Recent AI breast cancer risk prediction models are difficult to interpret, limiting their clinical utility. In this work we explore the explainability of an AI-based risk-prediction model by examining performance with respect to different characteristics of the future cancer. In particular, saliency maps were used to examine how often the model focused on regions coinciding with future lesions and assess the characteristics of future lesions that were most likely to coincide with AI-assigned high-risk regions. An AI model for breast cancer risk prediction was previously trained on the UK OPTIMAM dataset, achieving an AUROC of 0.70 for the task of 3-year risk prediction. Re-visiting the test set used to evaluate this model (n=31351 examinations), we obtained additional information about the future cancer cases (n=1053), including future cancer type (invasive/in-situ) and grade, and future lesion visual characteristics. Patient-level risk was compared across different cancer types and grades, and saliency maps were generated to perform a localisation study. The AI tool performed similarly for future invasive and in-situ disease, with no significant difference in risk score observed. Similarly, risk scores did not vary significantly with future cancer grade. Saliency map analysis showed that the AI-indicated high-risk regions coincided more often with the location of future obvious lesions or lesions with calcifications. The results in this work provide insights into the decision-making process of the AI risk prediction tool. Further work is required to explore additional lesion characteristics and further validate these findings.
Purpose: To calculate continuous breast density measures from processed images using deep learning. Method: Processed and unprocessed mammograms were collected for 3251 women attending the UK NHS Breast Screening Programme (NHSBSP). The breast density measures investigated included volumetric breast density, fibroglandular volume and breast volume. The ground truth for these measures was calculated using Volpara software on unprocessed mammograms. A deep learning model was trained and validated to predict each breast density measure. The performance of the deep learning model was assessed using a hold-out test set. Results: The breast volume and fibroglandular volume predicted with deep learning were strongly correlated with the ground truth (r=0.96 and r=0.88 respectively). The volumetric breast density had a Pearson correlation coefficient of 0.90. Conclusions: It is possible to predict volumetric breast density from processed images using deep learning.
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