Mutual information-based radiomic feature selection with SHAP explainability for breast cancer diagnosis

Abstract

Breast cancer is a prevalent concern for women globally, with misdiagnosis potentially leading to detrimental outcomes. Early detection is crucial, often reliant on medical imaging analysis. Digital Breast Tomosynthesis (DBT) is a promising modality, addressing limitations of traditional mammograms. However, diagnosing breast cancer involves subjective visual examination, leading to inaccuracies. Radiomics, applied in various imaging modalities such as MRI, and digital mammography, remains underutilized in DBT. This study introduces a Mutual Information-based Radiomic Feature Selection (MIRFS) framework for DBT breast cancer evaluation followed by SHAP explanations. Selected features were assessed using machine learning algorithms, with Random Forest achieving 92% accuracy in lesion classification. MIRFS demonstrates significant performance improvements, addressing subjectivity and enhancing diagnostic accuracy through explainability. SHAP methodology elucidates feature importance, aiding model interpretation. Compared to deep learning methods, MIRFS outperforms both deep learning and existing machine learning approaches, promising advancements in breast cancer diagnosis and treatment. • Radiomics remains underutilized in DBT. • Study introduces MIRFS a novel framework for breast cancer diagnosis using DBT. • The radiomic features were assessed using Machine Learning for lesion diagnosis. • MIRFS shows excellent performance compared to the state-of-the-art. • SHAP elucidates feature importance, aiding model interpretation.