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A Deep Learning Approach to Glioblastoma Radiogenomic Classification Using Brain MRI

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Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries (BrainLes 2021)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 12963))

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Abstract

A malignant brain tumor known as a glioblastoma is an extremely life-threatening condition. It has been proven that the existence of a specific genetic sequence in the tumor known as MGMT promoter methylation is a favourable prognostic factor and a sign of how well a patient will respond to chemotherapy. Currently, the only way to identify the presence of the MGMT promoter is to perform a genetic analysis that requires surgical intervention. The development of an accurate method for determining the presence of the MGMT promoter using only MRI would help to reduce the number of surgeries. In this work, we developed a method for glioblastoma classification using just MRI by choosing an appropriate loss function, neural network architecture and ensembling trained models. This problem was successfully solved as part of the “RSNA-MICCAI Brain Tumor Radiogenomic Classification” competition, and the proposed algorithm was included in the top 5% of best solutions.

Supported by the RSNA and the MICCAI Society.

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Notes

  1. 1.

    https://www.kaggle.com/greylord1996/resnet50-all-mri?scriptVersionId=76647838.

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Acknowledgements

We would like to thank the Radiological Society of North America (RSNA) and the Medical Image Computing and Computer Assisted Intervention Society for providing the MRI scans and Kaggle, Inc. for hosting and organizing the “RSNA-MICCAI Brain Tumor Radiogenomic Classification” competition. We would also like to thank the Kaggle community for their valuable information.

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Authors and Affiliations

  1. Moscow Institute of Physics and Technology, Moscow, Russia

    Aleksandr Emchinov

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  1. Aleksandr Emchinov
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Correspondence to Aleksandr Emchinov .

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Editors and Affiliations

  1. Sano Centre for Computational Personaliz, Kraków, Poland

    Alessandro Crimi

  2. University of Pennsylvania, Philadelphia, PA, USA

    Spyridon Bakas

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Emchinov, A. (2022). A Deep Learning Approach to Glioblastoma Radiogenomic Classification Using Brain MRI. In: Crimi, A., Bakas, S. (eds) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2021. Lecture Notes in Computer Science, vol 12963. Springer, Cham. https://doi.org/10.1007/978-3-031-09002-8_31

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  • DOI: https://doi.org/10.1007/978-3-031-09002-8_31

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