Radiological and Biological Dictionary of Radiomics Features: Addressing Understandable AI Issues in Personalized Breast Cancer; Dictionary Version BM1.0

• URL: http://arxiv.org/abs/2507.16041v1
• Date: Mon, 21 Jul 2025 20:17:20 GMT
• Title: Radiological and Biological Dictionary of Radiomics Features: Addressing Understandable AI Issues in Personalized Breast Cancer; Dictionary Version BM1.0
• Authors: Arman Gorji, Nima Sanati, Amir Hossein Pouria, Somayeh Sadat Mehrnia, Ilker Hacihaliloglu, Arman Rahmim, Mohammad R. Salmanpour,
• Abstract summary: Radiomics-based AI models show promise for breast cancer diagnosis but often lack interpretability.<n>This study proposes a dual-dictionary framework to address the gap between radiomic features (RF) and the standardized BI-RADS lexicon.
• Score: 0.895629264339744
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Radiomics-based AI models show promise for breast cancer diagnosis but often lack interpretability, limiting clinical adoption. This study addresses the gap between radiomic features (RF) and the standardized BI-RADS lexicon by proposing a dual-dictionary framework. First, a Clinically-Informed Feature Interpretation Dictionary (CIFID) was created by mapping 56 RFs to BI-RADS descriptors (shape, margin, internal enhancement) through literature and expert review. The framework was applied to classify triple-negative breast cancer (TNBC) versus non-TNBC using dynamic contrast-enhanced MRI from a multi-institutional cohort of 1,549 patients. We trained 27 machine learning classifiers with 27 feature selection methods. SHapley Additive exPlanations (SHAP) were used to interpret predictions and generate a complementary Data-Driven Feature Interpretation Dictionary (DDFID) for 52 additional RFs. The best model, combining Variance Inflation Factor (VIF) selection with Extra Trees Classifier, achieved an average cross-validation accuracy of 0.83. Key predictive RFs aligned with clinical knowledge: higher Sphericity (round/oval shape) and lower Busyness (more homogeneous enhancement) were associated with TNBC. The framework confirmed known imaging biomarkers and uncovered novel, interpretable associations. This dual-dictionary approach (BM1.0) enhances AI model transparency and supports the integration of RFs into routine breast cancer diagnosis and personalized care.

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