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Breast density is an important consideration for breast cancer screening, where the amount of fibroglandular tissue in the breast can mask the detection of cancers. BI-RADS density grade estimates can result in high variability, prompting the need for an objective and reproducible assessment of breast density and tissue complexity. In this study, we investigate the utility of radiomic features to quantify texture and shape characteristics of tissue-specific regions of interest. Using Explainable AI (XAI), we identify key features for distinguishing breast density grade by computing each feature’s SHapley Additive exPlanations (SHAP) value. SHAP values measure a feature’s importance on the classifier’s prediction; the top SHAP value features from each density grade are selected as inputs to our classifier model. These features also identify relationships with clinical knowledge of breast cancer pathophysiology. Logistic regression classifiers fit to our radiomic features achieved a mean AUC per density grade class of [A : 0.949±0.055,B : 0.877±0.055,C : 0.884±0.023,D : 0.893±0.076] over nested five-fold cross-validation. Pooled confusion matrices show that class imbalance can affect the proposed method, particularly in density grades A and D. Furthermore, unsupervised clustering using Uniform Manifold Approximation and Projection (UMAP) on our radiomic feature set show inherent separability of the four density grades. The results of our preliminary analysis highlight how clinically interpretable radiomic features show promise as an important tool for breast cancer screening by preserving predictive performance while introducing AI explainability.
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