Denijs Frederique B, van Harten Meike J, Meenderink Jonas J L, Leenen Renée C A, Remmers Sebastiaan, Venderbos Lionne D F, van den Bergh Roderick C N, Beyer Katharina, Roobol Monique J
Department of Urology, Erasmus MC Cancer Institute, University Medical Center Rotterdam, Rotterdam, The Netherlands.
Department of Oncological Urology, University Medical Center Utrecht, Utrecht, The Netherlands.
Prostate Cancer Prostatic Dis. 2024 Sep;27(3):544-557. doi: 10.1038/s41391-024-00852-w. Epub 2024 Jun 3.
Prostate cancer (PCa) (early) detection poses significant challenges, including unnecessary testing and the risk of potential overdiagnosis. The European Association of Urology therefore suggests an individual risk-adapted approach, incorporating risk calculators (RCs) into the PCa detection pathway. In the context of 'The PRostate Cancer Awareness and Initiative for Screening in the European Union' (PRAISE-U) project ( https://uroweb.org/praise-u ), we aim to provide an overview of the currently available clinical RCs applicable in an early PCa detection algorithm.
We performed a systematic review to identify RCs predicting detection of clinically significant PCa at biopsy. A search was performed in the databases Medline ALL, Embase, Web of Science Core Collection, Cochrane Central Register of Controlled Trials and Google Scholar for publications between January 2010 and July 2023. We retrieved relevant literature by using the terms "prostate cancer", "screening/diagnosis" and "predictive model". Inclusion criteria included systematic reviews, meta-analyses, and clinical trials. Exclusion criteria applied to studies involving pre-targeted high-risk populations, diagnosed PCa patients, or a sample sizes under 50 men.
We identified 6474 articles, of which 140 were included after screening abstracts and full texts. In total, we identified 96 unique RCs. Among these, 45 underwent external validation, with 28 validated in multiple cohorts. Of the externally validated RCs, 17 are based on clinical factors, 19 incorporate clinical factors along with MRI details, 4 were based on blood biomarkers alone or in combination with clinical factors, and 5 included urinary biomarkers. The median AUC of externally validated RCs ranged from 0.63 to 0.93.
This systematic review offers an extensive analysis of currently available RCs, their variable utilization, and performance within validation cohorts. RCs have consistently demonstrated their capacity to mitigate the limitations associated with early detection and have been integrated into modern practice and screening trials. Nevertheless, the lack of external validation data raises concerns about numerous RCs, and it is crucial to factor in this omission when evaluating whether a specific RC is applicable to one's target population.
前列腺癌（PCa）的早期检测面临重大挑战，包括不必要的检测以及潜在过度诊断的风险。因此，欧洲泌尿外科学会建议采用个体风险适应性方法，将风险计算器（RCs）纳入PCa检测途径。在“欧盟前列腺癌筛查意识与倡议”（PRAISE-U）项目（https://uroweb.org/praise-u）的背景下，我们旨在概述目前适用于早期PCa检测算法的临床RCs。
我们进行了一项系统综述，以确定预测活检时临床显著PCa检测的RCs。在Medline ALL、Embase、Web of Science核心合集、Cochrane对照试验中央登记册和谷歌学术数据库中搜索了2010年1月至2023年7月期间的出版物。我们使用“前列腺癌”“筛查/诊断”和“预测模型”等术语检索相关文献。纳入标准包括系统综述、荟萃分析和临床试验。排除标准适用于涉及预先选定的高危人群、已确诊的PCa患者或样本量少于50名男性的研究。
我们识别出6474篇文章，筛选摘要和全文后纳入140篇。我们总共识别出96个独特的RCs。其中，45个进行了外部验证，28个在多个队列中得到验证。在经过外部验证的RCs中，17个基于临床因素，19个将临床因素与MRI细节相结合，4个仅基于血液生物标志物或与临床因素相结合，5个包括尿液生物标志物。经过外部验证的RCs的中位AUC范围为0.63至0.93。
本系统综述对目前可用的RCs、其不同的应用情况以及在验证队列中的表现进行了广泛分析。RCs一直证明其有能力减轻与早期检测相关的局限性，并已被纳入现代实践和筛查试验。然而，众多RCs缺乏外部验证数据令人担忧，在评估特定RC是否适用于目标人群时，必须考虑到这一遗漏因素。
