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P10 6 th <strong>International</strong> <strong>Workshop</strong> on Breast Densitometry and Breast Cancer Risk Assessment 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 6 th <strong>International</strong> <strong>Workshop</strong> on Breast Densitometry and Breast Cancer Risk Assessment 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
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