MRI radiomic features to predict idh1 mutation status in gliomas: A machine learning approach using gradient tree boosting

Yu Sakai, Chen Yang, Shingo Kihira, Nadejda Tsankova, Fahad Khan, Adilia Hormigo, Albert Lai, Timothy Cloughesy, Kambiz Nael

Research output: Contribution to journalArticlepeer-review

19 Scopus citations


In patients with gliomas, isocitrate dehydrogenase 1 (IDH1) mutation status has been studied as a prognostic indicator. Recent advances in machine learning (ML) have demonstrated promise in utilizing radiomic features to study disease processes in the brain. We investigate whether ML analysis of multiparametric radiomic features from preoperative Magnetic Resonance Imaging (MRI) can predict IDH1 mutation status in patients with glioma. This retrospective study included patients with glioma with known IDH1 status and preoperative MRI. Radiomic features were extracted from Fluid-Attenuated Inversion Recovery (FLAIR) and Diffusion-Weighted-Imaging (DWI). The dataset was split into training, validation, and testing sets by stratified sampling. Synthetic Minority Oversampling Technique (SMOTE) was applied to the training sets. eXtreme Gradient Boosting (XGBoost) classifiers were trained, and the hyperparameters were tuned. Receiver operating characteristic curve (ROC), accuracy, and f1-scores were collected. A total of 100 patients (age: 55 ± 15, M/F 60/40); with IDH1 mutant (n = 22) and IDH1 wildtype (n = 78) were included. The best performance was seen with a DWI-trained XGBoost model, which achieved ROC with Area Under the Curve (AUC) of 0.97, accuracy of 0.90, and f1-score of 0.75 on the test set. The FLAIR-trained XGBoost model achieved ROC with AUC of 0.95, accuracy of 0.90, f1-score of 0.75 on the test set. A model that was trained on combined FLAIR-DWI radiomic features did not provide incremental accuracy. The results show that a XGBoost classifier using multiparametric radiomic features derived from preoperative MRI can predict IDH1 mutation status with > 90% accuracy.

Original languageEnglish (US)
Article number8004
Pages (from-to)1-27
Number of pages27
JournalInternational Journal of Molecular Sciences
Issue number21
StatePublished - Nov 1 2020
Externally publishedYes


  • DWI
  • Glioma
  • IDH1
  • Machine learning
  • Radiomics

ASJC Scopus subject areas

  • Catalysis
  • Molecular Biology
  • Spectroscopy
  • Computer Science Applications
  • Physical and Theoretical Chemistry
  • Organic Chemistry
  • Inorganic Chemistry


Dive into the research topics of 'MRI radiomic features to predict idh1 mutation status in gliomas: A machine learning approach using gradient tree boosting'. Together they form a unique fingerprint.

Cite this