Construction of a Nomogram Model for Predicting Pathologic Complete Response in Breast Cancer Neoadjuvant Chemotherapy Based on the Pan-Immune Inflammation Value - Zhuowan Tian; Yiqing Xi; Mengting Chen; Meishun Hu; Fangfang Chen; Lei Wei; Jingwei Zhang (Current Oncology)
Background: The pan-immune inflammation value (PIV) has unclear predictive utility for pathologic complete response (pCR) in breast cancer patients undergoing neoadjuvant chemotherapy (NAC). This study aimed to evaluate the PIV’s predictive value and develop a nomogram integrating PIV for individualized pCR prediction. Methods: In a retrospective multicenter study of 507 NAC-treated patients (training cohort: 357; validation cohort: 150), independent predictors of pCR were identified through univariate and multivariate logistic regression. A nomogram was constructed and validated using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). Net reclassification improvement (NRI) and integrated discrimination improvement (IDI) evaluated the improvement in performance after incorporating the PIV indicator. Results: The high PIV patients (cutoff: 316.533) had significantly lower pCR rates than the low PIV patients (p < 0.001). The nomogram incorporating PIV, estrogen receptor (ER), human epidermal growth factor receptor-2 (Her2), tumor diameter, clinical node stage, and chemotherapy regimen showed excellent discrimination (training cohort area under the curve (AUC): 0.861, 95% confidence interval (CI): 0.821–0.901; validation cohort AUC: 0.815, 95% CI: 0.748–0.882). The calibration curves demonstrate high prediction accuracy (Hosmer–Lemeshow test: p > 0.05), while DCA, NRI (0.341, 95% CI: 0.181–0.500), and IDI (0.017, 95% CI: 0.004–0.029) confirm clinical utility. Conclusions: The PIV is an independent predictor of pCR, and the PIV-based nomogram provides a reliable tool for optimizing NAC response prediction in breast cancer.