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Journal of Neuro-Oncology

, Volume 142, Issue 2, pp 299–307 | Cite as

Machine learning reveals multimodal MRI patterns predictive of isocitrate dehydrogenase and 1p/19q status in diffuse low- and high-grade gliomas

  • Hao Zhou
  • Ken Chang
  • Harrison X. BaiEmail author
  • Bo Xiao
  • Chang Su
  • Wenya Linda Bi
  • Paul J. Zhang
  • Joeky T. Senders
  • Martin Vallières
  • Vasileios K. Kavouridis
  • Alessandro Boaro
  • Omar Arnaout
  • Li YangEmail author
  • Raymond Y. HuangEmail author
Clinical Study

Abstract

Purpose

Isocitrate dehydrogenase (IDH) and 1p19q codeletion status are importantin providing prognostic information as well as prediction of treatment response in gliomas. Accurate determination of the IDH mutation status and 1p19q co-deletion prior to surgery may complement invasive tissue sampling and guide treatment decisions.

Methods

Preoperative MRIs of 538 glioma patients from three institutions were used as a training cohort. Histogram, shape, and texture features were extracted from preoperative MRIs of T1 contrast enhanced and T2-FLAIR sequences. The extracted features were then integrated with age using a random forest algorithm to generate a model predictive of IDH mutation status and 1p19q codeletion. The model was then validated using MRIs from glioma patients in the Cancer Imaging Archive.

Results

Our model predictive of IDH achieved an area under the receiver operating characteristic curve (AUC) of 0.921 in the training cohort and 0.919 in the validation cohort. Age offered the highest predictive value, followed by shape features. Based on the top 15 features, the AUC was 0.917 and 0.916 for the training and validation cohort, respectively. The overall accuracy for 3 group prediction (IDH-wild type, IDH-mutant and 1p19q co-deletion, IDH-mutant and 1p19q non-codeletion) was 78.2% (155 correctly predicted out of 198).

Conclusion

Using machine-learning algorithms, high accuracy was achieved in the prediction of IDH genotype in gliomas and moderate accuracy in a three-group prediction including IDH genotype and 1p19q codeletion.

Keywords

Glioma Isocitrate dehydrogenase (IDH) 1p19q codeletion Machine learning Random forest MRI 

Notes

Acknowledgements

This work was supported by the Natural Science Foundation of China (81301988 to L.Y.), ShenghuaYuying Project of Central South University to L.Y. and the Natural Science Foundation of Hunan Province for Young Scientists, China (Grant No: 2018JJ3709 to Li Yang). This project was supported by a training grant from the National Institute of Biomedical Imaging and Bioengineering (NIBIB) of the National Institutes of Health under Award Number 5T32EB1680 to K. Chang. This project was supported by the RSNA research fellow Grant to H.X.B (RF1802), SIR Foundation resident research grant to H.X.B, and Research Fund for International Young Scientist by the National Natural Science Foundation of China (818580410556 to H.X.B.). This research was carried out in whole or in part at the Athinoula A. Martinos Center for Biomedical Imaging at the Massachusetts General Hospital, using resources provided by the Center for Functional Neuroimaging Technologies, P41EB015896, a P41 Biotechnology Resource Grant supported by the National Institute of Biomedical Imaging and Bioengineering (NIBIB), National Institutes of Health.

Funding

This work was supported by the Natural Science Foundation of China (81301988 to L.Y.), ShenghuaYuying Project of Central South University to L.Y. Project supported by the Natural Science Foundation of Hunan Province for Young Scientists, China (Grant No: 2018JJ3709 to Li Yang). This project was supported by a training grant from the National Institute of Biomedical Imaging and Bioengineering (NIBIB) of the National Institutes of Health under Award Number 5T32EB1680 to K. Chang. This project was supported by the RSNA research fellow grant to H.X.B (RF1802), SIR Foundation resident research grant to H.X.B, and Research Fund for International Young Scientist by the National Natural Science Foundation of China (818580410556 to H.X.B.).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

For this type of study, formal consent is not required.

Supplementary material

11060_2019_3096_MOESM1_ESM.pdf (494 kb)
Supplementary material 1 (PDF 493 KB)

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of NeurologyXiangya Hospital of Central South UniversityChangshaChina
  2. 2.Department of RadiologyBrigham and Women’s HospitalBostonUSA
  3. 3.Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General HospitalHarvard Medical SchoolBostonUSA
  4. 4.Department of NeurologyThe Second Xiangya Hospital of Central South UniversityChangshaChina
  5. 5.Department of RadiologyHospital of the University of PennsylvaniaPhiladelphiaUSA
  6. 6.Department of PathologyHospital of the University of PennsylvaniaPhiladelphiaUSA
  7. 7.Yale School of MedicineNew HavenUSA
  8. 8.Medical Physics UnitMcGill UniversityMontréalCanada
  9. 9.Center for Skull Base and Pituitary Surgery, Department of Neurosurgery, Brigham and Women’s HospitalHarvard Medical SchoolBostonUSA
  10. 10.Computational Neuroscience Outcomes Center, Department of Neurosurgery, Brigham and Women’s HospitalHarvard Medical SchoolBostonUSA

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