@文章{信息:doi/10.2196/16322，作者="Chen, I-Hsuan Alan and Chu, Chi-Hsiang and Lin, jentai and Tsai, zheng -Yu and Yu, Chia-Cheng and Sridhar, Ashwin Narasimha and Sooriakumaran, Prasanna and Loureiro, Rui C V and Chand, Manish"，标题="台湾人群队列中的前列腺癌风险计算器应用:验证研究"，期刊="J Med Internet Res"，年="2020"，月="12"，日="18"，卷="22"，号="12"，页数="e16322"，关键词="诊断;移动健康;移动应用;前列腺癌;前列腺特异性抗原;背景:移动健康应用程序已经成为患者和临床医生的有用工具，可以共享健康信息或协助临床决策。前列腺癌(PCa)风险计算器移动应用程序已被引入，以评估前列腺癌和高级别前列腺癌(Gleason评分≥7)的风险。鹿特丹前列腺癌风险计算器和Coral-前列腺癌Nomogram计算器应用程序分别由2个最受研究的前列腺癌风险计算器开发而来，分别是欧洲前列腺癌随机筛查研究(ERSPC)和北美前列腺癌预防试验(PCPT)风险计算器。一项系统综述表明，Rotterdam和Coral应用程序在活检前阶段表现最佳。然而，前列腺癌的流行病学在不同人群中有所不同，因此，这些应用程序在台湾人群中的适用性需要评估。 This study is the first to validate the PCa risk calculator apps with both biopsy and prostatectomy cohorts in Taiwan. Objective: The study's objective is to validate the PCa risk calculator apps using a Taiwanese cohort of patients. Additionally, we aim to utilize postprostatectomy pathology outcomes to assess the accuracy of both apps with regard to high-grade PCa. Methods: All male patients who had undergone transrectal ultrasound prostate biopsies in a single Taiwanese tertiary medical center from 2012 to 2018 were identified retrospectively. The probabilities of PCa and high-grade PCa were calculated utilizing the Rotterdam and Coral apps, and compared with biopsy and prostatectomy results. Calibration was graphically evaluated with the Hosmer-Lemeshow goodness-of-fit test. Discrimination was analyzed utilizing the area under the receiver operating characteristic curve (AUC). Decision curve analysis was performed for clinical utility. Results: Of 1134 patients, 246 (21.7{\%}) were diagnosed with PCa; of these 246 patients, 155 (63{\%}) had high-grade PCa, according to the biopsy results. After confirmation with prostatectomy pathological outcomes, 47.2{\%} (25/53) of patients were upgraded to high-grade PCa, and 1.2{\%} (1/84) of patients were downgraded to low-grade PCa. Only the Rotterdam app demonstrated good calibration for detecting high-grade PCa in the biopsy cohort. The discriminative ability for both PCa (AUC: 0.779 vs 0.687; DeLong's method: P<.001) and high-grade PCa (AUC: 0.862 vs 0.758; P<.001) was significantly better for the Rotterdam app. In the prostatectomy cohort, there was no significant difference between both apps (AUC: 0.857 vs 0.777; P=.128). Conclusions: The Rotterdam and Coral apps can be applied to the Taiwanese cohort with accuracy. The Rotterdam app outperformed the Coral app in the prediction of PCa and high-grade PCa. Despite the small size of the prostatectomy cohort, both apps, to some extent, demonstrated the predictive capacity for true high-grade PCa, confirmed by the whole prostate specimen. Following our external validation, the Rotterdam app might be a good alternative to help detect PCa and high-grade PCa for Taiwanese men. ", issn="1438-8871", doi="10.2196/16322", url="//www.mybigtv.com/2020/12/e16322/", url="https://doi.org/10.2196/16322", url="http://www.ncbi.nlm.nih.gov/pubmed/33337340" }