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纳入筛状癌和导管内癌的磁共振成像(MRI)适配前列腺癌风险评估工具

Magnetic Resonance Imaging (MRI)-Adapted Prostate Cancer Risk Tool Incorporating Cribriform and Intraductal Carcinoma.

作者信息

Nguyen Ngoc-Nhu Jennifer, Liu Kristen, Lajkosz Katherine, Bernardino Rui, Yin Leyi Bellinda, Hollemans Eva, Kroon Lisa J, Fleshner Neil, van Leenders Geert J L H, Iczkowski Kenneth A, van der Kwast Theodorus H, Downes Michelle R

机构信息

Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario, Canada; Department of Anatomic Pathology, Precision Diagnostics and Therapeutics Program, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada.

Department of Pathology, Medical College of Wisconsin, Milwaukee, Wisconsin.

出版信息

Mod Pathol. 2025 Jul 25;38(12):100852. doi: 10.1016/j.modpat.2025.100852.

Abstract

Current prostate cancer risk stratification tools are not adapted for magnetic resonance imaging (MRI)-targeted biopsies and do not include the presence of cribriform carcinoma/intraductal carcinoma (CC/IDC), an independent predictor of adverse clinical outcomes. We developed an MRI-adapted prostate cancer risk tool (MAPCaRT), which incorporates CC/IDC presence to the Cancer of the Prostate Risk Assessment (CAPRA) tool. We compared the prognostic power of MAPCaRT with that of CAPRA in MRI-targeted biopsies (n = 266, 2015-2023) and systematic-only biopsies (n = 1291, 2010-2018) that had matched radical prostatectomy. MAPCaRT employs the aggregate core count method for MRI-targeted lesions to calculate percent positive biopsy cores and uses the radiological stage when assessing MRI-targeted biopsies. Point attribution for CC/IDC presence and Gleason score was determined using a Cox proportional hazards model that included the CAPRA score, Gleason score, and CC/IDC status. Based on calculated MAPCaRT and CAPRA scores, patients were classified into the low-risk (0-2), intermediate-risk (3-5), or high-risk (6+) group. Model performance was assessed via the Kaplan-Meier curves, Harrell C-indices, and decision curve analysis for biochemical recurrence-free survival (BCR-FS) and event-free survival (EFS) (metastasis/cancer-specific death). CC/IDC was present in 84 of 266 (32%) MRI-targeted biopsies and 293 of 1291 systematic-only biopsies (23%). The median follow-up time was 3.4 years (IQR, 2.3-5.5 years) for the MRI-targeted biopsy cohort and 5.9 years (IQR, 3.4-8.1 years) for the systematic biopsy cohort. In the MRI-targeted biopsy cohort, MAPCaRT showed substantial improvement of the C-index compared with CAPRA (0.635 vs 0.574, P = .045) and greater net clinical benefit for 4-year BCR-FS. In the systematic biopsy cohort, MAPCaRT demonstrated improved C-index for BCR-FS (0.696 vs 0.655, P < .001) and greater net clinical benefit for 5-year BCR-FS and EFS. Other model performance metrics were marginally better with MAPCaRT. In summary, we developed MAPCaRT (prostatecancercalculator.lmp.utoronto.ca), a modified version of CAPRA incorporating CC/IDC presence, which demonstrated improved BCR-FS and EFS predictions. This may result in better clinical guidance for disease management decisions.

摘要

当前的前列腺癌风险分层工具并不适用于磁共振成像(MRI)靶向活检,且未将筛状癌/导管内癌(CC/IDC)的存在纳入其中,而CC/IDC是不良临床结局的独立预测因素。我们开发了一种适用于MRI的前列腺癌风险工具(MAPCaRT),它将CC/IDC的存在纳入前列腺癌风险评估(CAPRA)工具中。我们比较了MAPCaRT与CAPRA在MRI靶向活检(n = 266,2015 - 2023年)和单纯系统活检(n = 1291,2010 - 2018年)且已进行匹配根治性前列腺切除术的患者中的预后能力。MAPCaRT采用汇总核心计数法对MRI靶向病变进行计算,以得出活检阳性核心百分比,并在评估MRI靶向活检时使用放射学分期。使用包含CAPRA评分、Gleason评分和CC/IDC状态的Cox比例风险模型来确定CC/IDC存在和Gleason评分的点归因。根据计算出的MAPCaRT和CAPRA评分,将患者分为低风险(0 - 2)、中风险(3 - 5)或高风险(6 +)组。通过Kaplan - Meier曲线、Harrell C指数以及生化无复发生存期(BCR - FS)和无事件生存期(EFS,转移/癌症特异性死亡)的决策曲线分析来评估模型性能。在266例MRI靶向活检中有84例(32%)存在CC/IDC,在1291例单纯系统活检中有293例(23%)存在CC/IDC。MRI靶向活检队列的中位随访时间为3.4年(四分位间距,2.3 - 5.5年),系统活检队列的中位随访时间为5.9年(四分位间距,3.4 - 8.1年)。在MRI靶向活检队列中,与CAPRA相比,MAPCaRT的C指数有显著改善(0.635对0.574,P = 0.045),并且在4年BCR - FS方面具有更大的净临床获益。在系统活检队列中,MAPCaRT在BCR - FS方面的C指数有所改善(0.696对0.655,P < 0.001),并且在5年BCR - FS和EFS方面具有更大的净临床获益。MAPCaRT的其他模型性能指标也略好一些。总之,我们开发了MAPCaRT(prostatecancercalculator.lmp.utoronto.ca),这是一种纳入了CC/IDC存在情况的CAPRA改良版本,其在BCR - FS和EFS预测方面表现出改善。这可能会为疾病管理决策带来更好的临床指导。

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