%0 Journal Article
%T Diagnostic accuracy of a machine learning-based radiomics approach of MR in predicting IDH mutations in glioma patients: a systematic review and meta-analysis.
%A Chen X
%A Lei J
%A Wang S
%A Zhang J
%A Gou L
%J Front Oncol
%V 14
%N 0
%D 2024
%M 39139289
%F 5.738
%R 10.3389/fonc.2024.1409760
%X <B>UNASSIGNED: </B>To assess the diagnostic accuracy of machine learning (ML)-based radiomics for predicting isocitrate dehydrogenase (IDH) mutations in patients with glioma.<BR><B>UNASSIGNED: </B>A systematic search of PubMed, Web of Science, Embase, and the Cochrane Library from inception to 1 September 2023, was conducted to collect all articles investigating the diagnostic performance of ML for the prediction of IDH mutations in gliomas. Two reviewers independently screened all papers for eligibility. Methodological quality and risk of bias were assessed using the METhodological RadiomICs Score and Quality Assessment of Diagnostic Accuracy Studies-2, respectively. The pooled sensitivity, specificity, and 95% confidence intervals were calculated, and the area under the receiver operating characteristic curve (AUC) was obtained.<BR><B>UNASSIGNED: </B>In total, 14 original articles assessing 1740 patients with gliomas were included. The AUC of ML for predicting IDH mutation was 0.90 (0.87-0.92). The pooled sensitivity, specificity, and diagnostic odds ratio were 0.83 (0.71-0.90), 0.84 (0.74-0.90), and 25 (12,50) respectively. In subgroup analyses, modeling methods, glioma grade, and the combination of magnetic resonance imaging and clinical features affected the diagnostic performance in predicting IDH mutations in gliomas.<BR><B>UNASSIGNED: </B>ML-based radiomics demonstrated excellent diagnostic performance in predicting IDH mutations in gliomas. Factors influencing the diagnosis included the modeling methods employed, glioma grade, and whether the model incorporated clinical features.<BR><B>UNASSIGNED: </B>https://www.crd.york.ac.uk/PROSPERO/#myprospero, PROSPERO registry (CRD 42023395444).