Deep Learning Models for Automated Assessment of Breast Density Using Multiple Mammographic Image Types

Bastien Rigaud; Olena O Weaver; Jennifer B Dennison; Muhammad Awais; Brian M Anderson; Ting-Yu D Chiang; Wei T Yang; Jessica W T Leung; Samir M Hanash; Kristy K Brock

doi:10.3390/cancers14205003

Deep Learning Models for Automated Assessment of Breast Density Using Multiple Mammographic Image Types

Cancers (Basel). 2022 Oct 13;14(20):5003. doi: 10.3390/cancers14205003.

Authors

Affiliations

¹ Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
² Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
³ Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
⁴ Department of Clinical Cancer Prevention, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
⁵ Department of Radiation Medicine and Applied Sciences, University of California San Diego, San Diego, CA 92093, USA.
⁶ Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.

Abstract

Recently, convolutional neural network (CNN) models have been proposed to automate the assessment of breast density, breast cancer detection or risk stratification using single image modality. However, analysis of breast density using multiple mammographic types using clinical data has not been reported in the literature. In this study, we investigate pre-trained EfficientNetB0 deep learning (DL) models for automated assessment of breast density using multiple mammographic types with and without clinical information to improve reliability and versatility of reporting. 120,000 for-processing and for-presentation full-field digital mammograms (FFDM), digital breast tomosynthesis (DBT), and synthesized 2D images from 5032 women were retrospectively analyzed. Each participant underwent up to 3 screening examinations and completed a questionnaire at each screening encounter. Pre-trained EfficientNetB0 DL models with or without clinical history were optimized. The DL models were evaluated using BI-RADS (fatty, scattered fibroglandular densities, heterogeneously dense, or extremely dense) versus binary (non-dense or dense) density classification. Pre-trained EfficientNetB0 model performances were compared using inter-observer and commercial software (Volpara) variabilities. Results show that the average Fleiss' Kappa score between-observers ranged from 0.31-0.50 and 0.55-0.69 for the BI-RADS and binary classifications, respectively, showing higher uncertainty among experts. Volpara-observer agreement was 0.33 and 0.54 for BI-RADS and binary classifications, respectively, showing fair to moderate agreement. However, our proposed pre-trained EfficientNetB0 DL models-observer agreement was 0.61-0.66 and 0.70-0.75 for BI-RADS and binary classifications, respectively, showing moderate to substantial agreement. Overall results show that the best breast density estimation was achieved using for-presentation FFDM and DBT images without added clinical information. Pre-trained EfficientNetB0 model can automatically assess breast density from any images modality type, with the best results obtained from for-presentation FFDM and DBT, which are the most common image archived in clinical practice.

Keywords: deep learning (DL); digital breast tomosynthesis (DBT); full-field digital mammograms (FFDM); synthesized 2D images.

Grants and funding

CA016672/CA/NCI NIH HHS/United States