MRI Diagnostic Accuracy in Detecting Malignant Peripheral Nerve Sheath Tumors: A Systematic Review and Meta-Analysis. (16th November 2020)

• Record Type: Journal Article
• Authors: Jack, Andrew S; Jacques, Line G; Katlariwala, Prayash; Low, Gavin; McInnes, Matthew; Murad, Mohammad; Wilson, Mitchell
• Is Part Of: Neurosurgery. Volume 67(2010)Supplement 1
• Publication Date: 2020-11-16
• Subjects: Nervous system -- Surgery -- Periodicals; 617.48005
• DOI: 10.1093/neuros/nyaa447_600
• Languages: English
• ISSNs: 0148-396X

Abstract

Abstract: INTRODUCTION: Malignant peripheral nerve sheath tumors (MPNSTs) are soft-tissue sarcomas with a grim prognosis. Distinguishing these tumors from their benign counterpart (BPNSTs) can be difficult with more recent studies suggesting increased risk of neurological deficit and resection difficulty with biopsy. Non-invasive methods of increasing MPNST diagnostic accuracy are required. METHODS: A systematic review of MEDLINE, EMBASE, Scopus, the Cochrane Library, and the Grey Literature from inception to December 2019 was performed. Original articles with >10 patients evaluating the diagnostic accuracy of MRI for detecting MPNSTs were included. Two reviewers independently extracted clinical and radiological data from included articles to calculate sensitivity, specificity, positive and negative predictive values, and accuracy. Meta-analysis was performed using a bivariate mixed-effects regression model. Risk of bias was evaluated using QUADAS-2. RESULTS: Fifteen studies with 798 lesions (252 MPNSTs and 546 BPNSTs) were included in the analysis. The respective pooled and weighted sensitivity, specificity, and area under curve values for MRI in detecting MPNSTs were 68% (95% confidence interval [CI] 52–80%), 93% (95%CI 85–97%), and 0.89 (95%CI 0.86-0.92) when using a combination of features and 88% (95%CI 74–95%), 94% (95%CI 89–96%), and 0.97 (95%CI 0.95-0.98) using either diffusion restriction or feature combination. Subgroup analysis such as neurofibromatosis type 1 (NF1) versus non-NF1 patients could not be performed due to insufficient data. Risk of bias amongst included studies was predominantly high or unclear for patient selection, both high and low for index test, low for reference standard, and unclear for flow and timing. CONCLUSION: Combining clinical and MRI features such as diffusion restriction optimizes the diagnostic accuracy of MRI for detecting MPNSTs. However, limitations within the literature such as heterogeneity and risk of bias necessitate more methodologically rigorous studies to allow subgroup analysis and further evaluate combining clinical and MRI features for MPNST diagnosis.