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

ALİS D. C., Bagcilar O., Senli Y. D., Yergin M., İŞLER C., Kocer N., ...Daha Fazla

JAPANESE JOURNAL OF RADIOLOGY, cilt.38, sa.2, ss.135-143, 2020 (SCI-Expanded) identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 38 Sayı: 2
  • Basım Tarihi: 2020
  • Doi Numarası: 10.1007/s11604-019-00902-7
  • Dergi Adı: JAPANESE JOURNAL OF RADIOLOGY
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Agricultural & Environmental Science Database, Biotechnology Research Abstracts, CINAHL, EMBASE, MEDLINE
  • Sayfa Sayıları: ss.135-143
  • Anahtar Kelimeler: Artificial intelligence, IDH1, Gliomas, Machine-learning, Texture analysis, CLASSIFICATION, NEUROONCOLOGY, ASSOCIATION, PREDICTION, FEATURES, SYSTEM, NUMBER
  • Acıbadem Mehmet Ali Aydınlar Üniversitesi Adresli: Evet

Özet

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.