基于病理特征的前列腺癌根治术后生化复发预测模型

doi:10.3969/j.issn.1006-7795.2026.02.007

首都医科大学学报 ›› 2026, Vol. 47 ›› Issue (2): 267-274.doi: 10.3969/j.issn.1006-7795.2026.02.007

• 肿瘤演进机制与临床防治 • 上一篇下一篇

基于病理特征的前列腺癌根治术后生化复发预测模型

熊天宇^1,2,赵有权^1,2,谢萍^1,2,3*,牛亦农^1,2*

1.首都医科大学附属北京友谊医院泌尿外科，北京 100050； 2.北京市卫生健康委员会泌尿外科研究所，北京 100050； 3.首都医科大学基础医学院细胞生物学系，北京 100069

收稿日期:2025-10-20 修回日期:2025-12-29 出版日期:2026-04-21 发布日期:2026-04-21
通讯作者: 谢萍,牛亦农 E-mail:xiep@ccmu.edu.cn; niuyinong@mail.ccmu.edu.cn
基金资助:
国家临床重点专科建设项目（20250829），北京市临床重点专科建设项目（20240930）。

Prediction model for biochemical recurrence after radical prostatectomy based on pathological features

Xiong Tianyu^1,2, Zhao Youquan^1,2, Xie Ping^1,2,3*, Niu Yinong^1,2*

1.Department of Urology, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China; 2. Institute of Urology, Beijing Municipal Health Commission, Beijing 100050, China; 3. Department of Cell Biology, School of Basic Medical Sciences, Capital Medical University, Beijing 100069, China

Received:2025-10-20 Revised:2025-12-29 Online:2026-04-21 Published:2026-04-21
Supported by:
This study was supported by National Key Clinical Specialty Development Project（20250829）, Beijing Key Clinical Specialty Development Project（20240930）.

摘要/Abstract

摘要： 目的寻找影响前列腺癌患者术后生化复发的病理特征，并构建术后生化复发的风险预测模型。方法回顾性纳入237例接受根治性前列腺切除术的前列腺癌患者，收集患者临床资料及术后病理资料。根据门诊随访结果分析患者生化复发情况，应用Kaplan-Meier曲线分析不同病理特征对患者无生化复发生存期（biochemical recurrence-free survival，BRS）的影响。应用Cox回归分析筛选影响BRS的危险因素，并利用筛选出的变量构建预测12个月BRS的预测模型，绘制列线图，计算C-index并绘制相关校正曲线。结果肿瘤病理分期、国际泌尿病理学会分级分组、基底和膀胱颈切缘阳性以及磷酸酶与张力蛋白同源物（phosphatase and tensin homolog，PTEN）缺失突变是影响患者术后BRS的独立危险因素（P值均<0.05），基于上述分析所筛选的变量，建立患者术后12个月BRS的预测模型及列线图，区分度良好（C-index=0.810）。结论基于病理特征的前列腺癌术后生化复发预测模型为临床提供了易于获取的个体化预后评估工具，有助于指导术后辅助治疗决策与密集监测方案的制定。

关键词: 前列腺癌, 根治性前列腺切除术, 病理特征, 生化复发, 列线图, 预后

Abstract: Objective To explore pathological features that might affect biochemical recurrence in patients with prostate cancer following radical prostatectomy, and to establish a risk prediction model for biochemical recurrence. Methods A total of 237 prostate cancer patients who underwent radical prostatectomy were retrospectively enrolled. Clinical and pathological data were retrieved. Biochemical recurrence data were collected from outpatient follow-up records, and Kaplan-Meier analysis was performed to assess the influence of different pathological features on biochemical recurrence-free survival (BRS). Cox regression analyses were performed to identify risk factors for BRS. A prediction model and nomogram for 12-month BRS were established with these risk factors. Results Pathological stage, International Society of Urological Pathology (ISUP) grade group, positive basal and bladder neck margin, and phosphatase and tensin homolog (PTEN) deletion were independent risk factors for BRS. Based on these factors, a prediction model and nomogram for 12-month BRS were built, and showed good discrimination (C-index = 0.810). Conclusion The biochemical recurrence prediction model for prostate cancer based on pathological features provides an easily accessible personalized prognostic assessment tool for clinical practice, which helps guide postoperative adjuvant treatment decisions and the formulation of intensive surveillance protocols.

Key words: prostate cancer, radical prostatectomy, pathological feature, biochemical recurrence, nomogram, prognosis

中图分类号:

熊天宇, 赵有权, 谢萍, 牛亦农. 基于病理特征的前列腺癌根治术后生化复发预测模型[J]. 首都医科大学学报, 2026, 47(2): 267-274.

Xiong Tianyu, Zhao Youquan, Xie Ping, Niu Yinong. Prediction model for biochemical recurrence after radical prostatectomy based on pathological features[J]. Journal of Capital Medical University, 2026, 47(2): 267-274.

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基于病理特征的前列腺癌根治术后生化复发预测模型

Prediction model for biochemical recurrence after radical prostatectomy based on pathological features

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