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ABSTRACTS FOR SPEAKERS 6 th <strong>International</strong> <strong>Workshop</strong> on Breast Densitometry and Breast Cancer Risk Assessment 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
6 th <strong>International</strong> <strong>Workshop</strong> on Breast Densitometry and Breast Cancer Risk Assessment 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
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