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A Comparison of Three Different Deep Learning-Based Models to Predict the MGMT Promoter Methylation Status in Glioblastoma Using Brain MRI

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Abstract

Glioblastoma (GBM) is the most common primary malignant brain tumor in adults. The standard treatment for GBM consists of surgical resection followed by concurrent chemoradiotherapy and adjuvant temozolomide. O-6-methylguanine-DNA methyltransferase (MGMT) promoter methylation status is an important prognostic biomarker that predicts the response to temozolomide and guides treatment decisions. At present, the only reliable way to determine MGMT promoter methylation status is through the analysis of tumor tissues. Considering the complications of the tissue-based methods, an imaging-based approach is preferred. This study aimed to compare three different deep learning-based approaches for predicting MGMT promoter methylation status. We obtained 576 T2WI with their corresponding tumor masks, and MGMT promoter methylation status from, The Brain Tumor Segmentation (BraTS) 2021 datasets. We developed three different models: voxel-wise, slice-wise, and whole-brain. For voxel-wise classification, methylated and unmethylated MGMT tumor masks were made into 1 and 2 with 0 background, respectively. We converted each T2WI into 32 × 32 × 32 patches. We trained a 3D-Vnet model for tumor segmentation. After inference, we constructed the whole brain volume based on the patch’s coordinates. The final prediction of MGMT methylation status was made by majority voting between the predicted voxel values of the biggest connected component. For slice-wise classification, we trained an object detection model for tumor detection and MGMT methylation status prediction, then for final prediction, we used majority voting. For the whole-brain approach, we trained a 3D Densenet121 for prediction. Whole-brain, slice-wise, and voxel-wise, accuracy was 65.42% (SD 3.97%), 61.37% (SD 1.48%), and 56.84% (SD 4.38%), respectively.

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Code Availability

A link to the code will be shared after peer review.

Data Availability

The raw data required to reproduce the above findings are available to download from http://braintumorsegmentation.org/.

Abbreviations

BraTS:

Brain Tumor Segmentation

CCR:

Concurrent chemoradiation therapy

TMZ:

Temozolomide

GBM:

Glioblastoma

MGMT:

O-6-methylguanine-DNA methyltransferase

AUCROC:

Area under the receiver operating characteristic curve

TCIA:

The Cancer Imaging Archive

TCGA:

The Cancer Genome Atlas

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Acknowledgements

This project was supported in part by funding from the Mayo Clinic Center for Individualized Medicine (CIM).

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  1. Radiology Informatics Lab, Department of Radiology, Mayo Clinic, S.W, 200 1St Street, Rochester, MN, 55905, USA

    Shahriar Faghani, Bardia Khosravi, Mana Moassefi, Gian Marco Conte & Bradley J. Erickson

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Faghani, S., Khosravi, B., Moassefi, M. et al. A Comparison of Three Different Deep Learning-Based Models to Predict the MGMT Promoter Methylation Status in Glioblastoma Using Brain MRI. J Digit Imaging 36, 837–846 (2023). https://doi.org/10.1007/s10278-022-00757-x

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