Version 1
: Received: 7 October 2024 / Approved: 8 October 2024 / Online: 8 October 2024 (12:07:00 CEST)
How to cite:
Nathani, P.; Bharadwaj, A. Radiogenomic Prediction of MGMT Promoter Methylation Status in Glioblastoma Using Multi-Modal MRI and EfficientNet. Preprints2024, 2024100583. https://doi.org/10.20944/preprints202410.0583.v1
Nathani, P.; Bharadwaj, A. Radiogenomic Prediction of MGMT Promoter Methylation Status in Glioblastoma Using Multi-Modal MRI and EfficientNet. Preprints 2024, 2024100583. https://doi.org/10.20944/preprints202410.0583.v1
Nathani, P.; Bharadwaj, A. Radiogenomic Prediction of MGMT Promoter Methylation Status in Glioblastoma Using Multi-Modal MRI and EfficientNet. Preprints2024, 2024100583. https://doi.org/10.20944/preprints202410.0583.v1
APA Style
Nathani, P., & Bharadwaj, A. (2024). Radiogenomic Prediction of MGMT Promoter Methylation Status in Glioblastoma Using Multi-Modal MRI and EfficientNet. Preprints. https://doi.org/10.20944/preprints202410.0583.v1
Chicago/Turabian Style
Nathani, P. and Anikait Bharadwaj. 2024 "Radiogenomic Prediction of MGMT Promoter Methylation Status in Glioblastoma Using Multi-Modal MRI and EfficientNet" Preprints. https://doi.org/10.20944/preprints202410.0583.v1
Abstract
MGMT promotor hypermethylation is the most important biomarker for predicting temozolomide sensitivity in aggressive glioblastoma (GBM), a disease with a poor prognosis. An MGMT status prediction without biopsy is mandatory for tailored therapy because methylated MGMT patients have better prognosis. To this end, this paper proposes the radiogenomic model for predicting the status of MGMT Promoter methylation in glioblastoma patients based on the multi-modal MRI data and the EfficientNet deep learning architecture. Perhaps using sophisticated machine learning-based non-invasive methods of genetic prognosis may decrease risky biopsies. The Magnetic Resonance Imaging (MRI) sequences in the BraTS21 competition dataset include T1-weighted and contrast-enhanced images, T2-weighted images, and Fluid Attenuated Inversion Recovery (FLAIR) images. Image preprocessing consisted of normalization, scaling, and data augmentation Fourier transformations for cross-modal alignment. The EfficientNet-b0 model was initially pre-trained on ImageNet and then fine-tuned to the binary classification of methylation and unmethylated MGMT. The models were evaluated based on AUC-ROC, accuracy, and the F1 score. The model’s capability of accurately identifying MGMT methylation status was moderate, with an AUC-ROC score of 0.62393 at its best. These differences influenced validation findings: overfit and resulted in accuracy variations for the small or unbalanced datasets. Applying multimodal MRI data elevated feature extraction, proving that deep learning models have radiogenic predictive power. Therefore, this study demonstrates that ALC and DL models can noninvasively estimate MGMT promoter methylation status. They also suffer from problems of overfitting or generalization, which will require some optimization. It might be safer than biopsies and can enhance approaches to glioblastoma treatment.
Medicine and Pharmacology, Neuroscience and Neurology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
