Preoperative prediction of breast cancer tumor-infiltrating lymphocytes levels with multiphase tumoral–peritumoral DCE-MRI radiomics and conventional features

Tumor-infiltrating lymphocytes (TILs) are biomarkers of the breast cancer immune microenvironment associated with treatment response and prognosis, but their assessment relies mainly on surgical histopathology. We aimed to develop and validate a preoperative model combining multiphase tumoral–peritumoral DCE-MRI radiomics with conventional features to predict TIL levels. This retrospective two-center study included 495 patients who underwent preoperative breast DCE-MRI followed by surgical resection as initial treatment. TILs were assessed on surgical specimens using a prespecified 10% cutoff commonly adopted in previous breast MRI-based TIL studies. Radiomic features from tumoral and 3-mm peritumoral regions across six DCE-MRI phases were combined with clinical, semantic MRI, and biopsy-derived features. Model performance was assessed by discrimination, calibration, incremental value, and clinical utility, and Shapley additive explanations (SHAP) analysis was used to assess feature contributions. The combined model achieved the AUC of 0.882 (95% confidence interval [CI]: 0.840–0.920), 0.777 (95% CI: 0.672–0.866), and 0.738 (95% CI: 0.637–0.826) in the training, internal validation, and external validation sets, respectively. In external validation, the combined model showed significant integrated discrimination improvement (IDI) over the conventional model (IDI = 0.105, P = 0.005) and the tumoral–peritumoral model (IDI = 0.098, P = 0.001). Decision curve analysis showed greater net benefit across most evaluated threshold probabilities. SHAP analysis identified later-phase peritumoral radiomic features as the dominant predictive factors for TIL estimation. Integrating multiphase tumoral–peritumoral DCE-MRI radiomics with conventional preoperative features showed potential for predicting TIL levels, with late-phase peritumoral features dominating the predictive performance. This model provides a promising preoperative tool for immune-related risk stratification and individualized multidisciplinary treatment planning.