Department of Maternal Infant and Urologic Sciences, "Sapienza" University of Rome, Policlinico Umberto I Hospital, Viale del Policlinico 155, Rome, 00161, Italy.
Department of Urology, Stanford Medical Center, Stanford, CA, USA.
World J Urol. 2022 Jul;40(7):1617-1628. doi: 10.1007/s00345-022-03969-6. Epub 2022 Mar 16.
To determine through a comprehensive systematic review and meta-analysis the cumulative diagnostic performance of vesical imaging-reporting and data system (VIRADS) to predict preoperative muscle-invasiveness among different institutions, readers, and optimal scoring accuracy thresholds.
PubMed, Cochrane and Embase were searched from inception up to May 2021. Sensitivity (Sn), Specificity (Sp) were first estimated and subsequently pooled using hierarchical summary receiver operating characteristics (HSROC) modeling for both cut-off ≥ 3 and ≥ 4 to predict muscle-invasive bladder cancer (MIBC). Further sensitivity analysis, subgroup analysis and meta-regression were conducted to investigate contribution of moderators to heterogeneity.
In total, n = 20 studies from 2019 to 2021 with n = 2477 patients by n = 53 genitourinary radiologists met the inclusion criteria. Pooled weighted Sn and Sp were 0.87 (95% CI 0.82-0.91) and 0.86 (95% CI 0.80-0.90) for cut-off ≥ 3 while 0.78 (95% CI 0.74-0.81) and 0.94 (95% CI 0.91-0.96) for cut-off ≥ 4. The area under the HSROC curve was 0.93 (95% CI 0.90-0.95) and 0.91 (95% CI 0.88-0.93) for cut-off ≥ 3 and ≥ 4, respectively. Meta-regression analyses showed no influence of clinical characteristics nor cumulative reader's experience while study design and radiological characteristics were found to influence the estimated outcome.
We demonstrated excellent worldwide diagnostic performance of VI-RADS to determine pre-trans urethral resection of bladder tumor (TURBT) staging. Our findings corroborate wide reliability of VI-RADS accuracy also between different centers with varying experience underling the importance that standardization and reproducibility of VI-RADS may confer to multiparametric magnetic resonance imaging (mpMRI) for preoperative BCa discrimination.
通过全面的系统评价和荟萃分析，确定不同机构、读者和最佳评分准确性阈值下，膀胱影像学报告和数据系统（VIRADS）对术前肌肉侵犯性的累积诊断性能。
从建库到 2021 年 5 月，检索了 PubMed、Cochrane 和 Embase。首先估计了敏感度（Sn）和特异性（Sp），然后使用分层综合受试者工作特征（HSROC）模型对截断值≥3 和≥4 进行了汇总，以预测肌层浸润性膀胱癌（MIBC）。进一步进行了敏感性分析、亚组分析和荟萃回归，以研究调节因素对异质性的贡献。
共纳入 2019 年至 2021 年的 20 项研究，53 名泌尿生殖系统放射科医生共评估了 2477 名患者。对于截断值≥3，汇总的加权敏感度和特异度分别为 0.87（95%可信区间 0.82-0.91）和 0.86（95%可信区间 0.80-0.90）；对于截断值≥4，分别为 0.78（95%可信区间 0.74-0.81）和 0.94（95%可信区间 0.91-0.96）。HSROC 曲线下面积分别为 0.93（95%可信区间 0.90-0.95）和 0.91（95%可信区间 0.88-0.93）。荟萃回归分析表明，临床特征和累积读者经验无影响，而研究设计和放射学特征则影响估计结果。
我们证明了 VIRADS 在确定经尿道膀胱肿瘤切除术（TURBT）前分期方面具有出色的全球诊断性能。我们的发现证实了 VIRADS 在不同经验中心之间也具有广泛的可靠性，这凸显了 VIRADS 的标准化和可重复性对于术前 BCa 鉴别可能赋予多参数磁共振成像（mpMRI）的重要性。
