AI Article Synopsis

  • This systematic review and meta-analysis assessed how accurately MRI can differentiate malignant peripheral nerve sheath tumors (MPNSTs) from benign ones (BPNSTs) using data from various medical databases.
  • A total of 15 studies with 798 lesions were analyzed, revealing that MRI has a pooled sensitivity of 68% and specificity of 93% for detecting MPNSTs, with even better results when combining features like diffusion restriction.
  • The review highlights limitations in the existing research, including high risk of bias and the need for more robust studies to explore subgroup analyses and improve MRI diagnostic accuracy for MPNSTs.

Article Abstract

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.

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Source
http://dx.doi.org/10.2214/AJR.20.23403DOI Listing

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