Frontiers in oncology · 2026
Development and internal validation of a radiomics-clinical combined model for predicting axillary pathological complete response in clinically node-positive breast cancer patients after neoadjuvant chemotherapy.
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Yan Weitao, Lu Wenxuan, Dai Ying, Meng Xiangchao, Feng Kai
Аннотация
BACKGROUND: Accurate prediction of axillary pathological complete response (apCR) after neoadjuvant chemotherapy (NAC) in clinically node-positive (cN+) breast cancer patients may guide surgical de-escalation from axillary lymph node dissection to sentinel lymph node biopsy. This study aimed to develop and internally validate a combined radiomics-clinical prediction model for apCR.
METHODS: This single-center retrospective study enrolled 386 cN+ breast cancer patients (training, n = 270; validation, n = 116). Pre-NAC DCE-MRI radiomic features were extracted from primary tumors. LASSO regression selected eight features for Rad-score construction. Clinical predictors were identified via logistic regression. Model performance was evaluated using AUC, calibration metrics, and decision curve analysis.
RESULTS: The overall apCR rate was 43.5% (168/386). The combined model (tumor size, HER2 status, Ki-67, breast clinical complete response, and Rad-score) achieved a validation AUC of 0.703 (95% CI, 0.610-0.792). It significantly outperformed the radiomics-only model (ΔAUC = 0.094, P = 0.004) but not the clinical-only model (ΔAUC = 0.020, P = 0.713). The combined model showed a calibration slope of 0.811 and an intercept of 0.018, indicating moderate overfitting. Risk stratification showed monotonic gradients across tertiles (low 18.8%, intermediate 48.0%, high 58.8%). After bootstrap bias correction, the optimism-corrected training AUC of the combined model was 0.742.
CONCLUSIONS: The combined model demonstrated moderate discriminatory ability but did not add significant value over clinical predictors alone. The current misclassification rate precludes direct clinical application for surgical de-escalation. External multicenter validation is warranted.
METHODS: This single-center retrospective study enrolled 386 cN+ breast cancer patients (training, n = 270; validation, n = 116). Pre-NAC DCE-MRI radiomic features were extracted from primary tumors. LASSO regression selected eight features for Rad-score construction. Clinical predictors were identified via logistic regression. Model performance was evaluated using AUC, calibration metrics, and decision curve analysis.
RESULTS: The overall apCR rate was 43.5% (168/386). The combined model (tumor size, HER2 status, Ki-67, breast clinical complete response, and Rad-score) achieved a validation AUC of 0.703 (95% CI, 0.610-0.792). It significantly outperformed the radiomics-only model (ΔAUC = 0.094, P = 0.004) but not the clinical-only model (ΔAUC = 0.020, P = 0.713). The combined model showed a calibration slope of 0.811 and an intercept of 0.018, indicating moderate overfitting. Risk stratification showed monotonic gradients across tertiles (low 18.8%, intermediate 48.0%, high 58.8%). After bootstrap bias correction, the optimism-corrected training AUC of the combined model was 0.742.
CONCLUSIONS: The combined model demonstrated moderate discriminatory ability but did not add significant value over clinical predictors alone. The current misclassification rate precludes direct clinical application for surgical de-escalation. External multicenter validation is warranted.