Zhu MeiLin, Gao JiaHao, Han Fang, Yin LongLin, Zhang LuShun, Yang Yong, Zhang JiaWen
Department of Radiology, Huashan Hospital, Fudan University, Shanghai, 200040, China.
Department of Radiology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, 610072, China.
Insights Imaging. 2023 Aug 22;14(1):140. doi: 10.1186/s13244-023-01486-7.
In recent decades, diverse nomograms have been proposed to predict extraprostatic extension (EPE) in prostate cancer (PCa). We aimed to systematically evaluate the accuracy of MRI-inclusive nomograms and traditional clinical nomograms in predicting EPE in PCa. The purpose of this meta-analysis is to provide baseline summative and comparative estimates for future study designs.
The PubMed, Embase, and Cochrane databases were searched up to May 17, 2023, to identify studies on prediction nomograms for EPE of PCa. The risk of bias in studies was assessed by using the Prediction model Risk Of Bias ASsessment Tool (PROBAST). Summary estimates of sensitivity and specificity were obtained with bivariate random-effects model. Heterogeneity was investigated through meta-regression and subgroup analysis.
Forty-eight studies with a total of 57 contingency tables and 20,395 patients were included. No significant publication bias was observed for either the MRI-inclusive nomograms or clinical nomograms. For MRI-inclusive nomograms predicting EPE, the pooled AUC of validation cohorts was 0.80 (95% CI: 0.76, 0.83). For traditional clinical nomograms predicting EPE, the pooled AUCs of the Partin table and Memorial Sloan Kettering Cancer Center (MSKCC) nomogram were 0.72 (95% CI: 0.68, 0.76) and 0.79 (95% CI: 0.75, 0.82), respectively.
Preoperative risk stratification is essential for PCa patients; both MRI-inclusive nomograms and traditional clinical nomograms had moderate diagnostic performance for predicting EPE in PCa. This study provides baseline comparative values for EPE prediction for future studies which is useful for evaluating preoperative risk stratification in PCa patients.
This meta-analysis firstly evaluated the diagnostic performance of preoperative MRI-inclusive nomograms and clinical nomograms for predicting extraprostatic extension (EPE) in prostate cancer (PCa) (moderate AUCs: 0.72-0.80). We provide baseline estimates for EPE prediction, these findings will be useful in assessing preoperative risk stratification of PCa patients.
• MRI-inclusive nomograms and traditional clinical nomograms had moderate AUCs (0.72-0.80) for predicting EPE. • MRI combined clinical nomogram may improve diagnostic accuracy of MRI alone for EPE prediction. • MSKCC nomogram had a higher specificity than Partin table for predicting EPE. • This meta-analysis provided baseline and comparative estimates of nomograms for EPE prediction for future studies.
近几十年来，人们提出了多种列线图来预测前列腺癌（PCa）的前列腺外侵犯（EPE）。我们旨在系统评估包含MRI的列线图和传统临床列线图在预测PCa的EPE方面的准确性。本荟萃分析的目的是为未来的研究设计提供基线汇总和比较估计。
检索截至2023年5月17日的PubMed、Embase和Cochrane数据库，以确定关于PCa的EPE预测列线图的研究。使用预测模型偏倚风险评估工具（PROBAST）评估研究中的偏倚风险。采用双变量随机效应模型获得敏感性和特异性的汇总估计。通过Meta回归和亚组分析研究异质性。
纳入48项研究，共57个列联表和20395例患者。对于包含MRI的列线图和临床列线图，均未观察到明显的发表偏倚。对于预测EPE的包含MRI的列线图，验证队列的合并AUC为0.80（95%CI：0.76，0.83）。对于预测EPE的传统临床列线图，Partin表和纪念斯隆凯特琳癌症中心（MSKCC）列线图的合并AUC分别为0.72（95%CI：0.68，0.76）和0.79（95%CI：0.75，0.82）。
术前风险分层对PCa患者至关重要；包含MRI的列线图和传统临床列线图在预测PCa的EPE方面均具有中等诊断性能。本研究为未来研究提供了EPE预测的基线比较值，有助于评估PCa患者的术前风险分层。
本荟萃分析首次评估了术前包含MRI的列线图和临床列线图对预测前列腺癌（PCa）的前列腺外侵犯（EPE）的诊断性能（中等AUC：0.72 - 0.80）。我们提供了EPE预测的基线估计，这些发现将有助于评估PCa患者的术前风险分层。
• 包含MRI的列线图和传统临床列线图在预测EPE方面具有中等AUC（0.72 - 0.80）。• MRI联合临床列线图可能提高单独MRI对EPE预测的诊断准确性。• MSKCC列线图在预测EPE方面比Partin表具有更高的特异性。• 本荟萃分析为未来研究提供了EPE预测列线图的基线和比较估计。
