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Machine learning-based quantitative texture analysis of conventional MRI combined with ADC maps for assessment of IDH1 mutation in high-grade gliomas

Japanese Journal of Radiology volume 38, pages 135–143 (2020)Cite this article

Abstract

Purpose

To assess the performance of texture analysis of conventional magnetic resonance imaging (MRI) and apparent diffusion coefficient (ADC) maps in predicting IDH1 status in high-grade gliomas (HGG).

Materials and methods

A total of 142 patients with HGG were included in the study. IDH1 mutation was present in 48 of 142 HGG (33.8%). Patients were randomly divided into the training cohort (n = 96) and the validation cohort (n = 46). Texture features were extracted via regions of interest on axial T2WI FLAIR, post-contrast T1WI, and ADC maps covering the whole volume of the tumors. The training cohort was used to train the random forest classifier, and the diagnostic performance of the pre-trained model was tested on the validation cohort.

Results

The random forest model of conventional MRI sequences and ADC images achieved diagnostic accuracy of 82.2% and 80.4% in predicting IDH1 status in the validation cohorts, respectively. The combined model of T2WI FLAIR, post-contrast T1WI, and ADC images exhibited the highest diagnostic accuracy equating 86.94% in the validation cohort.

Conclusion

Texture analysis of conventional MRI sequences enhanced by ML analysis can accurately predict the IDH1 status of HGG. Adding textural analysis of ADC maps to conventional MRI results in incremental diagnostic performance.

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Author information

Affiliations

  1. Department of Radiology, Istanbul Mehmet Akif Ersoy Thoracic and Cardiovascular Surgery Training and Research Hospital, Halkali/Istanbul, Turkey

    Deniz Alis

  2. Department of Radiology, Istanbul University-Cerrahpasa, Cerrahpasa Faculty of Medicine, KMPasa, Istanbul, Turkey

    Omer Bagcilar, Yeseren Deniz Senli, Naci Kocer, Civan Islak & Osman Kizilkilic

  3. Department of Software Engineering and Applied Sciences, Bahcesehir University, Istanbul, Turkey

    Mert Yergin

  4. Department of Neurosurgery, Istanbul University-Cerrahpasa, Cerrahpasa Faculty of Medicine, KMPasa, Istanbul, Turkey

    Cihan Isler

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  1. Deniz Alis
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Correspondence to Deniz Alis.

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Ethical Statement

The local ethics committee approved this retrospective study conducted between January 2011 and May 2019. The committee waived the need for informed consent for the de-identified use of medical and radiological data. All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

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Alis, D., Bagcilar, O., Senli, Y.D. et al. Machine learning-based quantitative texture analysis of conventional MRI combined with ADC maps for assessment of IDH1 mutation in high-grade gliomas. Jpn J Radiol 38, 135–143 (2020). https://doi.org/10.1007/s11604-019-00902-7

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  • DOI: https://doi.org/10.1007/s11604-019-00902-7

Keywords

  • Artificial intelligence
  • IDH1
  • Gliomas
  • Machine-learning
  • Texture analysis