Prediction of MGMT methylation status in glioblastoma patients based on radiomics feature extracted from intratumoral and peritumoral MRI imaging.
Authors
Affiliations (6)
Affiliations (6)
- Department of Radiology, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), Haikou, 570311, China.
- Sheffield Institute for Translational Neuroscience, School of Medicine and Population Health, University of Sheffield, 385a Glossop Road, Sheffield, S10 2HQ, South Yorkshire, UK.
- Department of Radiology, Shenzhen Longhua District Central Hospital, Shenzhen, 518110, China.
- Department of Gynecology, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), Haikou, 570311, China. [email protected].
- Sheffield Institute for Translational Neuroscience, School of Medicine and Population Health, University of Sheffield, 385a Glossop Road, Sheffield, S10 2HQ, South Yorkshire, UK. [email protected].
- Department of Psychiatry, School of Clinical Medicine, University of Cambridge, Cambridge, CB2 OSP, UK. [email protected].
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.