Baboudjian Michael, Breda Alberto, Roumeguère Thierry, Uleri Alessandro, Roche Jean-Baptiste, Touzani Alae, Lacetera Vito, Beauval Jean-Baptiste, Diamand Romain, Simone Guiseppe, Windisch Olivier, Benamran Daniel, Fourcade Alexandre, Fiard Gaelle, Durand-Labrunie Camille, Roumiguié Mathieu, Sanguedolce Francesco, Oderda Marco, Barret Eric, Fromont Gaëlle, Dariane Charles, Charvet Anne-Laure, Gondran-Tellier Bastien, Bastide Cyrille, Lechevallier Eric, Palou Joan, Ruffion Alain, Van Der Bergh Roderick C N, Peltier Alexandre, Ploussard Guillaume
Department of Urology, La Croix du Sud Hôpital, Quint Fonsegrives, France.
Department of Urology, North Hospital, Aix-Marseille University, APHM, Marseille, France.
World J Urol. 2023 May;41(5):1301-1308. doi: 10.1007/s00345-023-04353-8. Epub 2023 Mar 15.
To develop new selection criteria for active surveillance (AS) in intermediate-risk (IR) prostate cancer (PCa) patients.
Retrospective study including patients from 14 referral centers who underwent pre-biopsy mpMRI, image-guided biopsies and radical prostatectomy. The cohort included biopsy-naive IR PCa patients who met the following inclusion criteria: Gleason Grade Group (GGG) 1-2, PSA < 20 ng/mL, and cT1-cT2 tumors. We relied on a recursive machine learning partitioning algorithm developed to predict adverse pathological features (i.e., ≥ pT3a and/or pN + and/or GGG ≥ 3).
A total of 594 patients with IR PCa were included, of whom 220 (37%) had adverse features. PI-RADS score (weight:0.726), PSA density (weight:0.158), and clinical T stage (weight:0.116) were selected as the most informative risk factors to classify patients according to their risk of adverse features, leading to the creation of five risk clusters. The adverse feature rates for cluster #1 (PI-RADS ≤ 3 and PSA density < 0.15), cluster #2 (PI-RADS 4 and PSA density < 0.15), cluster #3 (PI-RADS 1-4 and PSA density ≥ 0.15), cluster #4 (normal DRE and PI-RADS 5), and cluster #5 (abnormal DRE and PI-RADS 5) were 11.8, 27.9, 37.3, 42.7, and 65.1%, respectively. Compared with the current inclusion criteria, extending the AS criteria to clusters #1 + #2 or #1 + #2 + #3 would increase the number of eligible patients (+ 60 and + 253%, respectively) without increasing the risk of adverse pathological features.
The newly developed model has the potential to expand the number of patients eligible for AS without compromising oncologic outcomes. Prospective validation is warranted.
为中危（IR）前列腺癌（PCa）患者制定新的主动监测（AS）选择标准。
回顾性研究纳入了来自14个转诊中心的患者，这些患者接受了活检前多参数磁共振成像（mpMRI）、图像引导活检和根治性前列腺切除术。该队列包括符合以下纳入标准的未经活检的IR PCa患者： Gleason分级组（GGG）1 - 2、前列腺特异性抗原（PSA）<20 ng/mL以及cT1 - cT2期肿瘤。我们依赖一种开发用于预测不良病理特征（即≥pT3a和/或pN+和/或GGG≥3）的递归机器学习划分算法。
共纳入594例IR PCa患者，其中220例（37%）具有不良特征。前列腺影像报告和数据系统（PI-RADS）评分（权重：0.726）、PSA密度（权重：0.158）和临床T分期（权重：0.116）被选为根据患者不良特征风险进行分类的最具信息性的危险因素，从而创建了五个风险组。第1组（PI-RADS≤3且PSA密度<0.15）、第2组（PI-RADS 4且PSA密度<0.15）、第3组（PI-RADS 1 - 4且PSA密度≥0.15）、第4组（直肠指检正常且PI-RADS 5）和第5组（直肠指检异常且PI-RADS 5）的不良特征发生率分别为11.8%、27.9%、37.3%、42.7%和65.1%。与当前纳入标准相比，将AS标准扩展至第1组 + 第2组或第1组 + 第2组 + 第3组将增加符合条件的患者数量（分别增加60%和253%），而不会增加不良病理特征的风险。
新开发的模型有可能在不影响肿瘤学结局的情况下扩大符合AS条件的患者数量。有必要进行前瞻性验证。
