A Prognostic Nutritional Index-Based Nomogram to Predict Breast Cancer Metastasis: A Retrospective Cohort Validation

Zhimin Chen,1,* Honglan Gao,1,* Mingwen Cheng,2 Chenglin Song3 1Department of Clinical Nutrition, The Yancheng Clinical College of Xuzhou Medical University, The First People’s Hospital of Yancheng, Yancheng, Jiangsu, 224000, People’s Republic of China; 2Department of Public Health, Yancheng Center for Disease Control and Prevention, Yancheng, Jiangsu, 224000, People’s Republic of China; 3Department of Clinical Nutrition, The Second People’s Hospital of Lianyungang, Lianyungang Second People’s Hospital Affiliated to Kangda College of Nanjing Medical University, Lianyungang, Jiangsu, 222006, People’s Republic of China*These authors contributed equally to this workCorrespondence: Chenglin Song, Department of Clinical Nutrition, The Second People’s Hospital of Lianyungang, Lianyungang Second People’s Hospital Affiliated to Kangda College of Nanjing Medical University, No. 41, Hailian East Road, Lianyungang, Jiangsu, 222006, People’s Republic of China, Email [email protected]: The prognostic nutritional index (PNI) is significantly associated with the prognosis of breast cancer (BC). However, the relationship between PNI and BC metastasis has not yet been thoroughly studied. This study aims to explore the role of PNI in BC metastasis and develop a predictive nomogram model.Methods: A retrospective cohort of 311 BC patients was analyzed. The restricted cubic spline (RCS) was utilized to explore the nonlinear relationships between PNI, geriatric nutritional risk index (GNRI), neutrophil percentage-to-albumin ratio (NPAR), hemoglobin, albumin, lymphocyte, and platelet (HALP) ratio and BC metastasis. Multivariate logistic regression analysis was conducted to identify the influencing factors of BC metastasis. A nomogram model was established and internally validated. The performance and clinical applicability of the model were assessed through the area under the receiver operating characteristic (ROC) curve (AUC), calibration curve, Hosmer-Lemeshow test, and decision curve analysis (DCA).Results: RCS analysis demonstrated nonlinear associations between PNI and HALP with BC metastasis (P for nonlinear < 0.05). PNI and other factors such as T and N stage etc. were identified as independent influencing factors for BC metastasis. The nomogram based on these factors demonstrated strong predictive ability, with the AUCs of 0.85 (95% confidence interval [CI] 0.79, 0.91) and 0.82 (95% CI 0.71, 0.93) in the training and validation set, respectively. The calibration curve, Hosmer-Lemeshow test, and DCA further confirmed its clinical utility.Conclusion: PNI is an independent predictor of BC metastasis. This PNI-based nomogram provides a practical and user-friendly tool for assessing BC metastasis risk.Keywords: prognostic nutritional index, breast cancer, metastasis, nomogram