Light Alexander, Lophatananon Artitaya, Keates Alexandra, Thankappannair Vineetha, Barrett Tristan, Dominguez-Escrig Jose, Rubio-Briones Jose, Benheddi Toufik, Olivier Jonathan, Villers Arnauld, Babureddy Kirthana, Abdelmoteleb Haitham, Gnanapragasam Vincent J
Division of Urology, Department of Surgery, University of Cambridge, Cambridge CB2 0QQ, UK.
Department of Urology, Cambridge University Hospitals NHS Foundation Trust, Cambridge CB2 0QQ, UK.
J Clin Med. 2022 Dec 27;12(1):216. doi: 10.3390/jcm12010216.
For men with newly diagnosed prostate cancer, we aimed to develop and validate a model to predict the risk of progression on active surveillance (AS), which could inform more personalised AS strategies. In total, 883 men from 3 European centres were used for model development and internal validation, and 151 men from a fourth European centre were used for external validation. Men with Cambridge Prognostic Group (CPG) 1-2 disease at diagnosis were eligible. The endpoint was progression to the composite endpoint of CPG3 disease or worse (≥CPG3). Model performance at 4 years was evaluated through discrimination (C-index), calibration plots, and decision curve analysis. The final multivariable model incorporated prostate-specific antigen (PSA), Grade Group, magnetic resonance imaging (MRI) score (Prostate Imaging Reporting & Data System (PI-RADS) or Likert), and prostate volume. Calibration and discrimination were good in both internal validation (C-index 0.742, 95% CI 0.694-0.793) and external validation (C-index 0.845, 95% CI 0.712-0.958). In decision curve analysis, the model offered net benefit compared to a 'follow-all' strategy at risk thresholds of ≥0.08 and ≥0.04 in development and external validation, respectively. In conclusion, our model demonstrated good accuracy and clinical utility in predicting the progression on AS at 4 years post-diagnosis. Men with lower risk predictions could subsequently be offered less-intense surveillance. Further external validation in larger cohorts is now required.
对于新诊断出前列腺癌的男性患者，我们旨在开发并验证一种模型，以预测积极监测（AS）过程中的疾病进展风险，从而为更具个性化的AS策略提供依据。总共883名来自3个欧洲中心的男性用于模型开发和内部验证，151名来自第四个欧洲中心的男性用于外部验证。诊断时处于剑桥预后组（CPG）1-2期疾病的男性符合条件。终点是进展为CPG3期疾病或更严重情况（≥CPG3）的复合终点。通过区分度（C指数）、校准图和决策曲线分析评估4年时的模型性能。最终的多变量模型纳入了前列腺特异性抗原（PSA）、分级组、磁共振成像（MRI）评分（前列腺影像报告和数据系统（PI-RADS）或李克特量表）以及前列腺体积。内部验证（C指数0.742，95%置信区间0.694-0.793）和外部验证（C指数0.845，95%置信区间0.712-0.958）中的校准和区分度均良好。在决策曲线分析中，与“全部随访”策略相比，该模型在开发和外部验证中的风险阈值分别≥0.08和≥0.04时提供了净效益。总之，我们的模型在预测诊断后4年AS过程中的进展方面显示出良好的准确性和临床实用性。风险预测较低的男性患者随后可接受强度较低的监测。现在需要在更大的队列中进行进一步的外部验证。
