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18.11.2019 | Original Article

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

verfasst von: Deniz Alis, Omer Bagcilar, Yeseren Deniz Senli, Mert Yergin, Cihan Isler, Naci Kocer, Civan Islak, Osman Kizilkilic

Erschienen in: Japanese Journal of Radiology | Ausgabe 2/2020

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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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Titel: Machine learning-based quantitative texture analysis of conventional MRI combined with ADC maps for assessment of IDH1 mutation in high-grade gliomas
verfasst von: Deniz Alis
Omer Bagcilar
Yeseren Deniz Senli
Mert Yergin
Cihan Isler
Naci Kocer
Civan Islak
Osman Kizilkilic
Publikationsdatum: 18.11.2019
Verlag: Springer Japan
Erschienen in: Japanese Journal of Radiology / Ausgabe 2/2020
Print ISSN: 1867-1071
Elektronische ISSN: 1867-108X
DOI: https://doi.org/10.1007/s11604-019-00902-7

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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