Explainable Radiomics‐Based Model for Automatic Image Quality Assessment in Breast Cancer Dce Mri Data

This study aims to develop an explainable radiomics-based model for the automatic as-sessment of image quality in breast cancer Dynamic Contrast-Enhanced Magnetic Reso-nance Imaging (DCE-MRI) data. A cohort of 280 images obtained from a public database was annotated by two clinical experts, resulting in 110 high-quality and 110 low-quality images. The proposed methodology involved the extraction of 819 radiomic features and two No-Reference image quality metrics per patient, using both the whole image and the background as regions of interest. Feature extraction was performed under two scenarios: (i) from a sample of 12 slices per patient, and (ii) from the middle slice of each patient. Following model training, a range of machine learning classifiers was applied with ex-plainability assessed through SHapley Additive Explanations (SHAP). The best perfor-mance was achieved in the second scenario, where combining features from the whole image and background with a support vector machine classifier yielded sensitivity, speci-ficity, accuracy, and AUC values of 85.51%, 80.01%, 82.76%, and 89.37%, respectively. This proposed model demonstrates potential for integration into clinical practice and may also serve as a valuable resource for large-scale repositories and subgroup analyses aimed at ensuring fairness and explainability.