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SYSTEMATIC REVIEW article

Front. Oncol.
Sec. Neuro-Oncology and Neurosurgical Oncology
Volume 14 - 2024 | doi: 10.3389/fonc.2024.1409760
This article is part of the Research Topic Application of Emerging Technologies in the Diagnosis and Treatment of Patients with Brain Tumors: New Frontiers in Imaging for Neuro-oncology View all articles

Diagnostic Accuracy of Machine Learning-Based Radiomics approach of MR in predicting IDH mutations in glioma patients: A systematic review and meta-Analysis

Provisionally accepted
Xiaoli Chen Xiaoli Chen Junqiang Lei Junqiang Lei *Shuaiwen Wang Shuaiwen Wang Jing Zhang Jing Zhang Lubin Gou Lubin Gou
  • Department of Radiology, First Hospital of Lanzhou University, Lanzhou, China

The final, formatted version of the article will be published soon.

    Objectives: To assess the diagnostic accuracy of machine learning (ML)-based radiomics for predicting isocitrate dehydrogenase (IDH) mutations in patients with glioma.Methods: A systematic search of PubMed, Web of Science, Embase, and the Cochrane Library from inception to September 1, 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.Results: Fourteen 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.Conclusion: 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.

    Keywords: Glioma, Isocitrate dehydrogenase (IDH), MRI, machine learning, deep learning, Radiomics

    Received: 31 Mar 2024; Accepted: 08 Jul 2024.

    Copyright: © 2024 Chen, Lei, Wang, Zhang and Gou. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Junqiang Lei, Department of Radiology, First Hospital of Lanzhou University, Lanzhou, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.