Suppr超能文献

扩大低危和中危前列腺癌的主动监测标准:我们能否准确预测多参数磁共振成像靶向活检诊断患者的误诊风险?

Expanding Active Surveillance Criteria for Low- and Intermediate-risk Prostate Cancer: Can We Accurately Predict the Risk of Misclassification for Patients Diagnosed by Multiparametric Magnetic Resonance Imaging-targeted Biopsy?

作者信息

Diamand Romain, Albisinni Simone, Roche Jean-Baptiste, Lievore Elena, Lacetera Vito, Chiacchio Giuseppe, Beatrici Valerio, Mastroianni Riccardo, Simone Giuseppe, Windisch Olivier, Benamran Daniel, Fourcade Alexandre, An Nguyen Truong, Fournier Georges, Fiard Gaelle, Ploussard Guillaume, Peltier Alexandre, Roumeguère Thierry

机构信息

Department of Urology, Jules Bordet Institute-Erasme Hospital, Hôpital Universitaire de Bruxelles, Université Libre de Bruxelles, Brussels, Belgium.

Department of Urology, Jules Bordet Institute-Erasme Hospital, Hôpital Universitaire de Bruxelles, Université Libre de Bruxelles, Brussels, Belgium.

出版信息

Eur Urol Focus. 2023 Mar;9(2):298-302. doi: 10.1016/j.euf.2022.09.011. Epub 2022 Oct 6.

Abstract

Models predicting the risk of adverse pathology (ie, International Society of Urological Pathology [ISUP] grade group ≥3, pT3, and/or pN1) among patients operated by radical prostatectomy (RP) have been proposed to expand active surveillance (AS) inclusion criteria. We aimed to test these models in a set of 1062 low-risk and favorable intermediate-risk prostate cancer (PCa) patients diagnosed by multiparametric magnetic resonance imaging (MRI) and MRI-targeted biopsy. We hypothesized that the inclusion of radiological features into a novel model would improve patient selection. Performance was assessed using discrimination, calibration, and decision curve analysis (DCA). Available models were characterized by poor discrimination (areas under the receiver operating characteristic curve [AUCs] of 59% and 60%), underestimation of predicted risk on calibration plots, and a small amount of net benefit against a probability threshold of 40-50% at the DCA. The development of a novel model slightly improved discrimination (AUC of 63% vs 59%, p = 0.001, and 63% vs 60%, p = 0.07) and net benefit against threshold probabilities of ≥30%. This first multicenter study demonstrated the poor performance of models predicting adverse pathology and that implementation of MRI and MRI-targeted biopsy in this setting was not associated with a clear improvement in patient selection. Patients harboring low-risk or favorable intermediate-risk PCa and candidates for RP cannot be referred accurately to an AS program without a non-negligible risk of misclassification. PATIENT SUMMARY: We tested prediction models that could expand the selection of prostate cancer patients for active surveillance. Models were inaccurate and associated with a high risk of misclassification despite the implementation of multiparametric magnetic resonance imaging and targeted biopsies.

摘要

已提出预测接受根治性前列腺切除术(RP)的患者出现不良病理(即国际泌尿病理学会[ISUP]分级组≥3、pT3和/或pN1)风险的模型,以扩大主动监测(AS)的纳入标准。我们旨在对一组1062例经多参数磁共振成像(MRI)和MRI靶向活检诊断为低风险和有利的中风险前列腺癌(PCa)的患者测试这些模型。我们假设将放射学特征纳入新模型会改善患者选择。使用区分度、校准和决策曲线分析(DCA)评估模型性能。现有模型的特点是区分度差(受试者操作特征曲线下面积[AUC]分别为59%和60%)、在校准图上预测风险估计不足,以及在DCA中针对40 - 50%的概率阈值时净效益较小。新模型的开发略微改善了区分度(AUC分别为63%对59%,p = 0.001,以及63%对60%,p = 0.07)以及针对≥30%阈值概率的净效益。这项首个多中心研究证明了预测不良病理的模型性能不佳,并且在这种情况下实施MRI和MRI靶向活检与患者选择的明显改善无关。患有低风险或有利的中风险PCa且适合RP的患者在没有不可忽略的误分类风险的情况下无法准确转诊至AS计划。患者总结:我们测试了可扩大前列腺癌患者主动监测选择的预测模型。尽管实施了多参数磁共振成像和靶向活检,但模型不准确且误分类风险高。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验