower Hunter New 

England (NSW Australia) BreastScreen and the NSW 45 and Up Study to examine longitudinal changes 

in breast density and breast cancer risk. The BIRADS density categories are not routinely employed in 

Australia hence density will be assessed via the Cumulus software and/or visual methods. A simple, 

efficient and inexpensive method to de-identify thousands of film-screen mammograms is needed. 

Coverage of identifying areas on film with standard sticky labels during digitisation on an Array 2905HD 

laser scanner is not effective due to the strength of the laser. Our aim was to evaluate a range of software 

programs available for this purpose. 

ABSTRACTS 

Methods: Software capable of quickly and easily batch de-identifying digital images in DICOM format 

was sought via general internet searches using the terms ‘DICOM anonymizer burned in’; searches in 

PubMed using similar terms were also undertaken. Software was evaluated by: 1) its capacity to easily 

batch de-identify DICOM images with burned-in identifying areas (i.e. digitised film-screen 

mammograms), 2) the ability to easily remove DICOM header information from digital mammograms, 

and 3) retention of detail and compatibility with the Cumulus software post-processing. 

Results: More than 60 free DICOM viewers and 20 de-identifiers/anonymisers were identified 

(dclunie.com). Searches in PubMed found two useful, recent (



P27 ASSOCIATION BETWEEN AUTOMATED, VOLUMETRIC BREAST DENSITY 

MEASURES AND BREAST CANCER IN A LARGE SCREENING POPULATION 

M.G.J. Kallenberg 1,2 , K. Holland 1 , J.O.P. Wanders 3 , G.H. van Gils 3 , and N. Karssemeijer 1 

1 Radboud University Nijmegen Medical Centre, Nijmegen, the Neterlands; 2 Matakina Technology, 

Wellington, New Zealand, michiel.kallenberg@matakina.com; 3 Julius Center for Health Sciences and 

Primary Care, University Medical Centre Utrecht, the Netherlands 

PURPOSE: The association between breast density and breast cancer risk has been established with 

semi-automated, area based measurement of breast density. In this work we investigate the performance 

of a fully automated, volumetric method to predict breast cancer in a large screening population. 

MATERIALS AND METHOD: The data that was used in this work comprised 107,488 digital 

screening exams, performed in 2003-2012, from 53,975 women in the Dutch Breast Cancer Screening 

Program, with a mean age of 59.2 years. The set contained 730 biopsy proven malignant breast cancer 

cases that were diagnosed through the screening program (screen-detected). Interval tumors (diagnosed in 

between screening rounds) have not yet been identified for this screening period. For 363 cases only 

mammograms in which cancers were detected were available, while for 367 cases we also had prior 

screening mammograms. For the latter cases we selected the oldest mammographic examination 

available. The average time between the oldest mammographic examination and diagnosis was 3.5 years. 

All images were recorded on a Hologic Selenia FFDM system, using standard clinical settings. 

For each mammographic image we assessed volumetric breast density with commercially available 

software (Volpara, Matakina Technology, Wellington, New Zealand, version 1.4.0). To obtain one 

density estimate per exam we averaged over the MLO and CC view and over the left and right breast. 

When a case contained pathology we averaged only over the non affected side. We examined the 

relationship between volume of fibroglandular tissue and the risk of breast cancer by calculating odds 

ratios and their 95% confidence intervals. Exams were categorized into four groups, in such a way that 

the group sizes equal the group sizes one would obtain with Volpara density grade (VDG) scoring, which 

is a four point scale analog to BI-RADS density scores. We used logistic regression to adjust for age at 

mammography and breast volume, which served as a surrogate marker for BMI. 

RESULTS: After adjustment for age and breast volume, dense volume was significantly higher in cases 

than controls (p

P28 



LONGITUDINAL TRACKING WITH TIME OF MAMMOGRAPHIC DENSITY 

MEASURES THAT PREDICT BREAST CANCER 

Kavitha Krishnan 1,2 , Laura Baglietto 1,2 , Jennifer Stone 2 , Carmel Apicella 2 , Dallas R English 1,2 , Gianluca 

Severi 1,2 , Graham G Giles 1,2,3 , John Hopper 2 

1 Cancer Epidemiology Centre, Cancer Council Victoria, Melbourne, AUSTRALIA, 2 Centre for Molecular, 

Environmental, Genetic and Analytical epidemiology, University of Melbourne, AUSTRALIA, 

3 Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, AUSTRALIA 

Background: Mammographic density adjusted for age and body mass index (BMI) is an established risk 

factor for breast cancer. If density for age and BMI tracks strongly with time from a young age then it 

could be possible to identify young women at high risk of breast cancer and try to offer them more 

effective screening regimes. This might increase early detection of breast cancer and hence reduce 

mortality due to breast cancer. 

ABSTRACTS 

Methods: Combining both the Melbourne Collaborative Cohort Study (MCCS) and the Australian Breast 

Cancer Family Study (ABCFS), we identified 1,064 women who had an average of 4 mammograms each 

between the ages of 24-83 years (N=4,723 mammograms; range 1-14 mammograms per woman). 

Mammographic density was measured with the software Cumulus and transformed to approximate 

normality using the Box-Cox power formula. BMI at each mammogram was assigned using measures 

available at study entry and at two follow-ups for the MCCS and one follow-up for the ABCFS based on 

the proximity of the date at which the mammogram was taken and the attendance dates of the studies. 

Transformed dense area adjusted for age and BMI was fitted using mixed models with a random intercept, 

overall and separately for mammograms in the premenopausal age group 24-49 and postmenopausal age 

group 50-83, with no correlation between measurements, exponential correlation between measurements 

and random slope. Estimates of the correlations between dense areas between different ages were 

estimated from the best fit model identified using the log restricted-likelihood. 

Results: Dense area was transformed to the power of 0.2. The overall best fit model was mean dense area 

decreasing quadratically with age and linearly with BMI with an exponential correlation structure. The 

correlations in adjusted dense area measures 2, 4, 6, 8 and 10 years apart were 0.93, 0.90, 0.89, 0.88 and 

0.88, respectively. For mammograms in the premenopausal age group 24-49, the mean dense area 

decreased quadratically with age but there was no significant association with BMI. The correlations in 

adjusted dense area measures 2, 4, 6, 8 and 10 years apart were slightly lower at 0.85, 0.80, 0.79, 0.79 and 

0.78, respectively. The model for the postmenopausal age group was similar to the overall model and the 

correlations were also slightly higher than the overall model, being 0.94, 0.91, 0.90, 0.89 and 0.89 at 2, 4, 

6, 8 and 10 years apart, respectively. 

Conclusion: Mammographic dense area measurements that predict breast cancer risk are highly 

correlated over 10 years regardless of menopausal status. 

63

P29 



MULTI-VENDOR VALIDATION OF A FULLY-AUTOMATED BREAST DENSITY 

ESTIMATION METHOD APPLICABLE TO BOTH RAW AND PROCESSED DIGITAL 

MAMMOGRAMS 

Brad M. Keller*, Ph.D., Diane L. Nathan, M.D., Lee D. McDaniel, M.S., Nigel Bristol, A.S., Yan Wang, 

Ph.D., Yuanjie Zheng, Ph.D., James C. Gee, Ph.D., Emily F. Conant, M.D., Despina Kontos, Ph.D. 

Department of Radiology, Perelman School of Medicine, University of Pennsylvania 

Breast density is becoming increasingly recognized as an important biomarker of a woman’s breast tissue 

composition as it is known to affect both her individual risk for developing cancer as well as the 

sensitivity and specificity of screening mammography. In the United States, this has led to an increasing 

amount of legislation mandating that breast density be measured as part of routine clinical practice, 

communicated to the patient and potentially used to guide tailored screening and surveillance strategies. 

However, inter-reader variability in the measurement of breast density, particularly when using qualitative 

or semi-automated assessment strategies, is a known issue as variations in the interpretation of density 

could lead to potential differences in risk evaluation and any subsequent screening recommendations. 

Development of an automated system to measure breast density accurately and reproducibly can alleviate 

such issues, resulting in objective and reproducible quantitative measures. In order to be generalizable for 

use in both prospective clinical practice and retrospective research settings, such a system would ideally 

need to be able to estimate breast density from both raw (“For Processing”) and vendor post-processed 

(i.e., “For Presentation”) digital mammography images, as well as be validated on mammographic images 

derived from machines across multiple vendors. In this study, we demonstrate the multi-format, multivendor 

generalizability of a fully-automated breast percent density (PD%) estimation algorithm recently 

developed by our group. 

In this IRB-approved, HIPAA-compliant study, we used bilateral MLO full-field digital screening 

mammograms in both raw and post-processed format from a total 246 women to train and validate our 

previously developed adaptive fuzzy c-means density estimation computer algorithm. Data were available 

from two vendors: 81 studies were acquired using the Senographe Essential system (GE Healthcare); and 

165 studies were acquired using the Selenia Dimensions system (Hologic, Inc.). Leave-one-out cross 

validation was used to determine algorithm agreement with a radiologist’s manual estimation of breast 

PD% using the widely validated semi-automated Cumulus software on a per-woman basis. 

When training our density estimation software with GE Essential digital mammograms, the algorithm was 

able to achieve strong correlation between the automated density estimates and the radiologist’s manual 

percent density estimates for both raw (r=0.85, p



P30 GENETIC DETERMINANTS OF FIBROGLANDULAR BREAST TISSUE: 

IMPLICATIONS ON USING MAMMOGRAPHIC DENSITY MEASURES IN BREAST 

CANCER RISK ASSESSMENT 

D. Kontos 1* , B. Keller 1 , S. M. Domchek 2 , J. Chen 3 , E. Handorf 2 , M. Jones 2 , L. Boghossian 2 , K. 

Armstrong 2 , E. Conant 1 

University of Pennsylvania, Departments of Radiology 1 , Medicine 2 , and Epidemiology 3 

Breast density, typically measured as the percent area of mammographically dense tissue, is a strong risk 

factor for breast cancer. Currently, the biological determinants of the association between breast density 

and the risk of breast cancer are not yet fully understood. We investigate potential genetic determinants of 

breast density, by examining the association between new measures of fibroglandular tissue volume, 

versus the standard percent area density measures, and a series of validated single nucleotide 

polymorphisms (SNPs) associated with established breast cancer susceptibility loci. Bilateral two-view 

full-field digital mammography (FFDM) images were retrospectively analyzed under HIPAA and IRBapproval 

from 433 women (59.6% Caucasian, 40.4% African American, Age 53±7yrs, Gail Risk Lifetime 

Risk 10.4±4.4) with a negative screening exam who had results available from a validated genetic assay 

for breast cancer risk assessment (deCODE BreastCancer, deCODE genetics, Inc.). 

ABSTRACTS 

This genetic assay measures the allelic variation for 12 validated SNPs previously associated with breast 

cancer risk. Fibroglandular breast tissue volume, both in terms of relative (%) and absolute amount (cm 3 ), 

was measured on a per-breast basis using validated FDA-cleared automated software (Quantra TM , 

Hologic, Inc). Mammographic area percent density (PD%) was estimated with our previously validated 

computer algorithm based on automated intensity clustering and thresholding. The fibroglandular tissue 

measures were averaged between the left and right breasts for each woman to create corresponding 

measures on a per-woman basis. Associations between each SNP and the fibroglandular tissue measures 

were assessed with univariate linear regression. Model adjustments were considered for age, ethnicity, 

and Gail lifetime risk. Bonferroni correction was also applied to adjust for multiple comparisons across 

the different models. 

Statistically significant associations (p

P31 



FINE-MAPPING OF THREE MAMMOGRAPHIC DENSITY LOCI USING NEXT- 

GENERATION SEQUENCING 

Sara Lindström 1,2 , Brad Chapman 3 , Constance Chen 1,2 , Rory Kirchner 3 , Stacey Gabriel 4 , Oliver 

Hofmann 2 , Rulla Tamimi 2,5 , Peter Kraft 1,2,6 

1 

Program in Molecular and Genetic Epidemiology, Harvard School of Public Health, Boston, MA; 2 

Department of Epidemiology, Harvard School of Public Health, Boston, MA; 3 HSPH Bioinformatics 

core, Harvard School of Public Health, Boston, MA; 4 Genetic Analysis Platform and Program in Medical 

and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA; 5 Channing Division 

of Network Medicine, Harvard Medical School, Boston, MA; 6 Department of Biostatistics, Harvard 

School of Public Health, Boston, MA 

Introduction: Recent studies have shown that single nucleotide polymorphisms (SNPs) in the breast 

cancer susceptibility regions 6q25.1 (ESR1), 10q22 (ZNF365) and 14q24 (RAD51L1) are also associated 

with percent mammographic density, a strong risk factor for breast cancer. We used next-generation 

sequencing to characterize these regions and localize candidate causal variants. 

Methods: As a part of a large initiative to sequence known breast cancer susceptibility regions, we 

obtained high-coverage sequence data for 598 breast cancer cases and 560 controls from the Nurse’s 

Health Study for whom we also had data on percent mammographic density. Across all three regions, we 

sequenced a total of 1.93Mb and captured 80% of the targeted region at a sequencing depth >20x. For 

each region, we conducted conditional analysis as well as burden tests of rare variants to detect 

independent signals. We also used an approximate Bayesian approach to calculate the smallest number of 

SNPs needed to obtain a set (PPS) such that there is 90% probability that the causal SNP is included. 

Results: We identified a total of 10,274 single nucleotide variants across all three regions and of those, 

2,571 had a minor allele frequency (MAF) > 0.005. Conditional analysis on the original index SNPs 

revealed a suggestive independent signal in the ZNF365 region (P=0.0004). However, we were not able to 

narrow down the number of potentially causal SNPs. Burden tests did not reveal any excess of rare 

variants associated with mammographic density (P ESR1 =0.14, P ZNF365 =0.08, P RAD51L1 =0.66). 

Conclusions: We did not find any unambiguous evidence for potential causal variants in these three 

genetic regions associated with mammographic density. This study illustrates the challenges facing finemapping 

studies in the post-GWAS era. 

ABSTRACTS 

66



P32 APPLICATION OF SHAPE AND APPEARANCE MODEL APPROACH TO 

MAMMOGRAMS 

Serghei Malkov 1 , Karla Kerlikowske 2 John A. Shepherd 1 

1 Department of Radiology and Biomedical Imaging, University of California, San Francisco, 1 Irving 

Street, Suite AC108, San Francisco, CA 94143-0628 ; 2 Departments of Medicine and 

Epidemiology/Biostatistics, University of California, San Francisco, 1635 Divisadero Street, Suite 600, 

San Francisco, CA 94115 

Abstract: The extraction of new information from clinical breast mammograms could help to improve 

breast cancer risk assessment techniques. The image shape and texture estimation (appearance) designed 

for face recognition seems to be promising approach for application in breast imaging. 

Purpose: The purpose of this study is to apply a shape and appearance model approach to digital 

mammograms. Potentially, the breast shape and appearance model parameters could give new information 

valuable for breast density estimation, breast cancer risk assessment and diagnostics. 

ABSTRACTS 

Materials and Methods: We built a shape and appearance model using 200 full field digital 

mammograms with calculated percent fibroglandular volumes (%FGV) from San Francisco 

Mammography Registry. The model approach was implemented according to the method of Cootes et al 

[1]. To build the shape model, we used the edge and grid point coordinates inside of the breast area. A 

Procrustes transformation was used to remove rotation, translation and scale. A Principal Component 

Analysis (PCA) was applied to extract significant and uncorrelated components. In order to build an 

appearance model, first, we transformed the breast images into the mean texture image by piecewise 

linear image transformation. That step aligned all texture information inside the reference mean image. 

Then, the image pixels grey-scale values are converted into a set of significant principal component 

vectors. Finally, the shape and texture feature sets of each image are created using principal component 

vectors. In addition, to remove shape and texture correlations another PCA transform was applied to get a 

combined shape-appearance model. The stepwise regression with forward selection and backward 

elimination was used to estimate the outcome %FGV with shape and appearance features and other 

parameters including age, bmi, thickness, mAs and force. 

Results: We obtained the maps and pixel distributions of different shape and appearance PCA 

components. The shape and appearance scores are found to correlate moderately to breast %FGV, dense 

tissue and actual volumes, bmi and age. The highest Pearson correlation coefficient is equal 0.76 for the 

first shape PCA component and actual breast volume. The stepwise regression of the outcome %FGV 

from shape and appearance variables, thickness, age, mAs and force, met the 0.01 significance level for 

entry into the model during forward selection, demonstrated R-square around 0.8. Eleven significant 

variables were selected by regression among 185 parameters. 

Conclusion: We applied the shape and appearance model approach to mammograms and demonstrated its 

feasibility to extract a new set of variables. Further exploring and testing of this approach is necessary. 

References: 

T.F. Cootes, G.J Edwards, and C,J. Taylor "Active Appearance Models", IEEE Transactions on Pattern 

Analysis and Machine Intelligence 2001 

67



P33 

METHODS TO ASSESS AND REPRESENT MAMMOGRAPHIC DENSITY: AN 

ANALYSIS OF FOUR CASE-CONTROL STUDIES 

Maskarinec G 1 , Woolcott CG 2 , Conroy SM 3 , Nagata C 4 , Ursin G 5 , Vachon CM 6 , Yaffe MJ 7 , Byrne C 8 

1 University of Hawaii Cancer Center, Honolulu, HI ; 2 Dalhousie University, Halifax, Nova Scotia, Canada ; 

3 Alberta Health Services-Cancer Care, Calgary, Canada ; 4 Gifu University Graduate School of Medicine, 

Gifu, Japan ; 5 University of Oslo, Oslo, Norway ; 6 Mayo Clinic, Rochester, Minnesota ; 7 Sunnybrook 

Research Institute, Toronto, Ontario, Canada ; 8 Uniformed Services University, Bethesda, MD 

To achieve greater statistical power in studies of mammographic densities and breast cancer risk, it is 

often desirable to combine data from multiple studies. This is not straightforward due to variation in 

mammographic density assessment arising from different techniques and observers. Using data from a 

pooled data set of 4 case-control studies, we compared the measures of mammographic densities made by 

the original observers and readings by a single observer who re-assessed all images. In addition, we 

identified predictors of differences in density readings and evaluated the strength of the association 

between mammographic density and breast cancer risk using the original percent density values, the reassessed 

readings, and original values calibrated based on their relations with re-assessed measurements 

on a subset of mammograms. 

The pooled data set included 1699 cases and 2422 controls from California, Hawaii, Minnesota, and 

Japan. One reader re-assessed all mammographic images for density using Cumulus software. To calibrate 

the original readings using reassessed values, we performed linear regression on 100 randomly selected 

mammograms for each location and applied the regression equation to all of the original density 

measurements from that location. The association between mammographic density and breast cancer risk 

was represented by odds ratios (OR) estimated from unconditional logistic regression models and 

adjusted for relevant covariates. Different representations of density values were used in these analyses: 

the approximate tertiles in the combined control group (35%); 5 predefined categories 

(

P34 


INTERNATIONAL POOLING PROJECT OF MAMMOGRAPHIC DENSITY 


Anya Burton 1 , Joachim Schuz 1 , Isabelle Romieu 1 , Norman Boyd 3 , Isabel dos Santos Silva 1, 2 , Valerie 

McCormack 1 

1 <strong>International</strong> Agency for Research on Cancer, Lyon, France; 2 London School of Hygiene and Tropical 

Medicine, London, England; 3 Ontario Cancer Institute / Princess Margaret Hospital, Toronto, Canada 

Background: The <strong>International</strong> Pooling Project of Mammographic Density is a worldwide collaborative 

project which aims to: 

(i) pool and obtain standardised comparable data on mammographic density (MD) from countries 

spanning the full breast cancer (BC) incidence range 

(ii) describe international variations in overall and age-specific distributions of MD 

(iii) assess whether variations in MD are explained by individual-level risk factors for this marker 

(iv) quantify the extent to which international variations in MD correlate with corresponding BC 

incidence rates and Pike’s model of breast tissue ageing. 

ABSTRACTS 

Methods: The project will pool original data from previously conducted studies worldwide. Eligible 

studies are those based on general population asymptomatic women with original analogue (ideally 

digitized) or digital films and breast cancer risk factor information for each woman. To increase betweenpopulation 

heterogeneity, studies spanning low to high BC risk populations (e.g. multi-ethnic studies) 

will be especially informative. From each population, data and anonymized films from 200 premenopausal 

(ages 35-49 years) and 200 post-menopausal women (50-70 years) will be requested and sent 

to the study coordinators at the <strong>International</strong> Agency for Research on Cancer (IARC). All films will be 

re-read using Cumulus or other validated methods, so that standardised comparable MD is obtained 

across studies. A validation study of MD readings from digitised analogue, raw digital and processed 

digital images is being conducted to inform whether information from these images can be combined. 

Progress to date: Currently, 20 studies from 6 continents have agreed to participate in the project, 

including North America, Northern and Southern Europe, South Africa, Turkey, India, Saudi Arabia, 

Egypt, Singapore, Brazil and Mexico. Further low risk populations would enhance the scientific value of 

the project. IARC is liaising with participating studies to put agreements in place, anonymize images and 

data prior to their transfers. 

What we hope to achieve: This international perspective will investigate to what extent MD is an 

intermediate marker of BC risk at the population-level. It will examine factors that account for betweenpopulation 

differences in MD, i.e. target factors that could be modified to reduce MD and BC risk. 

Greater exposure variability will provide greater statistical power to clarify their influence on MD. We 

will assess age-specific variations in MD at ages 35-70 years, identifying critical exposure windows. The 

debate over which MD measure (absolute/relative, recent/cumulative) is the best indicator of BC risk will 

be addressed, an important issue for personalised risk assessment incorporating MD. 

Funding: Office of Research on Women’s Health (ORWH), National Cancer Institute 1R03CA167771-01. 

69

P35 



AN APPROACH TO THE STANDARDIZATION OF PERCENT MAMMOGRAPHIC 

DENSITY TO ENABLE JOINT ANALYSIS OF DATA ACROSS MULTIPLE CENTERS 

V. Shane Pankratz 1 , Christopher G. Scott 1 , Matthew R. Jensen 1 , Kimberly A. Bertrand 2,3 , Rulla M. 

Tamimi 2,3 , Karla Kerlikowske 4 , Celine M. Vachon 5 

1 Department of Health Sciences Research, Division of Biomedical Statistics and Informatics, Mayo Clinic 

College of Medicine, Rochester, MN; 2 Channing Division of Network Medicine, Department of Medicine, 

Brigham and Women’s Hospital and Harvard Medical School, Boston, MA; 3 Department of 

Epidemiology, Harvard School of Public Health, Boston, MA; 4 Departments of Epidemiology and 

Biostatistics and General Internal Medicine Section, Department of Veterans Affairs, University of 

California, San Francisco, CA; 5 Department of Health Sciences Research, Division of Epidemiology, 

Mayo Clinic College of Medicine, Rochester, MN 

Background: Percent mammographic density (MD) is one of the strongest risk factors for breast cancer 

(BC). Because MD is often measured by a computer-assisted approach which requires human operators to 

set thresholds, MD measures may differ when they are assessed by different readers, or even within a 

single reader when measurements for a cohort are separated by long periods of time. This random 

variability in MD measurements across readers and “batches” introduces measurement error in 

epidemiologic analyses and may bias associations of interest. In order to integrate MD data obtained from 

multiple readers across several reading epochs into a single pooled analysis, we developed an approach 

that standardizes measures of MD across multiple readers and batches. 

Methods: Data were obtained from six cohort or case-control studies, which were comprised of 3414 

women with breast cancer and 7199 without. All underwent screening mammography and MD was 

assessed from digitized film-screen mammograms using a computer-assisted threshold technique by three 

different readers and across varying time frames. We developed a standardization approach that relies on 

the age-dependent trends in MD. In this approach, we regress a transformed version of MD on age at 

mammogram within each discrete set of MD reads, and extract the residuals from these regression 

models. We re-center and re-scale these sets of residuals separately, such that their medians and 

interquartile ranges were all equal to values estimated from a large set of MD reads obtained within one 

reading epoch within a single cohort of individuals. We then add these standardized residuals to an age 

trend that we estimated from all currently available data. We applied this standardization approach to the 

data from these six studies and evaluated its effect on estimates of heterogeneity among the studies. 

Results: Prior to standardization, differences in the MD reads among the six studies explained 13.6% of 

the variability in percent MD, and we consistently observed evidence of significant heterogeneity among 

studies when analyzing associations between MD and various outcomes of interest, including breast 

cancer. We applied our standardization approach to the data from these studies to re-center and re-scale 

their percent MD measurements. The effort resulted in distributions of percent MD that were more similar 

across studies, while the rank ordering of within-study measurements was preserved. After 

standardization, differences among studies explained 1.4% of the variability in MD, and there was no 

longer evidence of significant heterogeneity among study sets in combined analyses of associations 

between percent MD and breast cancer status. 

ABSTRACTS 

Conclusion: We implemented a standardization approach for MD that makes it possible to appropriately 

combine MD measurements obtained across multiple readers. This approach may also be used to align 

MD measurements obtained from studies other than those explicitly included in these analyses. 

70

P36 



BREAST DEVELOPMENT AT DIFFERENT BREAST TANNER STAGES: EVIDENCE 

FROM THE GROWTH OBESITY CHILEAN COHORT STUDY 

Ana Pereira 1 , John Shepherd 2 , María Luisa Garmendia 1 , Ricardo Uauy 1 , Camila Corvalan 1 

1 Institute of Nutrition and Food Technology, University of Chile; 2 Department of Radiology and 

Biomedical Imaging, University of California at San Francisco 

Background: Understanding breast development and composition at young ages is crucial to further 

investigate early determinants of breast cancer risk. A previous study carried out using Dual X-Ray 

Absorptiometry(DXA) in adolescents Hawaiian girls showed that absolute fibroglandular volume 

(AFGV) increased mostly until Tanner B4, while between B4 and B5 the most important increase was in 

breast volume (BV) rather than AFGV, thus % fibroglandular volume (%FGV) decreased. 

Objective: To describe breast composition (%FGV, AFGV, BV) at different Tanner stages in Chilean 

Girls and as a by-product to evaluate whether B4-B5 breast composition changes vary by menarche or 

adiposity. 

ABSTRACTS 

Methods: This cross-sectional study is part of ongoing birth cohort (2002-2003) of 1196 Chilean 

children (50% female). Biannual anthropometric measurements and breast Tanner assessment are being 

carried out by trained personnel. Breast composition was assessed using DXA in a randomly selected subsample 

of girls in Tanner B1 (n=6), B2 (n=7) and B3 (N=6, and in all girls that reached B4 (n=55) and B5 

(n=13) during 2012. Breast composition data was calculated based on a two-compartment model of 

adipose and fibro-glandular tissue using a software developed by University of California at San 

Francisco; test-retest precision was 3.5%. DXA scans of each breast were analyzed separately (although 

correlation was taking into account) according to their tanner stage. We used descriptive statistics to 

describe progression of breast composition across all breast tanner stages, and lineal multivariate analysis 

to assess if changes between B4 and B5 vary by menarche and body mass index 

(BMI=weight(kg)/height(mt) 2 ). 

Results preliminary: Descriptive results are shown in Table 1. In summary, we observed that mean age, 

%FGV, AFGV, and BV increased across breast tanner stages. BV, AFGV and %FGV increased by 47%, 

68% and 11% from B4 to B5 respectively. In our linear regression models, we observed that changes 

between B4 and B5 disappeared or diminished after adjusting by menarche status; there were not 

statistical differences in %FGV and BV and the regression coefficient of AFGV diminished by half, but it 

remained significantly (β=28.3, 95%CI:4.7; 51.9). Further, adjusting by BMI did not modify the 

regression coefficient between B4 and B5; however BMI was inversely associated with %FGV and 

positively with BV. 

Discussion: We observed lower levels of AFGV and %FGV (all Tanner) and BV (B4 and B5) compared 

to previous studies. AFGV, % FGV, BV increased across all breast Tanner stages including B5; however 

our results suggest that measurements at time of menarche will be more critical to assess breast 

development. Follow-up of the remaining subjects in the cohort will help to establish the consistency of 

these findings. 

Table 1: Descriptive analysis of age and breast composition data stratified by breast Tanner stage 

Breast Tanner Stage N Age (mean, (sd)) %FGV (%) AFGV (cm3) BV (cm3) 

B1 15 121 (3.0) 30.1 22.5 77.6 

B2 12 118 (3.6) 22.5 26.5 125.4 

B3 12 124 (5.0) 36.5 80.1 251.8 

B4 111 124 (4.0) 39.9 89.2 237.9 

B5 26 127 (5.0) 44.4 150.3 348.6 

Funding Sources: Fondecyt 3130532 , Fondecyt 1020326 and WCRF 2010/245 

71

P37 



DUAL X-RAY ABSORPTIOMETRY AN ALTERNATIVE METHOD TO MEASURE 

VOLUMETRIC BREAST DENSITY 

Pereira A 1 , Shepherd J 2 , Garmendia ML 1 , Galleguillos B 1 , Ralph Highnam 3, Uauy R 1 , Corvalán C 1 . 

1 Institute of Nutrition and Food Technology, University of Chile; 2 Department of Radiology and 

Biomedical Imaging, University of California at San Francisco; 3 Mātakina Technology Limited, 

Wellington, New Zealand 

Background: Mammographic breast density (BD) is a strong risk factor for breast cancer; however its 

use in adolescent populations is limited due to radiation exposure. Recently, applications to measure BD 

have been developed for Dual X-Ray Absorptiometry (DXA) systems as a low-dose alternative method. A 

previous validation study carried out in ~100 adult women showed a high correlation (r=0.70) between 

volumetric breast density (%FGV) measured by DXA and area based mammographic BD. Since 2010, we 

have followed the mothers of 550 girls participating in the Growth and Obesity Chilean Cohort Study to 

assess nutritional determinants of increased BD. In these women, both mammograms and DXA scans of 

the breasts were obtained. Our aims were i) to determine the precision of %FGV measured by DXA, and 

ii) to assess the equivalency of %FGV, absolute fibroglandular volume (AFGV), and breast volume (BV) 

measures made using DXA and mammograms. 

Methods: In this cross-sectional study, 126 premenopausal women received both screening digital 

mammograms (Hologic Selenia, Bedford, MA, USA) and breast DXA scans (GE iDXA, GE healthcare, 

Madison, WI). Mammographic “raw” images were used to measure volumetric BD using a fullyautomated 

commercial method (Volpara®, Matakina Technology, Wellington, New Zealand). For the 

DXA scans, a left-right-left breast scanning protocol was used. The DXA analysis software was 

developed by University of California at San Francisco. The end result was measures of %FGV, AFGV, 

and BV by both mammography (Mx) and DXA. In addition, all women underwent anthropometric 

measures (body mass index (BMI)=weight(kg)/height(mt) 2 ) and answered a sociodemographic 

questionnaire. We estimated intraclass correlation coefficient (ICC) and the coefficient of variation 

(precision) of DXA after repositioning, considering obesity (BMI>30), age (over 36 years) and BV (less 

than 654 cm 3 ). Using linear regression models we estimated the correlation coefficient and R 2 between 

volumetric BD measured with DXA (left image) and with Mx (MLO left image). 

Results: The mean age was 37.1 years (sd=6.5) and 31.6% were obese. Mean time between 

mammographic and DXA measurements was 9 months (sd=5.6). The ICCs for %FGV, AFGV and BV 

after reposition the left breast were over 0.9 and the precision for %FGV, 3.7%; however the latter 

declined to 5% in obese women and women with breast volumes less than 654 cm 3 . Mean values for 

DXA and Mx are shown in Table 1. The correlation coefficients for %FGV, AFGV and BV between DXA 

and Mx were all over 0.8 and the R 2 were 0.6, 0.7 and 0.8, respectively. The R 2 of %FGV increased to 0.8 

if the time between both exams was less than six months. 

ABSTRACTS 

Discussion: DXA is a precise method to measure volumetric BD and it correlates better to volumetric 

mammography measurements compared to area based models. These results suggest that DXA is a 

reliable method to measure volumetric BD that can be used to continuously monitor BD in adult women 

in follow-up studies 

Table 1: Mean and standard deviation(sd) of breast composition using DXA and Mx 

Breast Composition Data DXA (mean(sd)) Mx (mean(sd)) 

%FGV(%) 30.9(10.5) 10.2(5.2) 

AFGV(cm 3 ) 214.4(98.8) 67.6(34.7) 

BV(cm 3 ) 732.8(315.8 767.9(415.6) 

Funding sources: Fondecyt 3130532 and 11100238, WCRF 2010/245 , Ellison Medical Foundation Grant to Ana Pereira 

72

P38 



AUTOMATED DENSITY RISK SCORING WITH LEARNED HIERARCHICAL 

FEATURES 

K Petersen 1 , M Lillholm 2 , P Diao 2 , J Marques 2 , N Karssemeijer 3 , M Nielsen 1,2 

1 University of Copenhagen, Dept. of Comp. Science; 2 Biomediq A/S; 3 Radboud University, Nijmegen 

Medical Centre 

Purpose: Radiodense fibroglandular tissue is an established risk factor for breast cancer. Density 

estimation is commonly performed using the interactive threshold software Cumulus, which measures the 

percental area of dense breast tissue in a mammogram. However, for large studies, this method is too 

laborious and expensive. We propose a novel machine learning based method called multiscale 

convolutional neural network (MSCNN) for scoring dense breast tissue. 

ABSTRACTS 

Material and Methods: The density scores were evaluated on digitized film-based mammograms from 

the Dutch screening program (age 58 ± 5.7 years), consisting of the left and right mediolateral oblique 

views (LMLO and RMLO) of 495 women (123 interval cancers, 122 screen-detected cancers, and 250 

controls). The mammograms were taken 2-4 years prior to diagnosis of interval cancers and 4 years prior 

to screen-detected cancer. The MSCNN was trained on labeled RMLO views, for which density labels 

from expert Cumulus scoring were available. Testing was performed on both LMLO and RMLO views, 

i.e., 990 mammograms in total. To score all views, two-fold cross-validation was employed. MSCNNs are 

multiscale multi-layer networks for learning a deep hierarchy of discriminative features. These features 

are learned directly from raw intensity values of the mammograms, and used for discriminating dense 

from non-dense tissues using a multinomial logistic regression. MSCNNs allow to efficiently learn 

features in a data-driven fashion by sharing parameters and modeling the topology of images. They 

circumvent the modeling and selection of handcrafted density features. 

Results: MSCNN scores for RMLO (R1) and LMLO (L1) were compared to the expert's Cumulus scores 

on RMLO (R2) by evaluating the area under the ROC curve (AUC), the p-value of a ranksum test, and 

Pearson's R to R2. R1 performed very similar to R2 in separating interval cancers from controls (R1: 

AUC=0.60, p=5.3e-3 / R2: AUC=0.60, p=2.6e-3) and in discriminating all cancers from controls (R1: 

AUC=0.56, p=4.1e-2 / R2: AUC=0.56, p=2.7e-2). The separation of screen cancers was non-significant 

for R1 and R2. Even L1 (which was trained on RMLO only) performed comparable for interval cancers 

(L1: AUC=0.58, p=1.9e-2). For screen cancers and all cancers the discrimination based on L1 was nonsignificant. 

The correlation between all cancers and control scores was R=0.79 for R1 vs. R2 and R=0.75 

for L1 vs. R2. 

Conclusion: Automated MSCNN density scores based on RMLO views (R1) performed very similar to 

expert's scores (R2) in predicting breast cancer and showed a strong correlation to R2. MSCNN scores for 

LMLO views were well correlated with R2, and were predictive for interval cancers. 

73



P39 ASSURE - ADAPTING BREAST CANCER SCREENING STRATEGY USING 

PERSONALISED RISK ESTIMATION 

Bram Platel, Ritse Mann, Nico Karssemeijer 

Radboud University Nijmegen Medical Centre, The Netherlands 

ASSURE consists of 10 project partners from 7 countries with leading expertise in the field of breast 

imaging, with the Radboud University Nijmegen Medical Centre as the coordinating partner. The project 

started in December 2012 and is supported by the European Commission under the 7th Framework 

Programme. 

Approximately 1 in 8 women develop breast cancer during their lifetime. Screening programs have been 

introduced, decreasing the mortality rate and allowing for less radical treatment options for early detected 

cancers. Unfortunately not all cancers are detected in screening. Approximately 30% of breast cancers are 

detected between screening rounds. This constitutes a need for improved cancer screening. 

The ASSURE project promotes going from a one-size-fits-all approach to a personalised screening 

protocol. Nowadays all women undergo the same screening protocol, independent of the density of their 

breast tissue and the risk to develop breast cancer. Almost all women make use of the same diagnostic 

modality, X-ray mammography. 

Unfortunately the sensitivity of mammographic screening is seriously impaired in women with dense 

breasts. Fibroglandular and stromal tissue look equally bright as tumours on mammography images, this 

causes tumours to remain masked for radiologists and thus breast cancer to remain undetected. 

One task of the project is to estimate personal risk depending on breast density, age, gene mutations, 

family and/or personal history, etc., and based on this risk, propose an optimal, cost effective, 

personalized screening strategy. 

Another task is to improve the use of MRI and Automatic Breast UltraSound (ABUS) as additional breast 

cancer screening modalities, by optimizing protocols, enhance reading workflow, and by offering better 

diagnostic performance through software solutions. 

Expected Results & Impact 

Effective breast cancer screening substantially reduces mortality and maintains the quality of life of 

women. Additionally, less disfiguring treatments are required. A new stratification protocol and new 

screening tools will result from this project. The screening process will be optimised in response to the 

increased awareness regarding the low sensitivity of current breast cancer screening in women with dense 

breasts. Our estimations show that a reduction of interval cancers of at least 30% is achievable, assuming 

the efforts in this project lead to a sensitivity for dense breast comparable to that of non-dense breasts. 

Furthermore, the risk stratification enabled by this project will benefit a very low risk group (10-15% of 

women) as their screening interval can be further optimized (and increased) to limit the adverse effects 

due to the radiation inherent with X-ray mammography screening. 

ABSTRACTS 

The ASSURE project will be an excellent opportunity for the participating SMEs (small and mediumsized 

enterprises) to join forces with leading research institutions and national screening experts. 

Exploitable results from this project are expected to include (1) new and validated measures for breast 

density, (2) a quality assurance system for breast cancer screening with ABUS and MRI, (3) new 

sequences for breast cancer screening with MRI and (4) dedicated screening software for high-throughput 

ABUS and MRI reading. 

74

P40 



BREAST DENSITY INFORM: NEW YORK STATE AND FEDERAL INITIATIVES 

J o A n n P u s h k i n; CoFounder, D.E.N.S.E.; Founder, D.E.N.S.E. NY; (Density Education National 

Survivors' Effort) 

Introduction: Breast density compromises the effectiveness of a mammogram and is recognized as an 

independent risk factor for breast cancer ii . These facts are especially alarming as 40% iii of women of 

mammography age have dense breasts and 95% iv of women have no knowledge of their own breast 

density. However, as recently as four years ago breast density disclosure was not required through state 

or federal law. Based on the ultrasound “find rate” in Connecticut, the first state to pass a density inform 

law in 2009, it is estimated that each year at least 2,500 New York State women with dense breasts 

receive “normal/negative” mammogram results - but actually have invasive breast cancer that would be 

found by ultrasound screening. 

Purpose: To provide NYS women information about their own breast density, I sought the introduction of 

the NYS Breast Density Inform bill and cofounded D.E.N.S.E. (TM) (Density Education National 

Survivors’ Effort) to support legislation. 

ABSTRACTS 

Method: Advocacy included: initiating bill introduction, providing input on bill language, responding to 

opposition concerns, building a coalition of radiological experts, advocates and supportive organizations, 

meeting with Governor Cuomo’s health counsel, organizing a capital Lobby Day and reaching out to NYS 

legislature. 

Result: On July 23, 2012, New York’s Breast Density Inform Bill was signed into law by Governor 

Cuomo having received unanimous bi-partisan support. NY became the first state to unambiguously tell 

women if they have dense breasts. NYS law requires a woman with dense tissue (>50% density) be 

informed: 

1. Her mammogram shows that her breast tissue is dense 

2. Dense tissue can compromise a mammogram 

3. Dense tissue may be associated with an increased risk of breast cancer 

4. To discuss the value of additional screening with her doctor 

As a result, New York women are now aware about their breast density and its associated risks. 

Multi-state Status: Six states v have mandatory Breast Density Inform Laws, each with a different density 

notification. Further almost half the country has density legislation pending. As a result, women living in 

neighboring states may get very different density notifications. 

Federal Solution: A national standardized Breast Density Inform notification is necessary to rectify this 

growing inequity. For this reason an amendment to the Federal Mammography Quality Standards Act to 

require the inclusion of breast density in the letter women receive after their mammogram, and a Federal 

Breast Density Inform Bill have been initiated, the latter of which has 45 co-sponsors and is scheduled for 

a 2013 re-introduction to the House. 

Through intensive advocacy work and with bi-partisan support it is possible to enact breast density inform 

legislation and make breast density awareness a national priority. 

75

P41 



CIRCULATING INSULIN-LIKE GROWTH FACTOR-1, INSULIN-LIKE GROWTH 

FACTOR BINDING PROTEIN-3, GENETIC POLYMORPHISMS, AND 

MAMMOGRAPHIC DENSITY IN PREMENOPAUSAL MEXICAN WOMEN: RESULTS 

FROM THE ESMAESTRAS COHORT 

Sabina Rinaldi 1 , Carine Biessy 1 , M Hernandez 1 , F Lesueur 1,3 , Isabel dos Santos Silva 4 , Megan S Rice 5,6 , 

Martin Lajous 2 , RuyLopez-Ridaura 2 , Gabriela Torres-Mejia 2 , Isabelle Romieu 1 

1 <strong>International</strong> Agency for Research on Cancer (IARC), Lyon, France; 2 Instituto Nacional de Salud 

Publica, Cuernavaca, Mexico; 3 INSERM, U900, Institut Curie, Paris, France; 4 London School of 

Hygiene & Tropical Medicine, London, United Kingdom; 5 Department of Epidemiology, Harvard School 

of Public Health, Boston, MA, USA; 6 Channing Division of Network Medicine, Brigham & Women’s 

Hospital, Boston, MA, USA 

Background: The insulin-like growth factor (IGF) axis plays an essential role in the development of the 

mammary gland. Higher circulating levels of IGF-I have been associated with an increased risk of breast 

cancer. However, results from studies of IGF-1 and mammographic density have been inconsistent. 

Methods: We conducted a cross-sectional analysis among 593 premenopausal Mexican women in the 

ESMaestras cohort study to examine the associations between serum IGF-I, IGFBP-3, and the IGF- 

1/IGFBP-3 ratio and mammographic density. Our initial models adjusted for age at mammogram, IGF- 

1/IGFBP-3 batch, state (Jalisco/Veracruz), family history of breast cancer (no, yes), history of benign 

breast disease (no, yes), age at menarche (

P42 



ADJUSTING FOR INTER-OBSERVER VARIATION IN VISUAL ASSESSMENT OF 

PERCENTAGE BREAST DENSITY ON A CONTINUOUS SCALE AND DETERMINING 

WOMEN AT HIGH RISK OF DEVELOPING BREAST CANCER RISK 

Jamie C. Sergeant 1 , Matthew Sperrin 2 , Lawrence Bardwell 3 , Iain Buchan 2 , D. Gareth Evans 4 , Anthony 

Howell 4 , Susan M. Astley 1 

1 Centre for Imaging Sciences, Institute for Population Health, University of Manchester, Manchester, UK; 

2 Centre for Health Informatics, Institute for Population Health, University of Manchester, Manchester, 

UK; 

3 Department of Mathematics and Statistics, University of Lancaster, Lancaster, UK 

4 Nightingale Centre and Genesis Prevention Centre, University Hospital of South Manchester, 

Manchester, UK 

ABSTRACTS 

Aim: Assessment of percentage breast density on a continuous visual analogue scale (VAS) is a method 

strongly associated with risk of developing breast cancer and readily applicable in breast screening 

programms due to its simplicity. However, it is subject to inter-observer variation which may affect 

patient outcomes if density is used in risk stratification. We present a novel method of adjusting 

observers’ disparate VAS breast density estimates to make them directly comparable and we examine the 

effect of applying our method in a clinical context where women may be classified as being at high risk of 

developing breast cancer. 

Method: Our approach to adjusting observers’ individual estimates of breast density consists of two 

stages. Firstly, all observers are transformed onto a common distribution. Secondly, we correct for 

different observers having seen different case mixes. We apply our method to a subset of data from the 

Predicting Risk Of Cancer At Screening (PROCAS) study in which women are classified as being at high 

risk of developing breast cancer if their 10-year risk, computed using the Tyrer-Cuzick risk model, is at 

least 8% (following UK National Institute for Health and Care Excellence (NICE) guidelines), or if their 

Tyrer-Cuzick 10 year risk is at least 5% and their VAS breast density places them in the top decile of 

densities. Consenting high risk women are informed of their status and offered a consultation with a 

clinician to discuss their risk. In the current substudy a total of 13,694 screening cases with Full-Field 

Digital Mammography (FFDM) images acquired on GE Senographe Essential systems were assessed by 

13 experienced mammographic readers. The number of women classified as high risk before and after the 

application of our method of adjustment was determined. 

Results: Of the 13,694 women, 165 (1.2%) were classified as high risk due to having a Tyrer-Cuzick 10- 

year breast cancer risk of at least 8%. In addition, 1,125 (8.2%) of the women had a Tyrer-Cuzick risk of 

at least 5% but less than 8%, making them potentially high risk, depending on their VAS density. Before 

adjustment of densities, 126 (11.2%) of the 1,125 women were classified as high risk, which increased to 

147 (13.1%) after the densities had been adjusted. After adjustment, 35 women had been reclassified from 

non-high risk to high risk (3.5% of those initially classified as non-high risk) and 14 women had been 

reclassified from high risk to non-high risk (11.1% of those initially classified as high risk). 

Conclusion: We have presented a novel adjustment of VAS breast density estimates to account for interobserver 

variation. Applying our method on data from the PROCAS study had a substantial impact on 

identifying high risk women based on their Tyrer-Cuzick 10-year risk estimate and breast density. The 

reclassification of women as high or non-high risk after adjustment highlights the need to consider 

correcting for inter-observer variation if VAS breast density assessment is used in assessing breast cancer 

risk for the purposes of stratification and personalized screening. 

77

P43 



VISUAL ANALOGUE SCALE ASSESSMENT OF PERCENTAGE BREAST DENSITY: A 

STUDY OF AGREEMENT BETWEEN OBSERVERS AND WITH INTERACTIVE 

THRESHOLDING 

Jamie C. Sergeant 1 , Mary Wilson 2 , Nicky Barr 2 , Ursula Beetles 2 , Caroline Boggis 2 , Sara Bundred 2 , Megan 

Bydder 2 , Soujanya Gadde 2 , Emma Hurley 2 , Anil Jain 2 , Yit Lim 2 , Liz Lord 2 , Valerie Reece 2 , D. Gareth 

Evans 2 , Anthony Howell 2 , Susan M. Astley 1 

1 Centre for Imaging Sciences, Institute for Population Health, University of Manchester, Manchester, UK: 

2 Nightingale Centre and Genesis Prevention Centre, University Hospital of South Manchester, 

Manchester, UK 

Aim: The measurement of percentage breast density on a visual analogue scale (VAS) has been shown to 

have an association with breast cancer risk comparable to that of interactive thresholding software such as 

Cumulus (Sunnybrook Health Sciences Centre, Toronto, Canada). While interactive thresholding is too 

labour intensive to implement in a breast screening programme for the purposes of prospective risk 

estimation, the quick and simple VAS method is more well-suited to this application. However, for VAS 

density measurement to be satisfactorily used for this purpose, there must be comparability between 

estimates produced by different observers. We examine the agreement between a group of observers 

assessing VAS breast density and their agreement with Cumulus. 

Method: 120 screening cases were sampled from the Predicting Risk Of Cancer At Screening 

(PROCAS) study, a large clinical study based in Manchester, UK, which aims to predict the risk of breast 

cancer when women attend routine breast screening. The cases all had Full-Field Digital Mammography 

(FFDM) images acquired on GE Senographe Essential equipment. The breast density of the cases was 

independently assessed by 12 experienced mammographic readers using a separate VAS for each 

radiographic projection (craniocaudal and mediolateral oblique) of both breasts. In addition, a trained and 

validated Cumulus user also estimated the density for each image. Assessment of inter-observer 

agreement and agreement with Cumulus was performed using the Bland-Altman limits of agreement 

approach and the concordance correlation coefficient (CCC). All density estimates were also discretised 

to the four BI-RADS breast composition categories and agreement on this ordinal scale measured with 

Cohen’s weighted kappa. 

Results: The maximum observed difference between estimates by two readers assessing the same case 

was 67.75 percentage points, while the mean difference between pairs of readers ranged from 0.76 to 

28.58 percentage points and the mean difference from Cumulus ranged from 0.38 to 22.98 percentage 

points. 95% limits of agreement between reader pairs, between which 95% of differences are expected to 

lie, were (-6.96, 18.62) at their narrowest and (-59.13, 1.97) at their widest, while between readers and 

Cumulus they ranged from (-32.55, -3.29) to (-49.71, 3.94). Pairwise CCC values ranged from 0.44 to 

0.92, while the overall CCC across the 12 readers was 0.70. Concordance with Cumulus ranged from 0.48 

to 0.87. Pairwise kappa values for the BI-RADS classification ranged from 0.37 to 0.84 for the 12 readers, 

with a mean of 0.66. Kappa values between the readers and Cumulus ranged from 0.40 to 0.80. 

ABSTRACTS 

Conclusion: A lack of agreement was found between the readers visually assessing breast density and a 

comparable lack of agreement was also found between the readers and Cumulus. This group of readers 

had varying levels of experience of VAS breast density assessment, but were all routinely performing 

estimation in the PROCAS study. The current study suggests that for VAS density to be used for risk 

stratification and personalised screening, inter-observer variation must be accounted for, either through 

standardised training or by applying a correction to results after reading has been performed. 

78

P44 



LARGE INTERNATIONAL STUDY REVEALS NOVEL ASSOCIATIONS BETWEEN 

COMMON BREAST CANCER SUSCEPTIBILITY VARIANTS AND RISK- 

PREDICTING MAMMOGRAPHIC DENSITY MEASURES 

Jennifer Stone 1 , Deborah Thompson 2 , Isabel dos Santos Silva 3 , Christopher Scott 4 , Melissa Southey 5 , 

Laura Baglietto 6 , Fergus Couch 4 , Sara Lindström 7,8 , Jingmei Li 9 , Gretchen Gierach 10 , Julie A Douglas 11 , 

Javier Benitez 12 , Peter Fasching 13 , Graham Giles 6 , Marina Pollan 12 , Jonine Figueroa 10 , Kristen Stevens 4 , 

Norman F. Boyd 14 , Per Hall 9 , Douglas F. Easton 2 , Rulla M. Tamimi 7,8,15 , John L. Hopper 1 and Celine 

Vachon 4 for the DENSNP and Markers of Density (MODE) consortia 

ABSTRACTS 

1 Centre for Molecular, Environmental, Genetic and Analytic Epidemiology, School of Population Health, 

The University of Melbourne, Melbourne, Australia; 2 Centre for Genetic Epidemiology, Department of 

Public Health and Primary Care, University of Cambridge, Cambridge, UK; 3 Faculty of Epidemiology 

and Population Health, London School of Hygiene and Tropical Medicine, London, UK; 4 Department of 

Health Sciences Research, Mayo Clinic, Rochester, MN, USA; 5 Genetic Epidemiology Laboratory 

(GEL), Department of Pathology, The University of Melbourne, Melbourne, Australia; 6 Cancer 

Epidemiology Centre, Cancer Council Victoria, Melbourne, Australia; 7 Program in Molecular and 

Genetic Epidemiology, 8 Department of Epidemiology , Harvard School Of Public Health, Boston, MA, 

USA; 9 Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden; 

10 

Hormonal and Reproductive Epidemiology Branch, National Cancer Institute, Rockville, MD ; 11 

Department of Human Genetics, University of Michigan Medical School, Ann Arbor, MI; 12 Centro 

Nacional de Investigaciones Oncologicas, Madrid, Spain; 13 Erlangen University Perinatal Center, 

Department of Gynaecology and Obstetrics, Erlangen University Hospital, Friedrich Alexander 

University of Erlangen-Nuremberg, Erlangen, Germany; 14 Campbell Family Institute for Breast Cancer 

Research, Ontario Cancer Institute, Toronto, Ontario, Canada; 15 Channing Division of Network 

Medicine, Brigham and Women’s Hospital, Boston, MA 

Background: Mammographic density adjusted for age and body mass index (BMI) is a heritable 

predictor of breast cancer risk but the underlying biological processes are unclear. To date, 77 common 

variants have been found to be associated with breast cancer susceptibility. 

Methods: We examined the associations between these variants and mammographic density measures 

(absolute dense area, percent dense area and absolute non-dense area) adjusted for study, age and BMI 

using mixed linear models using 10,727 women from the international DENSNP and Markers Of DEnsity 

(MODE) consortia. 

Results: We found strong support for previously reported associations of rs10995190 (in the region of 

ZNF365), rs2046210 (ESR1) and rs3817198 (LSP1) with adjusted absolute and percent dense areas (all p 

P45 



META-ANALYSIS OF TWELVE GENOME-WIDE ASSOCIATION STUDIES (GWAS) 

IDENTIFIES NOVEL GENETIC LOCI ASSOCIATED WITH MAMMOGRAPHIC 

DENSITY PHENOTYPES 

Sara Lindström 1,2 , Deborah Thompson 3 , Andrew D. Paterson 4 , Jingmei Li 5 , Gretchen L. Gierach 6 , Jennifer 

Stone 7 , Julie A. Douglas 8 , Isabel dos Santos Silva 9 , Javier Benitez 10 , Christopher Scott 11 , Peter A. Fasching 12 , 

Laura Baglietto 13 , Melissa Southey 7 , Graham Giles 13 , Marina Pollan 10 , Jonine Figueroa 6 , Fergus Couch 11 , John 

L. Hopper 7 , Per Hall 5 , Douglas F. Easton 3 , Norman F. Boyd 14 , Celine M. Vachon 11† , and Rulla M. Tamimi 1,2,15† 

† 

for the Markers of Density (MODE) consortium Co-Last Authors 

1 Program in Molecular and Genetic Epidemiology; 2 Department of Epidemiology , Harvard School Of Public 

Health, Boston, MA, USA; 3 Centre for Genetic Epidemiology, Department of Public Health and Primary Care, 

University of Cambridge, Cambridge, UK; 4 Program in Genetics and Genome Biology, The Hospital for Sick 

Children, Toronto, Ontario, Canada; 5 Department of Medical Epidemiology and Biostatistics, Karolinska 

Institutet, Stockholm, Sweden; 6 Hormonal and Reproductive Epidemiology Branch, National Cancer Institute, 

Rockville, MD ; 7 Centre for Molecular, Environmental, Genetic and Analytic Epidemiology, School of 

Population Health, The University of Melbourne, Melbourne, Australia; 8 Department of Human Genetics, 

University of Michigan Medical School, Ann Arbor, MI; 9 Faculty of Epidemiology and Population Health, 

London School of Hygiene and Tropical Medicine, London, UK; 10 Centro Nacional de Investigaciones 

Oncologicas, Madrid, Spain; 11 Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA; 

12 Erlangen University Perinatal Center, Department of Gynaecology and Obstetrics, Erlangen University 

Hospital, Friedrich Alexander University of Erlangen-Nuremberg, Erlangen, Germany; 

13 Cancer 

Epidemiology Centre, Cancer Council Victoria, Melbourne, Australia; 14 Campbell Family Institute for Breast 

Cancer Research, Ontario Cancer Institute, Toronto, Ontario, Canada; 15 Channing Division of Network 

Medicine, Brigham and Women’s Hospital, Boston, MA 

Mammographic density is one of the strongest risk factors for breast cancer and has been shown to have a 

heritable component that remains poorly understood. In a previous meta-analysis of five genome-wide 

association study (GWAS) of mammographic density in 4,887 women within the MODE consortium, we 

identified common variants in the breast cancer susceptibility locus ZNF365 associated with percent 

mammographic density. We have now extended our meta-analysis to include seven additional GWAS of 

percent mammographic density as well as report results from the first GWAS of dense area and non-dense 

area. We conducted a meta-analysis of 12 GWAS of percent mammographic density, dense area, and non-dense 

area on a mammogram in almost 8,000 women of European descent. Ten of the studies used linear regression 

treating the mammographic density phenotype as a quantitative trait adjusting for age, body mass index (BMI) 

and menopausal status. Two studies selected women based on the extreme categories of mammographic 

density (one using percent mammographic density and the other dense area) adjusted for age, BMI and 

menopausal status. The differences in study design (extreme sampling vs. continuous trait) did not allow us to 

perform meta-analysis based on the estimated effect size in each study as units of density measurement were 

not comparable across studies. Therefore, we performed a combined test for each SNP by combining p-values 

and the direction of association for each study, weighted by the square-root of the sample size and the study 

specific inflation factor. We conducted replication of SNPs with P



P46 NON-INVASIVE OPTICAL ASSESSMENT OF BREAST DENSITY AND 

IDENTIFICATION OF HIGH-RISK SUBJECTS 

Paola Taroni, 1 * Giovanna Quarto, 1 Antonio Pifferi, 1,2 Lorenzo Spinelli, 2 Alessandro Torricelli, 1 Francesca 

Ieva, 3 Anna Maria Paganoni, 3 Francesca Abbate, 4 Nicola Balestreri, 5 Simona Menna, 4 Enrico Cassano, 4 and 

Rinaldo Cubeddu 1,2 

1 Dipartimento di Fisica, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milano, Italy; 2 Istituto di 

Fotonica e Nanotecnologie, Consiglio Nazionale delle Ricerche, Piazza Leonardo da Vinci 32, 20133 Milano, 

Italy; 3 Dipartimento di Matematica, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milano, Italy; 

4 European Institute of Oncology, Breast Imaging Unit, Via G. Ripamonti, 435, 20141 Milano, Italy ; 

5 European Institute of Oncology, Department of Radiology, Via G. Ripamonti, 435, 20141 Milano, Italy 

We propose the assessment of breast density by non-invasive optical means using time domain diffuse optical 

spectroscopy. 

Optical techniques can provide functional and structural information on biological tissue in an absolutely noninvasive 

way, and they have already been successfully applied to the characterization of breast tissue in vivo. 

We have further exploited the potential of diffuse optical spectroscopy operating in the time domain to assess 

both tissue composition in terms of key constituents and scattering parameters that are related to the 

microscopic structure of tissue and specifically to breast density. 

ABSTRACTS 

Time domain multi-wavelength (635-1060 nm) optical mammography was performed on 147 subjects, and 

average breast tissue composition (water, lipid, collagen, oxy- and deoxyhemoglobin) and scattering 

parameters (amplitude a and slope b) were estimated using the diffusion approximation to the radiative transfer 

theory to model photon propagation in tissue. Mammographic density was classified through BI-RADS 

categories. 

The dependence of optically derived tissue composition and scattering parameters on BI-RADS categories was 

investigated. All parameters show highly significant difference for BI-RADS category 2 vs 3, and 3 vs 4 (nonparametric 

Wilcoxon test, p < 0.005), while for most parameters the difference between category 1 and 2 is not 

statistically significant. Specifically, increasing breast density corresponds to progressively increasing average 

amounts of water and collagen, while the lipid content decreases gradually. An increase with BI-RADS 

category is also observed in both scattering amplitude a and slope b, in agreement with differences in 

microscopic structure expected between fatty and fibroglandular tissue. The blood parameters (i.e. total 

hemoglobin content tHb and oxygenation level SO 2 ) are less sensitive, with only tHb showing a slight increase 

with mammographic density. 

The linear correlation between optically derived parameters was also investigated. In particular, lipid content 

shows strong negative correlation with both water and collagen content. Moreover, positive (negative) 

correlation exists between scattering amplitude a and concentrations of water and collagen (lipid). Such 

observations are consistent with the hypothesis that the major contribution to scattering comes from 

fibroglandular tissue components. 

To develop a procedure for the identification of high-risk women, the mammographic density was 

dichotomized, comparing subjects in BI-RADS categories 1 to 3 to subjects in category 4. The p-values of the 

Wilcoxon test showed that tHb, lipid, water, collagen, a e b are significantly different in the two populations 

considered (at least p < 0.001), while SO 2 is not. The best regression logistic model for the risk probability was 

found via a stepwise variables selection minimizing the AIC (Akaike Information Criterion) and resulted to 

depend on collagen content and scattering parameters. The model provides a total misclassification error of 

12.3%, corresponding to a simple kappa of 0.84, which compares favorably with the reproducibility of BI- 

RADS measures among radiologists and even intra-radiologist. 

These results qualify the optical technique as a potential candidate for non-invasive, relatively simple, and 

operator-independent assessment of breast-density related risk. 

81

P47 



RETROSPECTIVE ANALYSIS OF BREAST DENSITY IN TRIPLE NEGATIVE BREAST 

CANCERS 

Gopal R Vijayaraghavan, Nancy Restighini, Rebecca Hultman, Jilliane Sotello and Srinivasan 

Vedantham. 

Dept of Radiology, UMass Medical School, 55 lake avenue north, Worcester, MA-01655. 

Introduction: Breast density is generally considered a surrogate marker of breast tissue proliferation. 

Multiple studies have shown a 4-5 fold increased relative risk of breast cancer in women with dense 

breasts. Younger women tend to have denser breasts and triple negative tumors are more common in 

younger women. Triple negative breast tumors account for approximately 20% of all breast cancers and 

are generally considered more aggressive with a poorer prognosis. We retrospectively analyzed our data 

for any correlation between breast density and triple negative tumors, their clinical stage and node 

positivity. 

Materials and Methods: We retrospectively analyzed mammograms of all triple negative tumors 

operated in our institute over a 3 year period from Jan 2009- Dec 2011. These mammograms were 

reviewed on a secur view hologic work station for breast density and categorized from 1- 4 based on ACR 

BI-RADS category. From the hospital medical records demographic data such as patient age, clinical 

stage of the tumor, histology and nodal status were also obtained and tabulated on an excel spread sheet. 

There were 147 triple negative tumors. Records of mammograms in 4 cases were not retrievable. Breast 

density was tabulated in 143 cases and compared with a control arm of 132 BI-RAD’s 4 and 5 cases from 

a different study. Nodal status was available in 128 cases. 12 patients had a significant family history of 

breast cancer and 3 patients had a positive BRCA test. Given small numbers correlation to breast density 

in this cohort was not evaluated. 

Results: Of the 143 evaluable cases, there were 3 patients in BI-RADS breast density category 1, 59 in 

category 2, 73 in category 3 and 8 in category 4. The mean age of presentation was 60 years, the median 

was 61 years and the range from 29-93 years. Ductal histology was noted in 110 cases, lobular in 28 cases 

and 9 cases had other histologies. 78 tumors were clinically Stage 1, 49 Stage 2, 8 Stage 3 and 2 Stage 4 

tumors were documented. 6 of the cases were documented as Tx. 36 patients had positive nodes at surgery 

and 92 patients with negative nodes in the 128 evaluable cases. 

Table 1: Correlation (Spearman rho) between breast density, tumor stage and nodal status 

"TNC-BD" "Stage" "Node status" 

"TNC-BD" 1 0.24629* 0.14925 

"Stage" 0.24629* 1 0.46524* 

"Node status" 0.14925 0.46524* 1 

* p0.79, Mann-Whitney test) 

between women diagnosed with triple-negative cancers and the control group (BI-RADS 4/5 subjects). 

Statistically significant and positive correlations were observed between BI-RADS breast density and 

tumor stage, and between tumor stage and nodal involvement. 

Conclusion: In our study triple negative breast cancers did not display any unique correlation to breast 

density. The findings were no different from those described in literature for other breast cancers. 

82



P48 

A FULLY-AUTOMATED METHOD FOR QUANTIFYING FIBROGLANDULAR 

TISSUE AND BACKGROUND PARENCHYMAL ENHANCEMENT IN BREAST MRI 

Shandong Wu, Susan P. Weinstein, Emily F. Conant, Despina Kontos 

Department of Radiology, University of Pennsylvania, Philadelphia PA USA 

Purpose: The goal is to provide a methodological framework for automated quantitative analysis of the 

fibroglandular tissue (FGT) and background parenchymal enhancement (BPE) in breast magnetic 

resonance imaging (MRI) for breast cancer risk assessment and risk reduction interventions in high-risk 

women. 

Background: Breast MRI provides 3D scanning and therefore can eliminate the tissue superposition 

problem in mammography. Recent studies suggest that FGT and BPE are associated with breast cancer 

risk assessment, specifically for high-risk women. However, currently FGT and BPE are mainly evaluated 

qualitatively by visual assessment, which suffers from subjective results and inter- and intra-reader 

variability. 

ABSTRACTS 

Method: Our fully automated method for quantifying FGT and BPE includes three steps (Fig. 1). First, 

an integrated scheme of synergistic edge extraction has been developed to detect chest wall line so that 

segment the whole breast. Second, the FGT is estimated based on our proposed fuzzy-c-means (FCM)- 

Atlas method. Third, BPE is estimated through identifying the enhancing voxels in the DCE subtraction 

(sub) images by measuring the relative intensity change relative to the intensity in the pre-contrast (pre) 

images. We define an enhancement ratio R = Intensity_sub / Intensity_pre × 100 and the voxels that have 

a greater value than a predefined threshold are identified as the enhancing voxels. Based on the 

segmentation, we compute four measures: absolute volume of FGT (|FGT|), percentage of |FGT| relative 

to the whole breast volume (FGT%), absolute volume of BPE (|BPE|), and percentage of |BPE| relative to 

the whole breast volume (BPE%). Our method has been evaluated using a representative dataset of 60 

bilateral breast MRI scans (120 breasts) through comparing to the manual segmentation obtained from 

two experienced breast imaging radiologists by using our customized user-interactive software 

(“MRwizard V.1.0”). 

Results: The inter-reader Pearson correlation coefficient (r) is 0.95 for FGT% and 0.96 for |FGT|. When 

compared to the average of the two readers, the proposed FGT segmentation method achieves a 

correlation of r=0.88 for FGT% and r=0.84 for |FGT|; the bilateral correlation between left breasts and 

right breasts is r=0.90 for FGT% and r=0.86 for |FGT|. For BPE-related measures, the inter-reader 

correlation is r=0.80 for |BPE | and r=0.78 for BPE%, respectively. We parameterize the enhancement 

ratio threshold ranging from 0% to 200% and the corresponding BPE is estimated. It is found that there is 

a range, i.e., 30% - 80%, rather than a single value of the enhancement ratio that high correlations (r = 0.8 

to 0.9) are achieved between the automated BPE estimation and the manual segmentation in terms of the 

4 BPE-related measures (i.e., |BPE| and BPE% by 2 readers). Our fully automated method runs timeefficiently 

at ~5 minute for each 3D MR scan (56 slices), compared to ~65 minutes needed for the manual 

segmentation. 

Conclusions: We have developed and validated a fully automated method for quantifying FGT and BPE 

in breast MRI. It has been used successfully as a quantitative imaging biomarker for assessing the 

response of risk-reducing salpingo-oophorectomy in BRCA1/2 mutation carriers and can also be used to 

support other clinical applications. 

83



DCE Sub 

(a) (b) (c) 

(d) (e) (f) 

Figure 1. Examples illustrating the workflow of the proposed method. (a) A tomographic slice of a breast MR image 

with the segmented breast outlined by the red contour. (b) The segmnted fibroglandular tissue (FGT) regions (green) 

within the breast. (c) Three-dimensional (3D) volume of the breast (red) and the segmented FGT (green). (d) The DCE- 

MRI subtraction images are used to estimate the background parenchymal ehnhancement (BPE). (e) The translated 

breast mask (red), the FGT (green) over the subtraction image shown in (d) with the idenfitied BPE (purple). (f) 3D 

volume of the breast (red), the FGT (green), and the BPE (purple). 

ABSTRACTS 

84

2013 PARTICIPANTS 



PARTICIPANTS 

First Name Last Name Affiliation Email address 

Irene Acerbi UCSF irene.acerbi@ucsfmedctr.org 

Luana Acton Kaiser Permanente Division of Research luana.acton@kp.org 

Carmel Apicella University of Melbourne capic@unimelb.edu.au 

Valentina Assi Queen Mary, University of London v.assi@qmul.ac.uk 

Sue Astley U-Manchester sue.astley@manchester.ac.uk 

Alfred Au UCSF alfred.au@ucsfmedctr.org 

Marje Bakker 

Julius Center for Health Sciences and 

Primary Care, University Medical Center 

Utrecht, the Netherlands 

m.f.bakker-8@umcutrecht.nl 

Desiree Basila UCSF, SPORE Advocacy dlbasila@gmail.com 

Tricia Benjamin Palo Alto Medical Foundation benjamt@pamf.org 

Kimberly Bertrand Harvard School of Public Health kbertran@hsph.harvard.edu 

Rives Bird VuCOMP, Inc. kayla.svec@vucomp.com 

Archie Bleyer Pediatric & Young Adult Oncology ableyer@gmail.com 

Norman Boyd Ontario Cancer Institute boyd@uhnres.utoronto.ca 

Dejana Braithwaite UCSF DBraithwaite@epi.ucsf.edu 

Kathy Brandt Mayo Clinic brandt.kathy@mayo.edu 

Adam Brentnall Queen Mary University of London a.brentnall@qmul.ac.uk 

Allison Bunch Sutter Health buncha@sutterhealth.org 

Barry Burgdorf VuCOMP, Inc. kayla.svec@vucomp.com 

Anya Burton 

<strong>International</strong> Agency for Research on 

Cancer 

burtona@fellows.iarc.fr 

Sherry Butler Kaiser Permanente 

Celia Byrne 

Uniformed Services University of the 

Health Sciences 

celia.byrne@usuhs.edu 

Nancy Cappello Are You Dense Advocacy nancy@areyoudense.org 

Adrienne Castillo Kaiser Permanente-Division of Research adrienne.castillo@kp.org 

Ariane Chan Matakina Technology e.Chan@matakina.com 

Biao Chen Hologic, Inc. biao.chen@hologic.com 

Chira Chen-Tanyolac UCSF chira.chen-tanyolac@ucsf.edu 

Ann Johnston Cloud 

Mailman School of 

Columbia University 

Public Health, 

asj2117@columbia.edu 

Deborah Collyar UCSF & Patient Advocates In Research Deborah@tumortime.com 

Amy Colton Are You Dense, Inc abcolton@comcast.net 

Emily Conant University of Pennsylvania emily.conant@uphs.upenn.edu 

Alice Couwenberg 

University Medical Center, Utrecht, the 

Netherlands 

alice.couwenberg@gmail.com 

Margo Cusack Sutter Health cusackm@sutterhealth.org 

Lance Dai U-Systems Lance.Dai@ge.com 

Laura Damiano UCSF lauradamiano2706@gmail.com 

Hatef Darabi Karolinska Institutet hatef.darabi@ki.se 

Allen Davies U-Systems Alan.G.Davies@ge.com 

Shaheenah Dawood Harvard shaheenah@post.harvard.edu 

Judy Dean Santa Barbara Women's Imaging judy@judydeanmd.net 

RosaAnna DeFilippis UCSF rosa-anna.defilippis@ucsf.edu 

Caroline Dickens <strong>International</strong> Agency for Research on Cancer dickensc@fellows.iarc.fr 

Isabel dos Santos Silva 

London School of Hygiene and Tropical 

isabel.silva@lshtm.ac.uk 

Medicine 

Jennifer Drukteinis Moffitt Cancer Center jendruk@yahoo.com 

85




Meg Durbin Palo Alto Medical Foundation durbinm@pamf.org 

Merete Ellingjord-Dale 

University of Oslo, Institute of Basic 

Medical Sciences, Dept. of Nutrition 

ellingjord-dale@medisin.uio.no 

Sarah Elson UCSF Sarah.Elson@ucsf.edu 

Julius Center for Health Sciences and 

Marleen Emaus 

Primary Care, University Medical Center m.j.emaus@umcutrecht.nl 

Utrecht, the Netherlands 

Natalie Engmann 

Mailman School of Public Health, Columbia 

University 

ne2227@columbia.edu 

Luis Estevez UCSF lestevez@ucsf.edu 

Bo Fan UCSF bo.fan@ucsf.edu 

Peter Fasching University Hospital Erlangen, German peter.fasching@uk-erlangen.de 

Jonathan Fish Bay Imaging Associates shmd@gmail.com 

Henri Folse Archimedes, Inc. henri.folse@archimedesmodel.com 

June Fong Sutter Health Fongjy@sutterhealth.org 

Jacqueline Frost Sutter Health frostjv@sutterhealth.org 

Barbara Fuhrman University of Arkansas for Medical Sciences bjfuhrman@uams.edu 

Mary Luisa Garmendia 

Institute of Nutrition and Food Technology, 

University of Chile 

mgarmend@gmail.com 

Berta Geller U-Vermont berta.geller@uvm.edu 

Karthik Ghosh Mayo Clinic, Rochester, MN ghosh.karthik@mayo.edu 

Gretchen Gierach National Institute of Health gierachg@mail.nih.gov 

William Goodson Sutter Health whgjgg@sbcglobal.net 

Deborah Gordon UCSF gordond@dahsm.ucsf.edu 

Rebecca Graff Harvard School of Public Health rgraff@mail.harvard.edu 

Lori Greene Sutter Health greenlx@sutterhealth.org 

Sarah Griffith Sutter Health GriffiSZ@sutterhealth.org 

Arun Guruajan VuCOMP, Inc. kayla.svec@vucomp.com 

Laurel Habel Division of Research/Kaiser Permanente laurel.habel@kp.org 

Jennifer Harvey U-Virginia jah7w@virginia.edu 

Tyler Haskell Office of Supervisor S. Joseph Simitian Tyler.Haskell@bos.sccgov.org 

Ralph Highnam Matakina <strong>International</strong> 

Ralph.Highnam@VolparaDensity.co 

m 

John Hipwell UCL j.hipwell@ucl.ac.uk 

Solveig Hofvind Cancer registry of Norway Solveig.Hofvind@kreftregisteret.no 

John Hopper University of Melbourne j.hopper@unimelb.edu.au 

Carrie Hruska Mayo Clinic hruska.carrie@mayo.edu 

Scott Huntley Tractus sphuntley@aol.com 

Ella 

Huszti 

Campbell Family Institute for Breast Cancer 

Research, Ontario Cancer Institute 

ehuszti@uhnresearch.ca 

Debra Ikeda Stanford dikeda@stanford.edu 

Jessie Jacobs U-Systems jjacob@u-systems.com 

Abra Jeffers Stanford University abra@stanford.edu 

Judy Jobling University of Newcastle (Australia) judy.jobling@anzbctg.org 

Bonnie Joe UCSF Bonnie.Joe@ucsf.edu 

Ella Jones UCSF ella.jones@ucsf.edu 

Leah Josephs Advocate maaravi.leah@gmail.com 

Katherine Jue Kaiser Permanente Katherine.F.Jue@kp.org 

Jane Kakkis Orange Coast Breast Center askdrjane123@yahoo.com 

Celia Kaplan UCSF celia.kaplan@ucsf.edu 

Leah Karliner UCSF leah.karliner@ucsf.edu 

Nico Karssemeijer Radboud University Nijmegen Medical n.karssemeijer@rad.umcn.nl 

PARTICIPANTS 

86

PARTICIPANTS 

Centre 




Leila Kazemi UCSF leila.kazemi@ucsf.edu 

Patricia Keeley U-Wisconsin pjkeely@facstaff.wisc.edu 

Mallika Keralapura U-Systems mkeralapura@u-systems.com 

Karla Kerlikowske UCSF karla.kerlikowske@ucsf.edu 

Philip Kivitz Stanford University Medical Center pghsinc@aol.com 

Julia 

Knight 

Samuel Lunefeld Research Institute of 

Mount Sinai Hospital 

knight@lunenfeld.ca 

Despina Kontos U-Pennsylvania Despina.Kontos@uphs.upenn.edu 

Kavitha Krishnan Cancer Epidemiology Centre kavitha.krishnan@cancervic.org.au 

Ashwini Kshirsagar Hologic akshirsagar@hologic.com 

Jiyon Lee NYU JIYON_L@hotmail.com 

Vivian Lee Advocate vlee@aquapartners.net 

Jessica Leung Sutter Health LeungJW@sutterhealth.org 

Martin Lillholm Biomediq A/S / University of Copenhagen mli@biomediq.com 

Sara Lindstrom Harvard University slindstr@hsph.harvard.edu 

Amir Mahmoudzadeh UCSF amirpasha.mahmoudzadeh@ucsf.edu 

Kunlanat Makboom 

Suphanburi College of Public Health, 

Thailand 

kmakboon@ucdavis.edu 

Serghei Malkov UCSF Serghei.Malkov@ucsf.edu 

Ori Maller UCSF Ori.Maller@ucsfmedctr.org 

Lisa 

Martin 


Research 

lmartin@uhnres.utoronto.ca 

Gertraud Maskarinec U-Hawaii Cancer Center gertraud@cc.hawaii.edu 

Valerie McCormack <strong>International</strong> Agency for Research on Cancer mccormackv@iarc.fr 

Valerie McGuire Stanford vmcguire@stanford.edu 

Diana Miglioretti UC-Davis dmiglioretti@ucdavis.edu 

James Monaco VuCOMP, Inc. kayla.svec@yahoo.com 

Elizabeth Morris Memorial-Sloan Kettering Cancer Center Morrise@mskcc.org 

Sibel Narin Hologic sibel.narin@hologic.com 

Kevin Nguyen University of Melbourne nguk@unimelb.edu.au 

Mads Nielsen University of Copenhagen madsn@diku.dk 

Lawrence Oliver Boca Raton Regional Hospital kloliver@gmail.com 

Elissa Ozanne UCSF elissa.ozanne@ucsfmedctr.org 

Deng Pan UCSF deng.pan@ucsf.edu 

Shane Pankratz Mayo Clinic, Rochester MN pankratz.vernon@mayo.edu 

Bahram Parvin Lawrence Livermore National Laboratory parvin.bahram@gmail.com 

Ana 

Pereira 

University of Chile/Institute of Nutrition and 

Food Technology 

apereira@inta.uchile.cl 

Dan Perisho SonoCine, Inc. dperisho@sonocine.com 

Kersten Petersen University of Copenhagen ten@biomediq.com 

Suzanne Ponik U-Wisconsin suzponik@gmail.com 

Mark Powell Marin Women's Study mpowell@marincounty.org 

Lee Ann Prebil Marin County DHHS lprebil@cal.berkeley.edu 

Andrew Prokop UCSF andrew.prokop@ucsf.edu 

JoAnn Pushkin D.E.N.S.E., D.E.N.S.E. NY Dense-NY@optonline.net 

Dave Reinhart SonoCine Dreinhart@sonocine.com 

Richard Reitherman Orange Coast Breast Center reithermanbreastmri@yahoo.com 

Deborah Rhodes Mayo Clinic jsgould@charter.net 

Megan Rice Harvard/Brigham and Women's Hospital nhmsr@channing.harvard.edu 

Peter Richard Sutter Health PCRSF@YAHOO.COM 

Lisa Rivera-Serra U-Systems riveraserra@aol.com 

87

88 



Jimmy Roehrig Matakina jimmy.roehrig@pacbell.net 


Richard Rosene VuCOMP, Inc. kayla.svec@vucomp.com 

Eva Rubin Montgomery Radiology Associates Evarubin@aol.com 

Daniel Rubin Stanford University dlrubin@stanford.edu 

Bonnie Rush Breast Imaging Specialists brush4info@Aol.com 

Susan Samson Advocate ssamson@pacbell.net 

Valerie Sankatsing Erasmus MC, The Netherlands v.sankatsing@erasmusmc.nl 

Karen Sein Sutter Health seink@sutterhealth.org 

John Shepherd UCSF john.shepherd@ucsf.edu 

Ed Sickles UCSF edward.sickles@ucsfmedctr.org 

Weiva Sieh Stanford wsieh@stanford.edu 

Mahvash Sigaroudinia UCSF mahvash.sigaroudinia@ucsf.edu 

Joe Simitian Santa Clara County Supervisor Candace.Joy@bos.sccgov.org 

Patty Smith Imaging and EEG/ECG Services patty_smith@elcaminohospital.org 

Brian Sprague University of Vermont bsprague@uvm.edu 

Nicole Stendell-Hollis U-Minnesota nstendel@umn.edu 

Jennifer Stone U-Melbourne stonej@unimelb.edu.au 

Natasha Stout 

Harvard Medical School and Harvard 

Pilgrim Health Care Institute 

natasha_stout@hms.harvard.edu 

Dick Tabbutt VuCOMP, Inc. kayla.svec@vucomp.com 

Rulla Tamimi Harvard Medical School rulla.tamimi@channing.harvard.edu 

Paola Taroni Politecnico di Milano paola.taroni@polimi.it 

Mary Beth Terry 


University 

mt146@columbia.edu 

Jeff Tice UCSF jtice@medicine.ucsf.edu 

Amy Trentham-Dietz U-Wisconsin trentham@wisc.edu 

Melissa Troester UNC School of Public Health troester@unc.edu 

Celine Vachon Mayo Clinic vachon.celine@mayo.edu 

Carla Van Gils University Medical Center Utrecht C.vangils@umcutrecht.nl 

Nicolien van Ravesteyn Erasmus MC, Dept. of Public Health n.vanravesteyn@erasmusmc.nl 

Robert Van Uitert iCAD, Inc. rvanuitert@icadmed.com 

JoAnn Venticinque Breast Cancer Connections joan@bcconnections.org 

Gopal Vijayaraghavan UMass Medical School gopalvijay@gmail.com 

Marvella Villasenor Kaiser Permanente Division of Research Marvella.A.Villasenor@kp.org 

Marina Voronchikhina UCSF marina.voronchikhina@ucsf.edu 

Hanneke Wanders University Medical Center Utrecht j.o.p.wanders@umcutrecht.nl 

Valerie Weaver UCSF valerie.weaver@ucsfmedctr.org 

Lani Wegrzyn Harvard School of Public Health LRW@mail.harvard.edu 

Jeff Wehnes VuCOMP, Inc. kayla.svec@vucomp.com 

Dana Whaley Mayo Clinic whaley.dana@mayo.edu 

Alice Whittemore Stanford University alicesw@stanford.edu 

Dorota Wisner UCSF dorota.wisner@ucsf.edu 

Anna Wu USC annawu@usc.edu 

Martin Yaffe Sunnybrook Research Institute, U Toronto martin.yaffe@sri.utoronto.ca 

Alexandria Yonkers Sutter Health yonkera@sutterhealth.org 

Wei Zhang qview medical inc. wzhang@qviewmedical.com 

Jianxin Zhao UCSF jianxin.zhao@ucsf.edu 

Elad Ziv UCSF elad.ziv@ucsf.edu 

Judy Jobling University of Newcastle (Australia) judy.jobling@anzbctg.org 

Bonnie Joe UCSF Bonnie.Joe@ucsf.edu 

Ella Jones UCSF ella.jones@ucsf.edu 

Leah Josephs Advocate maaravi.leah@gmail.com 

PARTICIPANTS

PARTICIPANTS 



Katherine Jue Kaiser Permanente Katherine.F.Jue@kp.org 


Jane Kakkis Orange Coast Breast Center askdrjane123@yahoo.com 

Celia Kaplan UCSF celia.kaplan@ucsf.edu 

Leah Karliner UCSF leah.karliner@ucsf.edu 

Nico 

Karssemeijer 

Radboud University Nijmegen Medical 

Centre 

n.karssemeijer@rad.umcn.nl 

Leila Kazemi UCSF leila.kazemi@ucsf.edu 

Patricia Keeley U-Wisconsin pjkeely@facstaff.wisc.edu 

Mallika Keralapura U-Systems mkeralapura@u-systems.com 

Karla Kerlikowske UCSF karla.kerlikowske@ucsf.edu 

Philip Kivitz Stanford University Medical Center pghsinc@aol.com 

Julia 

Knight 

Samuel Lunefeld Research Institute of 

Mount Sinai Hospital 

knight@lunenfeld.ca 

Despina Kontos U-Pennsylvania Despina.Kontos@uphs.upenn.edu 

Kavitha Krishnan Cancer Epidemiology Centre kavitha.krishnan@cancervic.org.au 

Ashwini Kshirsagar Hologic akshirsagar@hologic.com 

Jiyon Lee NYU JIYON_L@hotmail.com 

Vivian Lee Advocate vlee@aquapartners.net 

Jessica Leung Sutter Health LeungJW@sutterhealth.org 

Martin Lillholm Biomediq A/S / University of Copenhagen mli@biomediq.com 

Sara Lindstrom Harvard University slindstr@hsph.harvard.edu 

Amir Mahmoudzadeh UCSF amirpasha.mahmoudzadeh@ucsf.edu 

Kunlanat Makboom 

Suphanburi College of Public Health, 

Thailand 

kmakboon@ucdavis.edu 

Serghei Malkov UCSF Serghei.Malkov@ucsf.edu 

Ori Maller UCSF Ori.Maller@ucsfmedctr.org 

Lisa 

Martin 


Research 

lmartin@uhnres.utoronto.ca 

Gertraud Maskarinec U-Hawaii Cancer Center gertraud@cc.hawaii.edu 

Valerie McCormack <strong>International</strong> Agency for Research on Cancer mccormackv@iarc.fr 

Diana Miglioretti UC-Davis dmiglioretti@ucdavis.edu 

James Monaco VuCOMP, Inc. kayla.svec@yahoo.com 

Elizabeth Morris Memorial-Sloan Kettering Cancer Center Morrise@mskcc.org 

Sibel Narin Hologic sibel.narin@hologic.com 

Kevin Nguyen University of Melbourne nguk@unimelb.edu.au 

Mads Nielsen University of Copenhagen madsn@diku.dk 

Lawrence Oliver Boca Raton Regional Hospital kloliver@gmail.com 

Deng Pan UCSF deng.pan@ucsf.edu 

Shane Pankratz Mayo Clinic, Rochester MN pankratz.vernon@mayo.edu 

Ana 

Pereira 

University of Chile/Institute of Nutrition and 

Food Technology 

apereira@inta.uchile.cl 

Dan Perisho SonoCine, Inc. dperisho@sonocine.com 

Kersten Petersen University of Copenhagen ten@biomediq.com 

Suzanne Ponik U-Wisconsin suzponik@gmail.com 

Mark Powell Marin Women's Study mpowell@marincounty.org 

Lee Ann Prebil Marin County DHHS lprebil@cal.berkeley.edu 

Andrew Prokop UCSF andrew.prokop@ucsf.edu 

JoAnn Pushkin D.E.N.S.E., D.E.N.S.E. NY Dense-NY@optonline.net 

Dave Reinhart SonoCine Dreinhart@sonocine.com 

Richard Reitherman Orange Coast Breast Center reithermanbreastmri@yahoo.com 

Deborah Rhodes Mayo Clinic jsgould@charter.net 

89



Megan Rice Harvard/Brigham and Women's Hospital nhmsr@channing.harvard.edu 


Peter Richard Sutter Health PCRSF@YAHOO.COM 

Lisa Rivera-Serra U-Systems riveraserra@aol.com 

Jimmy Roehrig Matakina jimmy.roehrig@pacbell.net 

Richard Rosene VuCOMP, Inc. kayla.svec@vucomp.com 

Eva Rubin Montgomery Radiology Associates Evarubin@aol.com 

Daniel Rubin Stanford University dlrubin@stanford.edu 

Bonnie Rush Breast Imaging Specialists brush4info@Aol.com 

Susan Samson Advocate ssamson@pacbell.net 

Valerie Sankatsing Erasmus MC, The Netherlands v.sankatsing@erasmusmc.nl 

Karen Sein Sutter Health seink@sutterhealth.org 

John Shepherd UCSF john.shepherd@ucsf.edu 

Ed Sickles UCSF edward.sickles@ucsfmedctr.org 

Weiva Sieh Stanford wsieh@stanford.edu 

Mahvash Sigaroudinia UCSF mahvash.sigaroudinia@ucsf.edu 

Joe Simitian Santa Clara County Supervisor Candace.Joy@bos.sccgov.org 

Patty Smith Imaging and EEG/ECG Services patty_smith@elcaminohospital.org 

Brian Sprague University of Vermont bsprague@uvm.edu 

Christine Stavem 

Deputy Chief of Staff, Supervisor S. Joseph 

Simitian 

cstavem@yahoo.com 

Nicole Stendell-Hollis U-Minnesota nstendel@umn.edu 

Jennifer Stone U-Melbourne stonej@unimelb.edu.au 

Natasha Stout 

Harvard Medical School and Harvard 

Pilgrim Health Care Institute 

natasha_stout@hms.harvard.edu 

Dick Tabbutt VuCOMP, Inc. kayla.svec@vucomp.com 

Rulla Tamimi Harvard Medical School rulla.tamimi@channing.harvard.edu 

Paola Taroni Politecnico di Milano paola.taroni@polimi.it 

Mary Beth Terry 


University 

mt146@columbia.edu 

Jeff Tice UCSF jtice@medicine.ucsf.edu 

Amy Trentham-Dietz U-Wisconsin trentham@wisc.edu 

Melissa Troester UNC School of Public Health troester@unc.edu 

Celine Vachon Mayo Clinic vachon.celine@mayo.edu 

Carla Van Gils University Medical Center Utrecht C.vangils@umcutrecht.nl 

Nicolien van Ravesteyn Erasmus MC, Dept. of Public Health n.vanravesteyn@erasmusmc.nl 

Robert Van Uitert iCAD, Inc. rvanuitert@icadmed.com 

JoAnn Venticinque Breast Cancer Connections joan@bcconnections.org 

Gopal Vijayaraghavan UMass Medical School gopalvijay@gmail.com 

Marvella Villasenor Kaiser Permanente Division of Research Marvella.A.Villasenor@kp.org 

Marina Voronchikhina UCSF marina.voronchikhina@ucsf.edu 

Hanneke Wanders University Medical Center Utrecht j.o.p.wanders@umcutrecht.nl 

Valerie Weaver UCSF valerie.weaver@ucsfmedctr.org 

Lani Wegrzyn Harvard School of Public Health LRW@mail.harvard.edu 

Jeff Wehnes VuCOMP, Inc. kayla.svec@vucomp.com 

Dana Whaley Mayo Clinic whaley.dana@mayo.edu 

Alice Whittemore Stanford University alicesw@stanford.edu 

Dorota Wisner UCSF dorota.wisner@ucsf.edu 

Anna Wu USC annawu@usc.edu 

Martin Yaffe Sunnybrook Research Institute, U Toronto martin.yaffe@sri.utoronto.ca 

Alexandria Yonkers Sutter Health yonkera@sutterhealth.org 

Jianxin Zhao UCSF jianxin.zhao@ucsf.edu 

PARTICIPANTS 

90


Elad Ziv UCSF elad.ziv@ucsf.edu 


NOTES 

91



NOTES 

92



NOTES 

93



NOTES 

94

LET US KNOW WHAT YOU THINK 



Thank you for attending the 6 th <strong>International</strong> <strong>Workshop</strong> on Breast Densitometry and Breast Cancer 

Risk Assessment. 

Your input regarding our program is critical to the success of future events. Feel free to add you 

contact information, or to remain anonymous. 

Would you please take a few moments to answer the following: 

1. What was your most significant learning experience 

2. Which presenters would you like to hear again – were the most compelling 

FEEDBACK 

3. What specific topic(s) pertaining to breast density would you like presented that were not 

included at this meeting What topic(s) would you like to have more information presented 

4. What did you think of the facilities 

5. What feedback can you give us to help us make the next conference an even greater success 

6 Will you tell your colleague(s) of the benefit from attending the meeting 

7. Would charging $150 registration fee impact your ability or desire to attend the next Breast 

Density <strong>Workshop</strong> 

95



• 

ii Centers for Disease Control and Prevention (2013), Breast Cancer Risk Factors. Retrieved from 

http://www.cdc.gtors.ht 

iii D’Orsi, CJ,Bassett LW, Berg WA et al. Breast Imaging and Reporting and Data System, BI-RADS: 

Mammography, 4th ed. Reston, VA: American College of Radiology, 2003 

iv Harris Interactive National Survey, 5/2010 

v CT, TX, VA, NY, CA and HI 

Affix 

Postage 

Here 

Alice LaRocca 

San Francisco Coordinating Center 

185 Barry Street 

Lobby 5, Suite 5700 

San Francisco, CA 94107 

Please tear out this form, complete reverse, fold into thirds, add postage and mail. Thank you 

96

6th International Workshop on Breast Densitometry and Breast ...

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