Xu Wei, Zhu Guoyu, Wang Xiaoxiang, Yan Xuebing, Wang Fujun, Li Shanyi, Li Wenji
Danyang Hospital of Traditional Chinese Medicine, Zhenjiang, Jiangsu, PR. China.
Institute of Translational Medicine, Medical College, Yangzhou University, Yangzhou, Jiangsu, PR. China.
PLoS One. 2025 Jan 30;20(1):e0318051. doi: 10.1371/journal.pone.0318051. eCollection 2025.
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.
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.
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.
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.
本研究旨在开发并验证一种预测前列腺癌（PCa）患者发生骨转移（BM）风险的列线图模型。
我们回顾性收集并分析了2013年1月1日至2022年12月31日期间在中国扬州两家医院经病理诊断为PCa的患者的临床数据。扬州大学附属医院的患者被分为训练集，扬州大学附属中医临床学院的患者被分为验证集。采用卡方检验、独立样本t检验和逻辑回归筛选关键风险因素。采用受试者操作特征（ROC）曲线、c指数、校准曲线和决策曲线分析（DCA）对列线图模型进行验证、校准、临床获益评估和外部验证。
扬州大学附属医院共收集到204例病例，其中64例被诊断为PCa BM；扬州大学附属中医临床学院收集到50例病例，其中12例被诊断为PCa BM。结果显示，饮酒史、直肠指检（DRE）时前列腺硬度、DRE时前列腺结节、纤维蛋白原（FIB）、碱性磷酸酶（ALP）、临床分期（cTx）和 Gleason评分是PCa发生BM的高危因素，并建立了列线图。最终模型的c指数为0.937（95%CI：0.899 - 0.975）。并通过外部验证集对模型进行验证（c指数：0.929）。ROC曲线和校准曲线表明列线图具有良好的预测准确性，DCA表明列线图具有良好的临床适用性。
我们的研究确定了PCa发生BM的七个高危因素，这些因素将为PCa BM的早期临床预防提供理论依据。
