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

  • Clinical Study
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Journal of Neuro-Oncology Aims and scope Submit manuscript

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.

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Data availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

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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.).

Author information

Authors and Affiliations

  1. Department of Neurology, Xiangya Hospital of Central South University, Changsha, Hunan, China

    Hao Zhou & Bo Xiao

  2. Department of Radiology, Brigham and Women’s Hospital, 75 Francis Street, Boston, MA, 02215, USA

    Raymond Y. Huang

  3. Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA

    Ken Chang

  4. Department of Neurology, The Second Xiangya Hospital of Central South University, No.139 Middle Renmin Road, Changsha, 410011, Hunan, China

    Li Yang

  5. Department of Radiology, Hospital of the University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA, 19104, USA

    Harrison X. Bai

  6. Department of Pathology, Hospital of the University of Pennsylvania, Philadelphia, PA, 19104, USA

    Paul J. Zhang

  7. Yale School of Medicine, New Haven, CT, 06510, USA

    Chang Su

  8. Medical Physics Unit, McGill University, Montréal, Québec, Canada

    Martin Vallières

  9. Center for Skull Base and Pituitary Surgery, Department of Neurosurgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, 02215, USA

    Wenya Linda Bi

  10. Computational Neuroscience Outcomes Center, Department of Neurosurgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, 02215, USA

    Joeky T. Senders, Vasileios K. Kavouridis, Alessandro Boaro & Omar Arnaout

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  1. Hao Zhou
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Correspondence to Harrison X. Bai, Li Yang or Raymond Y. Huang.

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Zhou, H., Chang, K., Bai, H.X. et al. Machine learning reveals multimodal MRI patterns predictive of isocitrate dehydrogenase and 1p/19q status in diffuse low- and high-grade gliomas. J Neurooncol 142, 299–307 (2019). https://doi.org/10.1007/s11060-019-03096-0

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