Diagnosing breast masses in BI-RADS 4: construction and interpretation of an intralesional and perilesional radiomics nomogram based on digital breast tomosynthesis

To evaluate the diagnostic performance of a digital breast tomosynthesis (DBT)-based, interpretable radiomics nomogram for differentiating between benign and malignant Breast Imaging Reporting and Data System (BI-RADS) category 4 masses. This retrospective study enrolled 312 patients with BI-RADS 4 lesions assessed via DBT. Radiomics features were extracted from intralesional and perilesional regions of interest (ROIs), and used to construct three models: Radscore_IL, Radscore_PL, and Radscore_Combine. Univariate and multivariate logistic regression analyses identified independent predictors, which were integrated into clinical and nomogram models. Model performance and interpretability were assessed using receiver operating characteristic (ROC) curves, calibration curves, decision curve analysis (DCA), and Shapley Additive Explanations (SHAP) values. Among the three radiomics models, Radscore_Combine demonstrated superior performance, with area under the curve (AUC) values of 0.935 (training) and 0.888 (validation). Age and BI-RADS 4 subclassification emerged as independent clinical predictors and were included in the clinical model. The final nomogram, incorporating Radscore_Combine, age, and BI-RADS 4 subclassification, achieved AUCs of 0.960 and 0.942 in the training and validation datases, respectively. DCA confirmed significant clinical utility of the nomogram. SHAP analysis enhanced interpretability by illustrating individual feature contributions to model predictions. The DBT-based radiomics nomogram offers high diagnostic accuracy and good interpretability, making it a valuable tool for differentiating benign from malignant BI-RADS 4 masses. This DBT-based radiomics nomogram accurately differentiates benign and malignant BI-RADS 4 breast lesions, supporting clinical decision-making to optimize management of these ambiguous findings. Developed an interpretable nomogram using DBT-based Radscore and clinical features. Combined intralesional and perilesional features improved diagnostic accuracy. Nomogram accurately distinguished benign from malignant BI-RADS 4 breast masses. SHAP analysis enhanced model transparency and feature interpretability.