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Review
. 2023 Jan-Dec:30:10732748231169149.
doi: 10.1177/10732748231169149.

Brain Tumor Radiogenomic Classification of O6-Methylguanine-DNA Methyltransferase Promoter Methylation in Malignant Gliomas-Based Transfer Learning

Houneida Sakly  1 , Mourad Said  2 , Jayne Seekins  3 , Ramzi Guetari  4 , Naoufel Kraiem  1   5 , Mehrez Marzougui  6
Affiliations
Review

Brain Tumor Radiogenomic Classification of O6-Methylguanine-DNA Methyltransferase Promoter Methylation in Malignant Gliomas-Based Transfer Learning

Houneida Sakly et al. Cancer Control. 2023 Jan-Dec.

Abstract

Artificial Intelligence (AI) is the subject of a challenge and attention in the field of oncology and raises many promises for preventive diagnosis, but also fears, some of which are based on highly speculative visions for the classification and detection of tumors. A brain tumor that is malignant is a life-threatening disorder. Glioblastoma is the most prevalent kind of adult brain cancer and the 1 with the poorest prognosis, with a median survival time of less than a year. The presence of O6 -methylguanine-DNA methyltransferase (MGMT) promoter methylation, a particular genetic sequence seen in tumors, has been proven to be a positive prognostic indicator and a significant predictor of recurrence.This strong revival of interest in AI is modeled in particular to major technological advances which have significantly increased the performance of the predicted model for medical decision support. Establishing reliable forecasts remains a significant challenge for electronic health records (EHRs). By enhancing clinical practice, precision medicine promises to improve healthcare delivery. The goal is to produce improved prognosis, diagnosis, and therapy through evidence-based sub stratification of patients, transforming established clinical pathways to optimize care for each patient's individual requirements. The abundance of today's healthcare data, dubbed "big data," provides great resources for new knowledge discovery, potentially advancing precision treatment. The latter necessitates multidisciplinary initiatives that will use the knowledge, skills, and medical data of newly established organizations with diverse backgrounds and expertise.The aim of this paper is to use magnetic resonance imaging (MRI) images to train and evaluate your model to detect the presence of MGMT promoter methylation in this competition to predict the genetic subtype of glioblastoma based transfer learning. Our objective is to emphasize the basic problems in the developing disciplines of radiomics and radiogenomics, as well as to illustrate the computational challenges from the perspective of big data analytics.

Keywords: O6 -methylguanine-DNA methyltransferase promoter methylation; Transfer learning; artificial intelligence; big data; brain tumor; classification; radiogenomic.

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Conflict of interest statement

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Figures

Figure 1.
Figure 1.
Transfer learning architecture.
Figure 2.
Figure 2.
Radiogenomics system diagram.
Figure 3.
Figure 3.
Perfermance of models -based transfer learning.
Figure 4.
Figure 4.
Comparison of O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation levels in glioblastomas (GBMs) with unmethylated (n = 8) and methylated (n = 10) histograms of typical tumour mass.
Figure 5.
Figure 5.
The ROC curve.
Figure 6.
Figure 6.
The confusion matrix.
Figure 7.
Figure 7.
Minimum classification error.
See this image and copyright information in PMC

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