MRI-based radiomics with SHAP interpretation for preoperative prediction of upstaging in ductal carcinoma in situ: a comparative study of intratumoral, peritumoral, habitat, and fusion models

Abstract

Background Preoperative prediction of upstaging in ductal carcinoma in situ (DCIS) is crucial to avoid unnecessary sentinel lymph node biopsy (SLNB) in low-risk patients. We aimed to develop and validate an interpretable magnetic resonance imaging (MRI)-based radiomics model for this purpose and to systematically compare the predictive value of intratumoral, peritumoral, and habitat-based features. Methods This retrospective study included 108 women with biopsy-proven DCIS. Radiomics features were extracted from intratumoral and peritumoral regions (2, 4, 6 mm) on dynamic contrast-enhanced MRI. Six models (clinical, intratumoral, peritumoral, habitat, feature-fusion, image-fusion) were developed and compared using multiple machine learning classifiers. The best-performing model was validated on an independent test set (n=33). Model interpretability was achieved using SHapley Additive exPlanations (SHAP). Results The image-fusion model integrating intratumoral and 2-mm peritumoral features showed promising performance, with an area under the curve (AUC) of 0.892 [95% confidence interval (CI): 0.817–0.967] in the training set and 0.864 (95% CI: 0.735–0.992) in the test set. SHAP analysis identified textural heterogeneity as a key predictor. Using predefined radiomics score (Rad-score) thresholds derived from the training set, the high-sensitivity threshold achieved a negative predictive value of 100% (10/10) and the high-specificity threshold achieved a positive predictive value of 61.5% (8/13) in the independent test set. An exploratory ultra-low-risk threshold (Rad-score <0.25) identified a subgroup of 5 out of 33 patients (15.2%) with no upstaging. Conclusions The proposed MRI-based radiomics model may assist in preoperative risk stratification for DCIS upstaging, but these findings are exploratory and require prospective multicenter validation.