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Diagnostic Accuracy of MRI for the Detection of Malignant Peripheral Nerve Sheath Tumors: A Systematic Review and Meta-Analysis.
Wilson, Mitchell P; Katlariwala, Prayash; Low, Gavin; Murad, Mohammad H; McInnes, Matthew D F; Jacques, Line; Jack, Andrew S.
Afiliação
  • Wilson MP; Department of Radiology and Diagnostic Imaging, University of Alberta, 2B2.41 WMC, 8440-112 St NW, Edmonton, AB T6G 2B7, Canada.
  • Katlariwala P; Department of Radiology and Diagnostic Imaging, University of Alberta, 2B2.41 WMC, 8440-112 St NW, Edmonton, AB T6G 2B7, Canada.
  • Low G; Department of Radiology and Diagnostic Imaging, University of Alberta, 2B2.41 WMC, 8440-112 St NW, Edmonton, AB T6G 2B7, Canada.
  • Murad MH; Evidence-Based Practice Center, Mayo Clinic, Rochester, MN.
  • McInnes MDF; Departments of Radiology and Epidemiology, University of Ottawa, Ottawa Hospital Research Institute, Ottawa, ON, Canada.
  • Jacques L; Department of Neurosurgery, University of California, San Francisco, CA.
  • Jack AS; Department of Neurosurgery, University of California, San Francisco, CA.
AJR Am J Roentgenol ; 217(1): 31-39, 2021 07.
Article em En | MEDLINE | ID: mdl-33909462
ABSTRACT
OBJECTIVE. This systematic review and meta-analysis evaluates the diagnostic accuracy of MRI for differentiating malignant (MPNSTs) from benign peripheral nerve sheath tumors (BPNSTs). MATERIALS AND METHODS. 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. RESULTS. 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. CONCLUSION. 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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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Imageamento por Ressonância Magnética / Neoplasias de Bainha Neural Tipo de estudo: Diagnostic_studies / Prognostic_studies / Systematic_reviews Limite: Humans Idioma: En Revista: AJR Am J Roentgenol Ano de publicação: 2021 Tipo de documento: Article País de afiliação: Canadá

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Imageamento por Ressonância Magnética / Neoplasias de Bainha Neural Tipo de estudo: Diagnostic_studies / Prognostic_studies / Systematic_reviews Limite: Humans Idioma: En Revista: AJR Am J Roentgenol Ano de publicação: 2021 Tipo de documento: Article País de afiliação: Canadá