Dr. Debbie L. Bennett Profile

Dr. Debbie L. Bennett

at Washington Univ. in St. Louis

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Publications (2)

Proceedings Article | 3 April 2024 Presentation + Paper

Breast density assessment via deep learning: head-to-head model comparisons in full-field digital mammograms and synthetic mammograms

Krisha Anant, Juanita Hernandez Lopez, Sneha Das Gupta, Debbie Bennett, Aimilia Gastounioti

Proceedings Volume 12927, 129270C (2024) https://doi.org/10.1117/12.3008648

KEYWORDS: Breast density, Mammography, Deep learning, Digital breast tomosynthesis, Education and training, Digital mammography, Image classification, Breast, Molybdenum, Medicine

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To enhance reproducibility and robustness in mammographic density assessment, various deep learning models have been proposed to automatically classify mammographic images into BI-RADS density categories. Our study aims to compare the performances of different deep learning models in making breast density classifications from full-field digital mammography (FFDM) versus synthetic mammography (SM), the newer 2D mammographic image format acquired with digital breast tomosynthesis (DBT). We retrospectively analyzed negative (BI-RADS 1 or 2) routine mammographic screening exams (Selenia or Selenia Dimensions; Hologic) acquired at sites within the Barnes-Jewish/Christian (BJC) Healthcare network in St. Louis, MO from 2015 to 2018. BI-RADS breast density assessments of radiologists were obtained from BJC’s mammography reporting software (Magview 7.1). For each mammographic imaging modality, a balanced dataset of 4,000 women was selected so there were equal numbers of women in each of the four BI-RADS density categories, and each woman had at least one mediolateral oblique (MLO) and one craniocaudal (CC) view per breast in that mammographic imaging modality. Previously validated pre-processing steps were applied to all FFDM and SM images to standardize image orientation and intensity. Images were then split into training, validation, and test sets at ratios of 80%, 10%, and 10%, respectively, while maintaining the distribution of breast density categories and ensuring that all images of the same woman appear only in one set. ResNet-50 and EfficientNet-B0 architectures were optimized, trained, and evaluated separately for different imaging modalities. Overall, the models had comparable performance, though ResNet-50 performed slightly better in most cases. Furthermore, FFDM images had better classification accuracies than SM images. Our preliminary findings suggest that further deep learning developments and optimizations may be needed as we develop breast density deep learning models for the newer mammographic imaging modality, DBT.

Proceedings Article | 3 April 2024 Poster + Paper

Beyond mammographic density: a computational imaging approach to identify effects of early adulthood adiposity on breast parenchymal tissue patterns in premenopausal women

Sneha Das Gupta, Kayla Getz, Juanita Hernandez Lopez, Debbie Bennett, Adetunji Toriola, Aimilia Gastounioti

Proceedings Volume 12927, 1292722 (2024) https://doi.org/10.1117/12.3008639

KEYWORDS: Breast, Mammography, Breast density, Adipose tissue, Breast cancer, Radiomics, Computational imaging, Principal component analysis

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Early-adulthood adiposity measures, such as weight and body-mass index, are associated with breast cancer risk. Moreover, studies have shown that breast parenchymal tissue patterns as reflected in digital mammograms (DMs) are also associated with breast cancer risk. We retrospectively analyzed DMs and early-adulthood adiposity data of 326 premenopausal women with the aim to assess the relationship of early-adulthood adiposity and breast parenchymal tissue patterns later in life. Radiomic features were extracted from DMs using a well-validated computational imaging pipeline and fused into woman-specific radiomic feature vectors via principal component analysis. Unsupervised hierarchical clustering was then applied to radiomic feature vectors to identify distinct phenotypes (clusters) of breast parenchymal complexity. For each early-adulthood adiposity measure, its associations with the identified breast parenchymal complexity clusters were assessed via logistic regression, adjusted for age at screening, race, family history of breast cancer, and parity. Two statistically significant clusters of breast parenchymal complexity (p-value < 0.001), “cluster 0” and “cluster 1”, were identified on the basis of principal components. Compared to women of high breast parenchymal complexity (“cluster 0”), women assigned to the cluster of lower breast parenchymal complexity (“cluster 1”) were associated with higher early-adulthood adiposity and larger adiposity changes over time. Among all early-adulthood adiposity measures, strongest associations with breast parenchymal complexity clusters were found for annual weight change from age 18 to age at DM screening (odds ratio [OR] = 2.46, 95% CI: [1.52, 3.99]) and annual weight change from age 30 to age at DM screening (OR = 1.73, 95% CI: [1.25, 2.39]). Our preliminary data suggest that adiposity in early adulthood, as well as weight gain from early adulthood to attained age are inversely associated with breast parenchymal complexity among premenopausal women and may have a lifelong impact on breast parenchymal tissue patterns.

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