Deep learning for prediction of isocitrate dehydrogenase mutation in gliomas: a critical approach, systematic review and meta-analysis of the diagnostic test performance using a Bayesian approach

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Karabacak M., Ozkara B. B., Mordag S., Bisdas S.

QUANTITATIVE IMAGING IN MEDICINE AND SURGERY, vol.12, pp.4033-4046, 2022 (SCI-Expanded, Scopus) identifier identifier identifier

  • Publication Type: Article / Review
  • Volume: 12
  • Publication Date: 2022
  • Doi Number: 10.21037/qims-22-34
  • Journal Name: QUANTITATIVE IMAGING IN MEDICINE AND SURGERY
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.4033-4046
  • Keywords: Deep learning (DL), gliomas, isocitrate dehydrogenase (IDH), radiomics, magnetic resonance imaging (MRI), GENOMIC ANALYSIS, NEURAL-NETWORKS, IDH2 MUTATIONS, GRADE GLIOMAS, GLIOBLASTOMA, RADIOMICS, MACHINE, SURVIVAL, CURVE
  • Open Archive Collection: AVESIS Open Access Collection
  • Istanbul University-Cerrahpasa Affiliated: Yes

Abstract

Background: Conventionally, identifying isocitrate dehydrogenase (IDH) mutation in gliomas is based on histopathological analysis of tissue specimens acquired via stereotactic biopsy or definitive resection. Accurate pre-treatment prediction of IDH mutation status using magnetic resonance imaging (MRI) can guide clinical decision-making. We aim to evaluate the diagnostic performance of deep learning (DL) to determine IDH mutation status in gliomas.