Machine learning-based prediction of disease-free survival in breast cancer patients with non-pathological complete response after neoadjuvant chemotherapy: a retrospective multicenter cohort study.

This study aimed to construct a robust machine learning (ML) model for predicting the disease-free survival (DFS) and risk stratification of breast cancer (BC) patients with non-pathological complete response (non-PCR) after neoadjuvant chemotherapy (NAC). The model will facilitate the initiation of early interventions for high-risk patients. This retrospective multicenter cohort study included BC patients from two hospitals in China who received NAC but did not achieve PCR. Four ML algorithms were utilized to construct models based on patients' clinicopathological data, followed by a performance evaluation of these models. To improve the interpretability of the model, the shapley additive explanation (SHAP) method was employed to analyze the contribution of each feature to the predictive outcomes. A total of 463 non-PCR patients were included in the study. Of these, 385 patients were from Ruijin Hospital, affiliated with Shanghai Jiao Tong University, and were randomly split into a training cohort and an internal validation cohort in a 3:1 ratio for model development and preliminary performance evaluation. In addition, 78 patients enrolled from Jiaxing Women and Children's Hospital were assigned to the external validation cohort to evaluate the model's generalizability. Univariate and multivariate Cox regression analyses demonstrated that age, residual tumor size, Ki67 change, molecular subtype, and axillary lymph node metastasis were independent factors influencing DFS. Among the four ML models, the random survival forest (RSF) model showed the best performance, with a concordance index of 0.820 in the training cohort, 0.642 in the internal validation cohort, and 0.689 in the external validation cohort. Further analysis revealed that the RSF model had excellent discriminative ability with a high area under curve value, while its low Brier score indicated excellent calibration. Decision curve analysis indicated that the RSF model offered a higher clinical net benefit at various time points and effectively stratified risk, successfully identifying high-risk patients. SHAP analysis underscored residual tumor size as the most influential predictive feature. The RSF model can effectively predict DFS and risk of BC patients with non-PCR following NAC, offering a critical reference for developing individualized treatment strategies.