Prediction of MGMT methylation status in glioblastoma patients based on radiomics feature extracted from intratumoral and peritumoral MRI imaging

Abstract

Assessing MGMT promoter methylation is crucial for determining appropriate glioblastoma therapy. Previous studies have focused on intratumoral regions, overlooking the peritumoral area. This study aimed to develop a radiomic model using MRI-derived features from both regions. We included 96 glioblastoma patients randomly allocated to training and testing sets. Radiomic features were extracted from intratumoral and peritumoral regions. We constructed and compared radiomic models based on intratumoral, peritumoral, and combined features. Model performance was evaluated using the area under the receiver-operating characteristic curve (AUC). The combined radiomic model achieved an AUC of 0.814 (95% CI: 0.767–0.862) in the training set and 0.808 (95% CI: 0.736–0.859) in the testing set, outperforming models based on intratumoral or peritumoral features alone. Calibration and decision curve analyses demonstrated excellent model fit and clinical utility. The radiomic model incorporating both intratumoral and peritumoral features shows promise in differentiating MGMT methylation status, potentially informing clinical treatment strategies for glioblastoma.

Metadata

Item Type:	Article
Authors/Creators:	Chen, W.-S. Fu, F.-X. Cai, Q.-L. Wang, F. Wang, X.-H. Hong, L. Su, L. https://orcid.org/0000-0002-6347-3986
Copyright, Publisher and Additional Information:	© The Author(s) 2025 . This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
Keywords:	Glioblastoma; MGMT methylation; Radiomics; MRI imaging; Machine learning; Personalized treatment
Dates:	Submitted: 17 December 2024 Accepted: 23 June 2025 Published (online): 29 July 2025 Published: 29 July 2025
Institution:	The University of Sheffield
Academic Units:	The University of Sheffield > Faculty of Medicine, Dentistry and Health (Sheffield) > School of Medicine and Population Health
Funding Information:	Funder Grant number Alzheimer’s Research UK ARUK-SRF2017B-1 National Institute for Health and Care Research NIHR203321
Depositing User:	Symplectic Sheffield
Date Deposited:	05 Aug 2025 10:19
Last Modified:	05 Aug 2025 10:19
Status:	Published
Publisher:	Springer Science and Business Media LLC
Refereed:	Yes
Identification Number:	10.1038/s41598-025-08608-9
Sustainable Development Goals:
Open Archives Initiative ID (OAI ID):	oai:eprints.whiterose.ac.uk:229997

Download

Published Version

Filename: s41598-025-08608-9.pdf

Licence: CC-BY-NC-ND 4.0

CLICK TO DOWNLOAD

CORE (COnnecting REpositories)

Prediction of MGMT methylation status in glioblastoma patients based on radiomics feature extracted from intratumoral and peritumoral MRI imaging

Abstract

Metadata

Download

Published Version

Export

Statistics