MRI-Based Deep Learning Tools for MGMT Promoter Methylation Detection: A Thorough Evaluation

• Published in: Cancers Vol. 15; no. 8; p. 2253
• Main Authors: Robinet, Lucas, Siegfried, Aurore, Roques, Margaux, Berjaoui, Ahmad, Cohen-Jonathan Moyal, Elizabeth
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 12.04.2023; MDPI
• Subjects: Accuracy; Algorithms; Biopsy; Brain cancer; Brain tumors; Cancer; Chemotherapy; Classification; Computer Science; Data mining; Datasets; Deep Learning; DNA methylation; DNA repair; Enzymes; Genetic aspects; Glioblastoma; Glioblastoma multiforme; Gliomas; Health aspects; Information processing; Learning algorithms; Machine learning; Magnetic resonance imaging; Methods; Methylation; MGMT; MRI; O6-methylguanine-DNA methyltransferase; Patients; Prognosis; Radiation therapy; Radiomics; Temozolomide; Tumors; France; Taiwan; United States > US; deep learning; glioblastoma; MGMT promoter; confidence
• ISSN: 2072-6694; 2072-6694
• DOI: 10.3390/cancers15082253

Glioblastoma is the most aggressive primary brain tumor, which almost systematically relapses despite surgery (when possible) followed by radio-chemotherapy temozolomide-based treatment. Upon relapse, one option for treatment is another chemotherapy, lomustine. The efficacy of these chemotherapy regimens depends on the methylation of a specific gene promoter known as MGMT, which is the main prognosis factor for glioblastoma. Knowing this biomarker is a key issue for the clinician to personalize and adapt treatment to the patient at primary diagnosis for elderly patients, in particular, and also upon relapse. The association between MRI-derived information and the prediction of MGMT promoter status has been discussed in many studies, and some, more recently, have proposed the use of deep learning algorithms on multimodal scans to extract this information, but they have failed to reach a consensus. Therefore, in this work, beyond the classical performance figures usually displayed, we seek to compute confidence scores to see if a clinical application of such methods can be seriously considered. The systematic approach carried out, using different input configurations and algorithms as well as the exact methylation percentage, led to the following conclusion: current deep learning methods are unable to determine MGMT promoter methylation from MRI data.