Radiomics and AI-Based Prediction of MGMT Methylation Status in Glioblastoma Using Multiparametric MRI: A Hybrid Feature Weighting Approach
<b>Background/Objectives</b>: Glioblastoma (GBM) is a highly aggressive primary central nervous system tumor with a median survival of 14 months. MGMT (O6-methylguanine-DNA methyltransferase) promoter methylation status is a key biomarker as a prognostic indicator and a predictor of chem...
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-05-01
|
| Series: | Diagnostics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-4418/15/10/1292 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849327656084963328 |
|---|---|
| author | Erdal Tasci Ying Zhuge Longze Zhang Holly Ning Jason Y. Cheng Robert W. Miller Kevin Camphausen Andra V. Krauze |
| author_facet | Erdal Tasci Ying Zhuge Longze Zhang Holly Ning Jason Y. Cheng Robert W. Miller Kevin Camphausen Andra V. Krauze |
| author_sort | Erdal Tasci |
| collection | DOAJ |
| description | <b>Background/Objectives</b>: Glioblastoma (GBM) is a highly aggressive primary central nervous system tumor with a median survival of 14 months. MGMT (O6-methylguanine-DNA methyltransferase) promoter methylation status is a key biomarker as a prognostic indicator and a predictor of chemotherapy response in GBM. Patients with MGMT methylated disease progress later and survive longer (median survival rate 22 vs. 15 months, respectively) as compared to patients with MGMT unmethylated disease. Patients with GBM undergo an MRI of the brain prior to diagnosis and following surgical resection for radiation therapy planning and ongoing follow-up. There is currently no imaging biomarker for GBM. Studies have attempted to connect MGMT methylation status to MRI imaging appearance to determine if brain MRI can be leveraged to provide MGMT status information non-invasively and more expeditiously. <b>Methods</b>: Artificial intelligence (AI) can identify MRI features that are not distinguishable to the human eye and can be linked to MGMT status. We employed the UPenn-GBM dataset patients for whom methylation status was available (<i>n</i> = 146), employing a novel radiomic method grounded in hybrid feature selection and weighting to predict MGMT methylation status. <b>Results</b>: The best MGMT classification and feature selection result obtained resulted in a mean accuracy rate value of 81.6% utilizing 101 selected features and five-fold cross-validation. <b>Conclusions</b>: This compared favorably with similar studies in the literature. Validation with external datasets remains critical to enhance generalizability and propagate robust results while reducing bias. Future directions include multi-channel data integration with radiomic features and deep and ensemble learning methods to improve predictive performance. |
| format | Article |
| id | doaj-art-bef187dc2d8844ef87434d68b6be2180 |
| institution | Kabale University |
| issn | 2075-4418 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj-art-bef187dc2d8844ef87434d68b6be21802025-08-20T03:47:48ZengMDPI AGDiagnostics2075-44182025-05-011510129210.3390/diagnostics15101292Radiomics and AI-Based Prediction of MGMT Methylation Status in Glioblastoma Using Multiparametric MRI: A Hybrid Feature Weighting ApproachErdal Tasci0Ying Zhuge1Longze Zhang2Holly Ning3Jason Y. Cheng4Robert W. Miller5Kevin Camphausen6Andra V. Krauze7Radiation Oncology Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, 9000 Rockville Pike, Building 10, CRC, Bethesda, MD 20892, USARadiation Oncology Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, 9000 Rockville Pike, Building 10, CRC, Bethesda, MD 20892, USARadiation Oncology Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, 9000 Rockville Pike, Building 10, CRC, Bethesda, MD 20892, USARadiation Oncology Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, 9000 Rockville Pike, Building 10, CRC, Bethesda, MD 20892, USARadiation Oncology Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, 9000 Rockville Pike, Building 10, CRC, Bethesda, MD 20892, USARadiation Oncology Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, 9000 Rockville Pike, Building 10, CRC, Bethesda, MD 20892, USARadiation Oncology Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, 9000 Rockville Pike, Building 10, CRC, Bethesda, MD 20892, USARadiation Oncology Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, 9000 Rockville Pike, Building 10, CRC, Bethesda, MD 20892, USA<b>Background/Objectives</b>: Glioblastoma (GBM) is a highly aggressive primary central nervous system tumor with a median survival of 14 months. MGMT (O6-methylguanine-DNA methyltransferase) promoter methylation status is a key biomarker as a prognostic indicator and a predictor of chemotherapy response in GBM. Patients with MGMT methylated disease progress later and survive longer (median survival rate 22 vs. 15 months, respectively) as compared to patients with MGMT unmethylated disease. Patients with GBM undergo an MRI of the brain prior to diagnosis and following surgical resection for radiation therapy planning and ongoing follow-up. There is currently no imaging biomarker for GBM. Studies have attempted to connect MGMT methylation status to MRI imaging appearance to determine if brain MRI can be leveraged to provide MGMT status information non-invasively and more expeditiously. <b>Methods</b>: Artificial intelligence (AI) can identify MRI features that are not distinguishable to the human eye and can be linked to MGMT status. We employed the UPenn-GBM dataset patients for whom methylation status was available (<i>n</i> = 146), employing a novel radiomic method grounded in hybrid feature selection and weighting to predict MGMT methylation status. <b>Results</b>: The best MGMT classification and feature selection result obtained resulted in a mean accuracy rate value of 81.6% utilizing 101 selected features and five-fold cross-validation. <b>Conclusions</b>: This compared favorably with similar studies in the literature. Validation with external datasets remains critical to enhance generalizability and propagate robust results while reducing bias. Future directions include multi-channel data integration with radiomic features and deep and ensemble learning methods to improve predictive performance.https://www.mdpi.com/2075-4418/15/10/1292MGMTradiomicsimage processingmachine learningfeature extractionfeature selection |
| spellingShingle | Erdal Tasci Ying Zhuge Longze Zhang Holly Ning Jason Y. Cheng Robert W. Miller Kevin Camphausen Andra V. Krauze Radiomics and AI-Based Prediction of MGMT Methylation Status in Glioblastoma Using Multiparametric MRI: A Hybrid Feature Weighting Approach Diagnostics MGMT radiomics image processing machine learning feature extraction feature selection |
| title | Radiomics and AI-Based Prediction of MGMT Methylation Status in Glioblastoma Using Multiparametric MRI: A Hybrid Feature Weighting Approach |
| title_full | Radiomics and AI-Based Prediction of MGMT Methylation Status in Glioblastoma Using Multiparametric MRI: A Hybrid Feature Weighting Approach |
| title_fullStr | Radiomics and AI-Based Prediction of MGMT Methylation Status in Glioblastoma Using Multiparametric MRI: A Hybrid Feature Weighting Approach |
| title_full_unstemmed | Radiomics and AI-Based Prediction of MGMT Methylation Status in Glioblastoma Using Multiparametric MRI: A Hybrid Feature Weighting Approach |
| title_short | Radiomics and AI-Based Prediction of MGMT Methylation Status in Glioblastoma Using Multiparametric MRI: A Hybrid Feature Weighting Approach |
| title_sort | radiomics and ai based prediction of mgmt methylation status in glioblastoma using multiparametric mri a hybrid feature weighting approach |
| topic | MGMT radiomics image processing machine learning feature extraction feature selection |
| url | https://www.mdpi.com/2075-4418/15/10/1292 |
| work_keys_str_mv | AT erdaltasci radiomicsandaibasedpredictionofmgmtmethylationstatusinglioblastomausingmultiparametricmriahybridfeatureweightingapproach AT yingzhuge radiomicsandaibasedpredictionofmgmtmethylationstatusinglioblastomausingmultiparametricmriahybridfeatureweightingapproach AT longzezhang radiomicsandaibasedpredictionofmgmtmethylationstatusinglioblastomausingmultiparametricmriahybridfeatureweightingapproach AT hollyning radiomicsandaibasedpredictionofmgmtmethylationstatusinglioblastomausingmultiparametricmriahybridfeatureweightingapproach AT jasonycheng radiomicsandaibasedpredictionofmgmtmethylationstatusinglioblastomausingmultiparametricmriahybridfeatureweightingapproach AT robertwmiller radiomicsandaibasedpredictionofmgmtmethylationstatusinglioblastomausingmultiparametricmriahybridfeatureweightingapproach AT kevincamphausen radiomicsandaibasedpredictionofmgmtmethylationstatusinglioblastomausingmultiparametricmriahybridfeatureweightingapproach AT andravkrauze radiomicsandaibasedpredictionofmgmtmethylationstatusinglioblastomausingmultiparametricmriahybridfeatureweightingapproach |