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

• Published in: Cancers Vol. 14; no. 20; p. 5003
• Main Authors: Rigaud, Bastien, Weaver, Olena O, Dennison, Jennifer B, Awais, Muhammad, Anderson, Brian M, Chiang, Ting-Yu D, Yang, Wei T, Leung, Jessica W. T, Hanash, Samir M, Brock, Kristy K
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-10-2022; MDPI
• Subjects: Automation; Breast cancer; Care and treatment; Classification; Deep learning; Image processing; Machine learning; Mammography; Medical screening; Mortality; Neural networks; Questionnaires; Risk assessment; Software; Technology application; United States
• ISSN: 2072-6694; 2072-6694
• DOI: 10.3390/cancers14205003

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.