Construction and validation of a predictive model of pathological complete response combined with MRI and tumor indicators for HER2-positive breast cancer after neoadjuvant targeted therapy.

Accurate models for predicting pathological complete response (pCR) after neoadjuvant therapy (NAT) are increasingly needed in patients with human epidermal growth factor receptor 2 (HER2)-positive breast cancer (BC). In the present study, a nomogram was developed to estimate the probability of achieving a pCR in this population. Clinical data were retrospectively and prospectively collected from patients with HER2-positive BC at three time points: Before NAT, after the first cycle of neoadjuvant targeted therapy and after the completion of NAT before surgery. Logistic regression analysis was performed to identify independent predictors of pCR, and these variables were used to construct a predictive model and corresponding nomogram. Model performance was evaluated using calibration curves, decision curve analysis, receiver operating characteristic (ROC) curves and the area under the ROC curve (AUC), with retrospective data and prospective data used for internal and external validations, respectively. Logistic regression analysis of the retrospective cohort identified eight predictors associated with pCR, which were incorporated into a concise nomogram. Internal validation demonstrated good calibration and strong predictive performance, with an AUC value of 0.886 (P<0.001), sensitivity of 0.822 and specificity of 0.818. External validation further confirmed the excellent discriminatory ability of the model, yielding an AUC value of 0.961 (P<0.001), sensitivity of 1.000 and specificity was 0.875. Overall, this nomogram, which integrates multiple clinically relevant factors, may serve as a useful tool for predicting post-NAT pCR in patients with HER2-positive BC and may support more precise treatment decision-making in clinical practice.