Predicting the MGMT Promoter Methylation Status in T2-FLAIR Magnetic Resonance Imaging Scans Using Machine Learning

Martyna Kurbiel, Agata Wijata, Jakub Nalepa

2024

Abstract

Glioblastoma is the most common form of brain cancer in adults, and is characterized by one of the worst prognosis, with median survival being less than one year. Magnetic resonance imaging (MRI) plays a key role in detecting and objectively tracking the disease by extracting quantifiable parameters of the tumor, such as its volume or bidimensional measurements. However, it has been shown that the presence a specific genetic sequence in a lesion, being the DNA repair enzyme O6 -methylguanine-DNA methyltransferase (MGMT) promoter methylation, may be effectively used to predict the patient’s responsiveness to chemotherapy. The invasive process of analyzing a tissue sample to verify the MGMT promoter methylation status is time-consuming, and may require performing multiple surgical interventions in longitudinal studies. Thus, building non-invasive techniques of predicting the genetic subtype of glioblastoma is of utmost practical importance to not only accelerate the overall process of determining the MGMT promoter methylation status in glioblastoma patients, but also to minimize the number of necessary surgeries. In this paper, we tackle this problem and propose an end-to-end machine learning classification pipeline benefitting from radiomic features extracted from brain MRI scans, and validate it over a well-established RSNA-MICCAI Brain Tumor Radiogenomic Classification benchmark dataset.

Paper Citation

in Harvard Style

Kurbiel M., Wijata A. and Nalepa J. (2024). Predicting the MGMT Promoter Methylation Status in T2-FLAIR Magnetic Resonance Imaging Scans Using Machine Learning. In Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM; ISBN 978-989-758-684-2, SciTePress, pages 872-879. DOI: 10.5220/0012467400003654

in Bibtex Style

@conference{icpram24,
author={Martyna Kurbiel and Agata Wijata and Jakub Nalepa},
title={Predicting the MGMT Promoter Methylation Status in T2-FLAIR Magnetic Resonance Imaging Scans Using Machine Learning},
booktitle={Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM},
year={2024},
pages={872-879},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012467400003654},
isbn={978-989-758-684-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM
TI - Predicting the MGMT Promoter Methylation Status in T2-FLAIR Magnetic Resonance Imaging Scans Using Machine Learning
SN - 978-989-758-684-2
AU - Kurbiel M.
AU - Wijata A.
AU - Nalepa J.
PY - 2024
SP - 872
EP - 879
DO - 10.5220/0012467400003654
PB - SciTePress