Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2020 Oct 27;21(21):8004.
doi: 10.3390/ijms21218004.

MRI Radiomic Features to Predict IDH1 Mutation Status in Gliomas: A Machine Learning Approach using Gradient Tree Boosting

Yu Sakai  1 , Chen Yang  1   2 , Shingo Kihira  1 , Nadejda Tsankova  3 , Fahad Khan  3 , Adilia Hormigo  4 , Albert Lai  5 , Timothy Cloughesy  5 , Kambiz Nael  1   6
Affiliations

MRI Radiomic Features to Predict IDH1 Mutation Status in Gliomas: A Machine Learning Approach using Gradient Tree Boosting

Yu Sakai et al. Int J Mol Sci. .

Abstract

Patients with gliomas, isocitrate dehydrogenase 1 (IDH1) mutation status have 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 Diffused 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.

Keywords: DWI; IDH1; glioma; machine learning; radiomics.

PubMed Disclaimer

Conflict of interest statement

Kambiz Nael is a consultant to Olea, none for others.

Figures

Figure A1
Figure A1
Spearman Correlation Matrix of DWI Radiomic Features.
Figure A2
Figure A2
Spearman Correlation Matrix of FLAIR Radiomic Features.
Figure 1
Figure 1
Receiver Operating Characteristic Curves with calculated AUC for (A) Diffusion-Weighted-Imaging (DWI), (B) Fluid-Attenuated Inversion Recovery (FLAIR), and (C) DWI-FLAIR XGBoost models.
Figure 2
Figure 2
Study Design Diagram.
Figure 3
Figure 3
Flowchart of Patient Inclusion and Exclusion.
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

See all "Cited by" articles

References

    1. Weller M., Wick W., Aldape K., Brada M., Berger M., Pfister S.M., Nishikawa R., Rosenthal M., Wen P.Y., Stupp R., et al. Glioma. Nat. Rev. Dis. Primers. 2015;1:15017. doi: 10.1038/nrdp.2015.17. - DOI - PubMed
    1. Bralten L.B.C., Kloosterhof N.K., Balvers R., Sacchetti A., Lapre L., Lamfers M., Leenstra S., De Jonge H., Kros J.M., Jansen E.E.W., et al. IDH1 R132H decreases proliferation of glioma cell lines in vitro and in vivo. Ann. Neurol. 2011;69:455–463. doi: 10.1002/ana.22390. - DOI - PubMed
    1. Wang H.-Y., Tang K., Liang T.-Y., Zhang W.-Z., Li J.-Y., Wang W., Hu H.-M., Li M.-Y., Wang H.-Q., He X.-Z., et al. The comparison of clinical and biological characteristics between IDH1 and IDH2 mutations in gliomas. J. Exp. Clin. Cancer Res. 2016;35:1–9. doi: 10.1186/s13046-016-0362-7. - DOI - PMC - PubMed
    1. Hartmann C., Meyer J., Balss J., Capper D., Mueller W., Christians A., Felsberg J., Wolter M., Mawrin C., Wick W., et al. Type and frequency of IDH1 and IDH2 mutations are related to astrocytic and oligodendroglial differentiation and age: A study of 1,010 diffuse gliomas. Acta Neuropathol. 2009;118:469–474. doi: 10.1007/s00401-009-0561-9. - DOI - PubMed
    1. Bent M.J.V.D., Dubbink H.J., Marie Y., Brandes A.A., Taphoorn M.J., Wesseling P., Frenay M., Tijssen C.C., Lacombe D., Idbaih A., et al. IDH1 and IDH2 Mutations Are Prognostic but not Predictive for Outcome in Anaplastic Oligodendroglial Tumors: A Report of the European Organization for Research and Treatment of Cancer Brain Tumor Group. Clin. Cancer Res. 2010;16:1597–1604. doi: 10.1158/1078-0432.CCR-09-2902. - DOI - PubMed

Substances