Impact of pectoral muscle removal on deep-learning-based breast cancer risk prediction.

Abstract

Objective.State-of-the-art breast cancer risk (BCR) prediction models have been originally trained on mammograms with pectoral muscle (PM) included. This study investigated whether excluding PM during training/fine-tuning improves the model's BCR discrimination performance, calibration, and robustness.Approach.First, the Original deep learning model (MIRAI), trained on the US (Massachusetts General Hospital) data, was validated, and the relative contribution of PM to BCR predictions was evaluated using saliency maps. Additionally, 23 792 mammograms from the Slovenian screening program were collected and two datasets were created, with and without screening positive exams. The original MIRAI was then fine-tuned on the training/fine-tuning set of Slovenian mammograms with and without PM, creating Fine-tuned MIRAI models. In total, four models (Original MIRAI with PM, Original MIRAI without PM, Fine-tuned MIRAI with PM, Fine-tuned MIRAI without PM) were compared on a test set in terms of discrimination performance for 1-5 Year BCR (evaluating area under the curve), calibration performance (measured with expected calibration error-ECE) and robustness to incremental PM removals/additions, and to incremental breast tissue removals.Results.The relative contribution of PM to the BCR prediction on the Original MIRAI model was low (∼5%); however, there were significant outliers where the relative contribution was more than 50%. The removal of PM did not impact the 1-5 Year BCR discrimination performance of the Original MIRAI (with screening positive exams: 0.77-0.91, without screening positive exams: 0.64-0.67). Fine-tuned MIRAI on mammograms with PM removed achieved significantly higher 1-5 Year BCR discrimination performance (with screening positive exams: 0.82-0.93, without screening positive exams: 0.71-0.79). After recalibration, all models had similar ECE (with screening positive exams: 0.04-0.05, without screening positive exams: 0.02-0.03).Significance.Improved BCR discrimination performance can be achieved when the model is trained/fine-tuned on mammograms with PM removed.