Arriaga-Izabal Diego, Morales-Lazcano Francisco, Canizalez-Román Adrian
Research Department, School of Medicine, Autonomous University of Sinaloa, Culiacan, México.
Prostate. 2025 May;85(6):513-523. doi: 10.1002/pros.24854. Epub 2025 Jan 13.
Prostate cancer (PCa) is the second most common cancer in men worldwide, with significant incidence and mortality, particularly in Mexico, where diagnosis at advanced stages is common. Early detection through screening methods such as digital rectal examination and prostate-specific antigen testing is essential to improve outcomes. Despite current efforts, compliance with prostate screening (PS) remains low due to several barriers. This study aims to develop and validate a predictive model for PCa screening compliance in Mexican men.
Retrospective observational design with data from the Mexican Health and Aging Study (MHAS). Participants were men aged 50-69 from three cohorts: development/internal validation, temporal validation, and external validation. Key predictors were identified using relaxed Least Absolute Shrinkage and Selection Operator (LASSO) regression, and model performance was assessed using the area under the curve (AUC) from receiver operating characteristic (ROC) analyses, along with calibration and decision curve analysis (DCA). A web nomogram was also developed.
The final model included seven key predictors. AUC values indicated good predictive performance: 0.783 for the training subgroup, 0.722 for the test subgroup, 0.748 for the time cohort, and 0.756 for the external cohort, with sensitivities of 73.5%. The DCA demonstrated the superior clinical utility of the model compared to the reference strategies.
The predictive model developed for performance to PCa screening is robust across different cohorts and highlights critical factors influencing performance. The accompanying web-based nomogram enhances clinical applicability and supports interventions aimed at improving PCa screening rates among Mexican men.
前列腺癌（PCa）是全球男性中第二常见的癌症，其发病率和死亡率都很高，在墨西哥尤为明显，该国晚期诊断很常见。通过直肠指检和前列腺特异性抗原检测等筛查方法进行早期检测对于改善治疗结果至关重要。尽管目前已做出努力，但由于多种障碍，前列腺筛查（PS）的依从性仍然很低。本研究旨在开发并验证一种针对墨西哥男性前列腺癌筛查依从性的预测模型。
采用回顾性观察设计，数据来自墨西哥健康与老龄化研究（MHAS）。参与者为来自三个队列的50 - 69岁男性：开发/内部验证队列、时间验证队列和外部验证队列。使用宽松的最小绝对收缩和选择算子（LASSO）回归确定关键预测因素，并使用来自受试者工作特征（ROC）分析的曲线下面积（AUC）以及校准和决策曲线分析（DCA）评估模型性能。还开发了一个网络列线图。
最终模型包括七个关键预测因素。AUC值表明模型具有良好的预测性能：训练亚组为0.783，测试亚组为0.722，时间队列亚组为0.748，外部队列亚组为0.756，灵敏度为73.5%。DCA表明该模型与参考策略相比具有更高的临床实用性。
所开发的针对前列腺癌筛查性能的预测模型在不同队列中都很稳健，并突出了影响筛查性能的关键因素。随附的基于网络的列线图增强了临床适用性，并支持旨在提高墨西哥男性前列腺癌筛查率的干预措施。
