Wu Shangrong, Ma Xudong, Liang Zhengxin, Jiang Yuchen, Chen Shuaiqi, Sun Guangyu, Chen Kaifei, Liu Ranlu
Department of Urology, The Second Hospital of Tianjin Medical University, Tianjin, China.
Tianjin Institute of Urology, The Second Hospital of Tianjin Medical University, Tianjin, China.
Prostate. 2023 Dec;83(16):1537-1548. doi: 10.1002/pros.24612. Epub 2023 Aug 17.
The specific risk factors contributing to the development of osteoporosis and the appropriate timing of treatment in Chinese prostate cancer (PCa) patients remain unclear. Our objective was to develop and validate a nomogram capable of predicting the occurrence of osteoporosis in PCa patients.
We conducted a cross-sectional study with PCa patients attending the Second Hospital of Tianjin Medical University, collecting data from June 2021 to February 2023. The patients were divided into training and validation sets in a 7:3 ratio. The LASSO regression was used to identify the most relevant predictive variables, and the multivariable logistic regression was used to construct the nomogram. The nomogram's performance was validated through receiver operating characteristic (ROC) curves, C-index, calibration curves, and decision curve analysis (DCA) in both the training and validation sets.
We collected data from a total of 596 patients and then constructed the nomogram using age, body mass index, hemoglobin, vitamin D3, testosterone, and androgen deprivation therapy duration. The C-index of the nomogram was 0.923 in the training set and 0.859 in the validation set. The nomogram showed good consistency in both sets. DCA demonstrated the clinical benefit of the nomogram across various prediction thresholds. Furthermore, a separate nomogram was constructed to predict bone loss in patients undergoing ADT, exhibiting equally favorable diagnostic performance and clinical benefit.
This study constructed two reliable nomograms to predict osteoporosis and bone loss, integrating personal health information and PCa-specific treatment data. These nomograms offer an easy and individualized approach to predict the occurrence of osteoporosis and bone loss in PCa patients.
中国前列腺癌（PCa）患者发生骨质疏松的具体危险因素以及合适的治疗时机仍不明确。我们的目标是开发并验证一种能够预测PCa患者骨质疏松发生情况的列线图。
我们对天津医科大学第二医院的PCa患者进行了一项横断面研究，收集2021年6月至2023年2月的数据。患者按7:3的比例分为训练集和验证集。采用LASSO回归识别最相关的预测变量，采用多变量逻辑回归构建列线图。通过训练集和验证集中的受试者工作特征（ROC）曲线、C指数、校准曲线和决策曲线分析（DCA）对列线图的性能进行验证。
我们共收集了596例患者的数据，然后使用年龄、体重指数、血红蛋白、维生素D3、睾酮和雄激素剥夺治疗持续时间构建列线图。列线图在训练集中的C指数为0.923，在验证集中为0.859。列线图在两个集合中均显示出良好的一致性。DCA证明了列线图在各种预测阈值下的临床益处。此外，还构建了一个单独的列线图来预测接受雄激素剥夺治疗（ADT）患者的骨质流失，其诊断性能和临床益处同样良好。
本研究构建了两个可靠的列线图来预测骨质疏松和骨质流失，整合了个人健康信息和PCa特异性治疗数据。这些列线图为预测PCa患者骨质疏松和骨质流失的发生提供了一种简单且个性化的方法。
