Department of Radiology and Diagnostic Imaging, University of Alberta, 2B2.41 WMC, 8440-112 St NW, Edmonton, AB T6G 2B7, Canada.
Evidence-Based Practice Center, Mayo Clinic, Rochester, MN.
AJR Am J Roentgenol. 2021 Jul;217(1):31-39. doi: 10.2214/AJR.20.23403. Epub 2021 Apr 28.
This systematic review and meta-analysis evaluates the diagnostic accuracy of MRI for differentiating malignant (MPNSTs) from benign peripheral nerve sheath tumors (BPNSTs). A systematic review of MEDLINE, Embase, Scopus, the Cochrane Library, and the gray literature from inception to December 2019 was performed. Original articles that involved at least 10 patients and that evaluated the accuracy of MRI for detecting MPNSTs were included. Two reviewers independently extracted clinical and radiologic data from included articles to calculate sensitivity, specificity, PPV, NPV, and accuracy. A meta-analysis was performed using a bivariate mixed-effects regression model. Risk of bias was evaluated using QUADAS-2. Fifteen studies involving 798 lesions (252 MPNSTs and 546 BPNSTs) were included in the analysis. Pooled and weighted sensitivity, specificity, and AUC values for MRI in detecting MPNSTs were 68% (95% CI, 52-80%), 93% (95% CI, 85-97%), and 0.89 (95% CI, 0.86-0.92) when using feature combination and 88% (95% CI, 74-95%), 94% (95% CI, 89-96%), and 0.97 (95% CI, 0.95-0.98) using diffusion restriction with or without feature combination. Subgroup analysis, such as patients with neurofibromatosis type 1 (NF1) versus those without NF1, could not be performed because of insufficient data. Risk of bias was predominantly high or unclear for patient selection, mixed for index test, low for reference standard, and unclear for flow and timing. Combining features such as diffusion restriction optimizes the diagnostic accuracy of MRI for detecting MPNSTs. However, limitations in the literature, including variability and risk of bias, necessitate additional methodologically rigorous studies to allow subgroup analysis and further evaluate the combination of clinical and MRI features for MPNST diagnosis.
这篇系统评价和荟萃分析评估了 MRI 鉴别恶性外周神经鞘瘤(MPNST)和良性外周神经鞘瘤(BPNST)的诊断准确性。我们对 MEDLINE、Embase、Scopus、Cochrane 图书馆和 2019 年 12 月以前的灰色文献进行了系统评价。纳入的文章至少涉及 10 例患者,并评估了 MRI 检测 MPNST 的准确性。两位审稿人独立从纳入的文章中提取临床和影像学数据,以计算敏感性、特异性、PPV、NPV 和准确性。使用双变量混合效应回归模型进行荟萃分析。使用 QUADAS-2 评估偏倚风险。共纳入 15 项研究,涉及 798 个病变(252 个 MPNST 和 546 个 BPNST)。使用特征组合时,MRI 检测 MPNST 的汇总和加权敏感性、特异性和 AUC 值分别为 68%(95%CI,52-80%)、93%(95%CI,85-97%)和 0.89(95%CI,0.86-0.92),使用弥散受限结合或不结合特征时,分别为 88%(95%CI,74-95%)、94%(95%CI,89-96%)和 0.97(95%CI,0.95-0.98)。由于数据不足,无法进行亚组分析,如神经纤维瘤病 1 型(NF1)患者与非 NF1 患者。患者选择的偏倚风险主要为高或不明确,检查方法的偏倚风险为混合,参考标准的偏倚风险为低,流程和时间的偏倚风险为不明确。结合弥散受限等特征可优化 MRI 检测 MPNST 的诊断准确性。然而,由于文献存在局限性,包括变异性和偏倚风险,需要进一步开展方法学严谨的研究,以便进行亚组分析,并进一步评估临床和 MRI 特征相结合对 MPNST 诊断的作用。