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...
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Nature Portfolio
2025-07-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-08608-9 |
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| author | Wang-Sheng Chen Fang-Xiong Fu Qin-Lei Cai Fei Wang Xue-Hua Wang Lan Hong Li Su |
| author_facet | Wang-Sheng Chen Fang-Xiong Fu Qin-Lei Cai Fei Wang Xue-Hua Wang Lan Hong Li Su |
| author_sort | Wang-Sheng Chen |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-2b2caebff7134a0086e47ce43a41d0d2 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
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| series | Scientific Reports |
| spelling | doaj-art-2b2caebff7134a0086e47ce43a41d0d22025-08-20T03:46:01ZengNature PortfolioScientific Reports2045-23222025-07-0115111110.1038/s41598-025-08608-9Prediction of MGMT methylation status in glioblastoma patients based on radiomics feature extracted from intratumoral and peritumoral MRI imagingWang-Sheng Chen0Fang-Xiong Fu1Qin-Lei Cai2Fei Wang3Xue-Hua Wang4Lan Hong5Li Su6Department of Radiology, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University)Department of Radiology, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University)Department of Radiology, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University)Department of Radiology, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University)Department of Radiology, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University)Department of Gynecology, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University)Sheffield Institute for Translational Neuroscience, School of Medicine and Population Health, University of SheffieldAbstract 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.https://doi.org/10.1038/s41598-025-08608-9GlioblastomaMGMT methylationRadiomicsMRI imagingMachine learningPersonalized treatment |
| spellingShingle | Wang-Sheng Chen Fang-Xiong Fu Qin-Lei Cai Fei Wang Xue-Hua Wang Lan Hong Li Su Prediction of MGMT methylation status in glioblastoma patients based on radiomics feature extracted from intratumoral and peritumoral MRI imaging Scientific Reports Glioblastoma MGMT methylation Radiomics MRI imaging Machine learning Personalized treatment |
| title | Prediction of MGMT methylation status in glioblastoma patients based on radiomics feature extracted from intratumoral and peritumoral MRI imaging |
| title_full | Prediction of MGMT methylation status in glioblastoma patients based on radiomics feature extracted from intratumoral and peritumoral MRI imaging |
| title_fullStr | Prediction of MGMT methylation status in glioblastoma patients based on radiomics feature extracted from intratumoral and peritumoral MRI imaging |
| title_full_unstemmed | Prediction of MGMT methylation status in glioblastoma patients based on radiomics feature extracted from intratumoral and peritumoral MRI imaging |
| title_short | Prediction of MGMT methylation status in glioblastoma patients based on radiomics feature extracted from intratumoral and peritumoral MRI imaging |
| title_sort | prediction of mgmt methylation status in glioblastoma patients based on radiomics feature extracted from intratumoral and peritumoral mri imaging |
| topic | Glioblastoma MGMT methylation Radiomics MRI imaging Machine learning Personalized treatment |
| url | https://doi.org/10.1038/s41598-025-08608-9 |
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