完善前列腺特异性抗原密度的临床相关临界值，以对 PI-RADS 3 病变患者进行风险分层。
Refining clinically relevant cut-offs of prostate specific antigen density for risk stratification in patients with PI-RADS 3 lesions.
发表日期：2024 Jul 24
作者：
Georges Mjaess, Laura Haddad, Teddy Jabbour, Arthur Baudewyns, Henri-Alexandre Bourgeno, Yolène Lefebvre, Mariaconsiglia Ferriero, Giuseppe Simone, Alexandre Fourcade, Georges Fournier, Marco Oderda, Paolo Gontero, Adrian Bernal-Gomez, Alessandro Mastrorosa, Jean-Baptiste Roche, Rawad Abou Zahr, Guillaume Ploussard, Gaelle Fiard, Adam Halinski, Katerina Rysankova, Charles Dariane, Gina Delavar, Julien Anract, Nicolas Barry Delongchamps, Alexandre Patrick Bui, Fayek Taha, Olivier Windisch, Daniel Benamran, Gregoire Assenmacher, Jan Benijts, Karsten Guenzel, Thierry Roumeguère, Alexandre Peltier, Romain Diamand
来源：
PROSTATE CANCER AND PROSTATIC DISEASES
摘要：
通过多参数磁共振成像 (mpMRI) 识别的前列腺成像报告和数据系统 (PI-RADS) 3 病变由于其在预测具有临床意义的前列腺癌 (csPCa) 方面的模棱两可性而提出了临床挑战。该研究的目的是改善 PI-RADS 3 病变患者和前列腺活检候选者的风险分层。回顾性地确定了 2016 年 1 月至 2023 年 4 月期间接受 MRI 和随后 MRI 靶向系统活检的 4841 名连续患者的队列。独立的前瞻性维护数据库。只有具有 PI-RADS 3 病变的患者才纳入最终分析。进行多变量逻辑回归分析以确定与 csPCa 相关的协变量，csPCa 定义为国际泌尿病理学会 (ISUP) 分级组≥2。使用受试者工作特征曲线 (AUC) 下面积、校准和净收益来评估模型的性能。然后使用卡方自动交互检测 (CHAID) 分析选择显着的预测因子进行进一步探索。总体而言，790 名患者患有 PI-RADS 3 病变，151 名患者 (19%) 患有 csPCa。观察到年龄（OR：1.1 [1.0-1.1]；p = 0.01）和 PSA 密度（OR：1643 [2717-41,997]；p < 0.01）之间的显着相关性。 CHAID 分析确定 PSAd 是影响决策树的唯一重要因素。双节点模型的 PSAd 截止值为 0.13ng/ml/cc（csPCa 检出率为 1% 与 18%），三节点模型的截止值为 0.09ng/ml/cc 和 0.16ng/ml/cc （csPCa 检出率分别为 0.5% vs. 2% vs. 17%）。对于前列腺 mpMRI PI-RADS 3 病变且 PSAd 低于 0.13，特别是低于 0.09 的个体，可以省略前列腺活检，以避免不必要的活检和非 csPCa 的过度诊断。© 2024。作者，获得 Springer Nature Limited 的独家许可。
Prostate Imaging Reporting and Data System (PI-RADS) 3 lesions, identified through multiparametric magnetic resonance imaging (mpMRI), present a clinical challenge due to their equivocal nature in predicting clinically significant prostate cancer (csPCa). Aim of the study is to improve risk stratification of patients with PI-RADS 3 lesions and candidates for prostate biopsy.A cohort of 4841 consecutive patients who underwent MRI and subsequent MRI-targeted and systematic biopsies between January 2016 and April 2023 were retrospectively identified from independent prospectively maintained database. Only patients who have PI-RADS 3 lesions were included in the final analysis. A multivariable logistic regression analysis was performed to identify covariables associated with csPCa defined as International Society of Urological Pathology (ISUP) grade group ≥2. Performance of the model was evaluated using the area under the receiver operating characteristic curve (AUC), calibration, and net benefit. Significant predictors were then selected for further exploration using a Chi-squared Automatic Interaction Detection (CHAID) analysis.Overall, 790 patients had PI-RADS 3 lesions and 151 (19%) had csPCa. Significant associations were observed for age (OR: 1.1 [1.0-1.1]; p = 0.01) and PSA density (OR: 1643 [2717-41,997]; p < 0.01). The CHAID analysis identified PSAd as the sole significant factor influencing the decision tree. Cut-offs for PSAd were 0.13 ng/ml/cc (csPCa detection rate of 1% vs. 18%) for the two-nodes model and 0.09 ng/ml/cc and 0.16 ng/ml/cc for the three-nodes model (csPCa detection rate of 0.5% vs. 2% vs. 17%).For individuals with PI-RADS 3 lesions on prostate mpMRI and a PSAd below 0.13, especially below 0.09, prostate biopsy can be omitted, in order to avoid unnecessary biopsy and overdiagnosis of non-csPCa.© 2024. The Author(s), under exclusive licence to Springer Nature Limited.