Han Yuefu, Wen Xingqiao, Chen Dong, Li Xiaojuan, Leng Qu, Wen Yuehui, Li Jun, Zhu Weian
Department of Urology, Shenzhen Hospital, The Third College of Clinical Medicine, Southern Medical University, Shenzhen, 518100 Guangdong, China.
Department of Urology, Zhujiang Hospital, Southern Medical University, Guangzhou, 510282 Guangdong, China.
J Oncol. 2022 Mar 22;2022:6358707. doi: 10.1155/2022/6358707. eCollection 2022.
This study sought to perform a survival analysis and construct a prognostic nomogram model based on the Gleason grade, total prostate-specific antigen (tPSA), alkaline phosphate (ALP), and TNM stage in patients with prostate cancer (PCa).
The progression-free survival (PFS) of 255 PCa patients was analyzed in this study. The prognostic value of tPSA and ALP was evaluated using the Kaplan-Meier survival curves and Cox regression analysis, and a nomogram model based on the Gleason grade, tPSA, ALP, and TNM stage was further established for PFS prediction in PCa patients.
PCa patients with different Gleason grades, tPSA and ALP levels, and TNM stages presented distinct PFS. The Gleason grade, tPSA, ALP, and TNM stage were four independent prognostic indicators. The C-index of the established nomogram was 0.705 for PFS in the test cohort and 0.687 for the validation cohort, and the calibration curves indicated a good consistency between predicted and actual PFS in PCa patients.
The data of this study demonstrated that the Gleason grade, tPSA, ALP, and TNM stage of PCa patients are independently correlated with PFS, and a nomogram model based on these indicators may be valuable for the PFS prediction in PCa patient.
本研究旨在对前列腺癌(PCa)患者进行生存分析,并基于Gleason分级、总前列腺特异性抗原(tPSA)、碱性磷酸酶(ALP)和TNM分期构建预后列线图模型。
本研究分析了255例PCa患者的无进展生存期(PFS)。采用Kaplan-Meier生存曲线和Cox回归分析评估tPSA和ALP的预后价值,并进一步建立基于Gleason分级、tPSA、ALP和TNM分期的列线图模型,用于预测PCa患者的PFS。
不同Gleason分级、tPSA和ALP水平以及TNM分期的PCa患者呈现出不同的PFS。Gleason分级、tPSA、ALP和TNM分期是四个独立的预后指标。在测试队列中,所建立列线图的PFS的C指数为0.705,在验证队列中为0.687,校准曲线表明PCa患者预测的和实际的PFS之间具有良好的一致性。
本研究数据表明,PCa患者的Gleason分级、tPSA、ALP和TNM分期与PFS独立相关,基于这些指标的列线图模型可能对PCa患者的PFS预测有价值。