Predicting the risk of bone metastases in patients with newly diagnosed prostate cancer – Development and validation of a nomogram model - Wei Xu; Guoyu Zhu; Xiaoxiang Wang; Xuebing Yan; Fujun Wang; Shanyi Li; Wenji Li (PLoS ONE, 2025)
Objectives The aim of this study was to develop and validate a nomogram model that predicts the risk of bone metastasis (BM) in a prostate cancer (PCa) population. Methods We retrospectively collected and analyzed the clinical data of patients with pathologic diagnosis of PCa from January 1, 2013 to December 31, 2022 in two hospitals in Yangzhou, China. Patients from the Affiliated Hospital of Yangzhou University were divided into a training set and patients from the Affiliated Clinical College of Traditional Chinese Medicine of Yangzhou University were divided into a validation set. Chi-square test, independent sample t-test, and logistic regression were used to screen key risk factors. Receiver operating characteristic (ROC) curves, c-index, calibration curves, and decision curves analysis (DCA) were used for the validation, calibration, clinical benefit assessment, and external validation of nomogram models. Results A total of 204 cases were collected from the Affiliated Hospital of Yangzhou University, including 64 cases diagnosed as PCa BM and 50 cases collected from the Affiliated Clinical College of Traditional Chinese Medicine of Yangzhou University, including 12 cases diagnosed as PCa BM. Results showed that history of alcohol consumption, prostate stiffness on Digital rectal examination(DRE), prostate nodules on DRE, FIB, ALP, cTx, and Gleason score were high-risk factors for BM in PCa and nomogram was established. The c-index of the final model was 0.937 (95% CI: 0.899–0.975). And the model was validated by external validation set (c-index: 0.929). The ROC curves and calibration curves showed that the nomogram had good predictive accuracy, and DCA showed that the nomogram had good clinical applicability. Conclusions Our study identified seven high-risk factors for BM in PCa and these factors would provide a theoretical basis for early clinical prevention of PCa BM.