MRI-Based Deep Learning Tools for MGMT Promoter Methylation Detection: A Thorough Evaluation.
Simple Summary: A major prognosis factor for glioblastoma patients is the methylation status of the DNA repair enzyme MGMT. Obtaining this information using deep learning models trained on non-invasive MRI data is a major challenge with no scientific consensus to date. In this study, we provide a mo...
| Publicado en: | Cancers Vol. 15; no. 8; pp. 2253 - 2269 |
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| Autores principales: | , , , , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
MDPI
Apr2023
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=163389512&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 163389512 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 20726694 B74B jtl: Cancers issn: 20726694 maglogo: N pubinfo: dt: Apr2023 vid: 15 iid: 8 pid: 97109 pub: MDPI artinfo: ui: 163389512 163389512 163389512 10.3390/cancers15082253 163389512 ppf: 2253 ppct: 16 formats: tig: atl: MRI-Based Deep Learning Tools for MGMT Promoter Methylation Detection: A Thorough Evaluation. aug: au: Robinet, Lucas Siegfried, Aurore Roques, Margaux Berjaoui, Ahmad Cohen-Jonathan Moyal, Elizabeth affil: IRT Saint-Exupéry, 31400 Toulouse, France sug: subj: Magnetic Resonance Imaging Deep Learning Tumor Markers, Biological Promoters Methylation Evaluation Glioma Prognosis Human Prediction Models Algorithms ROC Curve ab: Simple Summary: A major prognosis factor for glioblastoma patients is the methylation status of the DNA repair enzyme MGMT. Obtaining this information using deep learning models trained on non-invasive MRI data is a major challenge with no scientific consensus to date. In this study, we provide a more rigorous and comprehensive answer to this question by using confidence metrics and relating them to the exact percentage of methylation obtained at biopsy. This systematic approach confirms that the deep learning algorithms developed until now are not suitable for clinical application. We also provide, to the best of our knowledge, the first fully reproducible source code and experiments on this issue. 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. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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