Development and Validation of an AI-driven Mammographic Breast Density Classification Tool Based on Radiologist Consensus
Veronica Magni, Matteo Interlenghi, Andrea Cozzi, Marco Alì, Christian Salvatore, Alcide A. Azzena, Davide Capra, Serena Carriero, Gianmarco Della Pepa, Deborah Fazzini, Giuseppe Granata, Caterina Beatrice Monti, Giulia Muscogiuri, Giuseppe Pellegrino, Simone Schiaffino, Isabella Castiglioni, Sergio Papa, Francesco Sardanelli
Abstract
Mammographic breast density (BD) is commonly visually assessed using the Breast Imaging Reporting and Data System (BI-RADS) four-category scale. To overcome inter- and intraobserver variability of visual assessment, the authors retrospectively developed and externally validated a software for BD classification based on convolutional neural networks from mammograms obtained between 2017 and 2020. The tool was trained using the majority BD category determined by seven board-certified radiologists who independently visually assessed 760 mediolateral oblique (MLO) images in 380 women (mean age, 57 years ± 6 [SD]) from center 1; this process mimicked training from a consensus of several human readers. External validation of the model was performed by the three radiologists whose BD assessment was closest to the majority (consensus) of the initial seven on a dataset of 384 MLO images in 197 women (mean age, 56 years ± 13) obtained from center 2. The model achieved an accuracy of 89.3% in distinguishing BI-RADS a or b (nondense breasts) versus c or d (dense breasts) categories, with an agreement of 90.4% (178 of 197 mammograms) and a reliability of 0.807 (Cohen κ) compared with the mode of the three readers. This study demonstrates accuracy and reliability of a fully automated software for BD classification. Keywords: Mammography, Breast, Convolutional Neural Network (CNN), Deep Learning Algorithms, Machine Learning Algorithms Supplemental material is available for this article. © RSNA, 2022