Clinical-radiomics model for predicting internal mammary lymph node metastasis in operable breast cancer patients.

Objective: Although preoperative prediction of axillary lymph nodes status has been achieved using radiomics and combined models, there is a dearth of research on internal mammary lymph node (IMN) metastasis status prediction. We developed a predictive model by combining clinicopathological factors with preoperative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) radiomics to accurately predict IMN metastasis in breast cancer. Methods: Patients who had no evidence of IMN metastasis on preoperative images but underwent internal mammary sentinel lymph node biopsy (IM-SLNB) were included in this study. Preoperative DCE-MRI and clinicopathological data of 124 patients with breast cancer were obtained, to developed Clinical, radiomics, and clinical-radiomics models, separately. Decision curve analysis (DCA) was employed to assess the models' clinical applicability. Results: The resulting area under the curves (AUCs) were 0.913, 0.831, 0.964 for the clinical model, the radiomics model, and the clinical-radiomics model, respectively. The Delong test revealed significant differences in the receiver operating characteristic (ROC) curves only between the clinical and clinical-radiomics models (all P<0.05). DCA substantiated the clinical-radiomics model's optimal predictive efficiency, enhanced discriminatory ability, and maximum benefit. The AUC (95% confidence interval: 0.935-0.993) of the clinical-radiomics model is 0.964. Repeated k-fold cross validation showed that average accuracy and Standard deviation of clinical-radiomics model are 90.23% and 8.45%, respectively. And the calibration slope of clinical-radiomics model is 1.08(p=0.071). Conclusions: Although the clinical model was effective in predicting IMN status, the addition of DCE