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

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P29 6 th <strong>International</strong> <strong>Workshop</strong> on Breast Densitometry and Breast Cancer Risk Assessment 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
6 th <strong>International</strong> <strong>Workshop</strong> on Breast Densitometry and Breast Cancer Risk Assessment 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
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