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. 2022 Aug;12(8):4033-4046.
doi: 10.21037/qims-22-34.

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

Mert Karabacak #  1 , Burak Berksu Ozkara #  1 , Seren Mordag  2 , Sotirios Bisdas  3
Affiliations

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

Mert Karabacak et al. Quant Imaging Med Surg. 2022 Aug.

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.

Methods: A systematic search of Cochrane Library, Web of Science, Medline, and Scopus was conducted to identify relevant publications until August 1, 2021. Articles were included if all the following criteria were met: (I) patients with histopathologically confirmed World Health Organization (WHO) grade II, III, or IV gliomas; (II) histopathological examination with the IDH mutation; (III) DL was used to predict the IDH mutation status; (IV) sufficient data for reconstruction of confusion matrices in terms of the diagnostic performance of the DL algorithms; and (V) original research articles. Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) and Checklist for Artificial Intelligence in Medical Imaging (CLAIM) was used to assess the studies' quality. Bayes theorem was utilized to calculate the posttest probability.

Results: Four studies with a total of 1,295 patients were included. In the training set, the pooled sensitivity, specificity, and area under the summary receiver operating characteristic (SROC) curve were 93.9%, 90.9% and 0.958, respectively. In the validation set, the pooled sensitivity, specificity, and area under the SROC curve were 90.8%, 85.5% and 0.939, respectively. With a known pretest probability of 80.2%, the Bayes theorem yielded a posttest probability of 97.6% and 96.0% for a positive test and 27.0% and 30.6% for a negative test for training sets and validation sets, respectively.

Discussion: This is the first meta-analysis that summarizes the diagnostic performance of DL in predicting IDH mutation status in gliomas via the Bayes theorem. DL algorithms demonstrate excellent diagnostic performance in predicting IDH mutation in gliomas. Radiomic features associated with IDH mutation, and its underlying pathophysiology extracted from advanced MRI may improve prediction probability. However, more studies are required to optimize and increase its reliability. Limitations include obtaining some data via email and lack of training and test sets statistics.

Keywords: Deep learning (DL); gliomas; isocitrate dehydrogenase (IDH); magnetic resonance imaging (MRI); radiomics.

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Conflict of interest statement

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-22-34/coif). The authors have no conflicts of interest to declare.

Figures

Figure 1
Figure 1
The study selection process. n, number; IDH, isocitrate dehydrogenase.
Figure 2
Figure 2
Quality assessment with QUADAS-2. QUADAS-2, Quality Assessment of Diagnostic Accuracy Studies-2.
Figure 3
Figure 3
Forest plots of the deep learning algorithms in training sets. (A) Pooled sensitivity. (B) Specificity. (C) Diagnostic odds ratio. CI, confidence interval; OR, odds ratio.
Figure 4
Figure 4
Summary receiver operating characteristic curve of the diagnostic performance of deep learning algorithms for prediction of IDH mutation status in training sets. SROC, summary receiver operating characteristic; CI, confidence interval; IDH, isocitrate dehydrogenase.
Figure 5
Figure 5
Fagan’s nomogram for calculation of positive and negative posttest probabilities of IDH mutation status prediction by deep learning algorithms in training sets. IDH, isocitrate dehydrogenase.
Figure 6
Figure 6
Forest plots of deep learning algorithms in validation sets. (A) Pooled sensitivity. (B) Specificity. (C) Diagnostic odds ratio. CI, confidence interval; OR, odds ratio.
Figure 7
Figure 7
Summary receiver operating characteristic curve of the diagnostic performance of deep learning algorithms for prediction of IDH mutation status in validation sets. SROC, summary receiver operating characteristic; CI, confidence interval; IDH, isocitrate dehydrogenase.
Figure 8
Figure 8
Fagan’s nomogram for calculation of positive and negative posttest probabilities of IDH mutation status prediction by deep learning algorithms in validation sets. IDH, isocitrate dehydrogenase.
See this image and copyright information in PMC

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