Assessment of MGMT promoter methylation status in glioblastoma using deep learning features from multi-sequence MRI of intratumoral and peritumoral regions
Abstract Objective This study aims to evaluate the effectiveness of deep learning features derived from multi-sequence magnetic resonance imaging (MRI) in determining the O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation status among glioblastoma patients. Methods Clinical, patholog...
Saved in:
| Main Authors: | , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2024-12-01
|
| Series: | Cancer Imaging |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s40644-024-00817-1 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850103284109410304 |
|---|---|
| author | Xuan Yu Jing Zhou Yaping Wu Yan Bai Nan Meng Qingxia Wu Shuting Jin Huanhuan Liu Panlong Li Meiyun Wang |
| author_facet | Xuan Yu Jing Zhou Yaping Wu Yan Bai Nan Meng Qingxia Wu Shuting Jin Huanhuan Liu Panlong Li Meiyun Wang |
| author_sort | Xuan Yu |
| collection | DOAJ |
| description | Abstract Objective This study aims to evaluate the effectiveness of deep learning features derived from multi-sequence magnetic resonance imaging (MRI) in determining the O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation status among glioblastoma patients. Methods Clinical, pathological, and MRI data of 356 glioblastoma patients (251 methylated, 105 unmethylated) were retrospectively examined from the public dataset The Cancer Imaging Archive. Each patient underwent preoperative multi-sequence brain MRI scans, which included T1-weighted imaging (T1WI) and contrast-enhanced T1-weighted imaging (CE-T1WI). Regions of interest (ROIs) were delineated to identify the necrotic tumor core (NCR), enhancing tumor (ET), and peritumoral edema (PED). The ET and NCR regions were categorized as intratumoral ROIs, whereas the PED region was categorized as peritumoral ROIs. Predictive models were developed using the Transformer algorithm based on intratumoral, peritumoral, and combined MRI features. The area under the receiver operating characteristic curve (AUC) was employed to assess predictive performance. Results The ROI-based models of intratumoral and peritumoral regions, utilizing deep learning algorithms on multi-sequence MRI, were capable of predicting MGMT promoter methylation status in glioblastoma patients. The combined model of intratumoral and peritumoral regions exhibited superior diagnostic performance relative to individual models, achieving an AUC of 0.923 (95% confidence interval [CI]: 0.890 – 0.948) in stratified cross-validation, with sensitivity and specificity of 86.45% and 87.62%, respectively. Conclusion The deep learning model based on MRI data can effectively distinguish between glioblastoma patients with and without MGMT promoter methylation. |
| format | Article |
| id | doaj-art-86144b28b04c424b95c7fcbcb1e3d1c0 |
| institution | DOAJ |
| issn | 1470-7330 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | BMC |
| record_format | Article |
| series | Cancer Imaging |
| spelling | doaj-art-86144b28b04c424b95c7fcbcb1e3d1c02025-08-20T02:39:35ZengBMCCancer Imaging1470-73302024-12-0124111210.1186/s40644-024-00817-1Assessment of MGMT promoter methylation status in glioblastoma using deep learning features from multi-sequence MRI of intratumoral and peritumoral regionsXuan Yu0Jing Zhou1Yaping Wu2Yan Bai3Nan Meng4Qingxia Wu5Shuting Jin6Huanhuan Liu7Panlong Li8Meiyun Wang9Department of Radiology, Henan Provincial People’s Hospital & the People’s Hospital of Zhengzhou UniversityDepartment of Radiology, Henan Provincial People’s Hospital & the People’s Hospital of Zhengzhou UniversityDepartment of Radiology, Henan Provincial People’s Hospital & the People’s Hospital of Zhengzhou UniversityDepartment of Radiology, Henan Provincial People’s Hospital & the People’s Hospital of Zhengzhou UniversityDepartment of Radiology, Henan Provincial People’s Hospital & the People’s Hospital of Zhengzhou UniversityDepartment of Radiology, Henan Provincial People’s Hospital & the People’s Hospital of Zhengzhou UniversitySchool of Computer Science and Technology, Wuhan University of Science and TechnologyDepartment of Radiology, Henan Provincial People’s Hospital & the People’s Hospital of Zhengzhou UniversityDepartment of Radiology, Henan Provincial People’s Hospital & the People’s Hospital of Zhengzhou UniversityDepartment of Radiology, Henan Provincial People’s Hospital & the People’s Hospital of Zhengzhou UniversityAbstract Objective This study aims to evaluate the effectiveness of deep learning features derived from multi-sequence magnetic resonance imaging (MRI) in determining the O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation status among glioblastoma patients. Methods Clinical, pathological, and MRI data of 356 glioblastoma patients (251 methylated, 105 unmethylated) were retrospectively examined from the public dataset The Cancer Imaging Archive. Each patient underwent preoperative multi-sequence brain MRI scans, which included T1-weighted imaging (T1WI) and contrast-enhanced T1-weighted imaging (CE-T1WI). Regions of interest (ROIs) were delineated to identify the necrotic tumor core (NCR), enhancing tumor (ET), and peritumoral edema (PED). The ET and NCR regions were categorized as intratumoral ROIs, whereas the PED region was categorized as peritumoral ROIs. Predictive models were developed using the Transformer algorithm based on intratumoral, peritumoral, and combined MRI features. The area under the receiver operating characteristic curve (AUC) was employed to assess predictive performance. Results The ROI-based models of intratumoral and peritumoral regions, utilizing deep learning algorithms on multi-sequence MRI, were capable of predicting MGMT promoter methylation status in glioblastoma patients. The combined model of intratumoral and peritumoral regions exhibited superior diagnostic performance relative to individual models, achieving an AUC of 0.923 (95% confidence interval [CI]: 0.890 – 0.948) in stratified cross-validation, with sensitivity and specificity of 86.45% and 87.62%, respectively. Conclusion The deep learning model based on MRI data can effectively distinguish between glioblastoma patients with and without MGMT promoter methylation.https://doi.org/10.1186/s40644-024-00817-1GlioblastomaO6-methylguanine-DNA methyltransferaseMagnetic resonance imagingDeep learning |
| spellingShingle | Xuan Yu Jing Zhou Yaping Wu Yan Bai Nan Meng Qingxia Wu Shuting Jin Huanhuan Liu Panlong Li Meiyun Wang Assessment of MGMT promoter methylation status in glioblastoma using deep learning features from multi-sequence MRI of intratumoral and peritumoral regions Cancer Imaging Glioblastoma O6-methylguanine-DNA methyltransferase Magnetic resonance imaging Deep learning |
| title | Assessment of MGMT promoter methylation status in glioblastoma using deep learning features from multi-sequence MRI of intratumoral and peritumoral regions |
| title_full | Assessment of MGMT promoter methylation status in glioblastoma using deep learning features from multi-sequence MRI of intratumoral and peritumoral regions |
| title_fullStr | Assessment of MGMT promoter methylation status in glioblastoma using deep learning features from multi-sequence MRI of intratumoral and peritumoral regions |
| title_full_unstemmed | Assessment of MGMT promoter methylation status in glioblastoma using deep learning features from multi-sequence MRI of intratumoral and peritumoral regions |
| title_short | Assessment of MGMT promoter methylation status in glioblastoma using deep learning features from multi-sequence MRI of intratumoral and peritumoral regions |
| title_sort | assessment of mgmt promoter methylation status in glioblastoma using deep learning features from multi sequence mri of intratumoral and peritumoral regions |
| topic | Glioblastoma O6-methylguanine-DNA methyltransferase Magnetic resonance imaging Deep learning |
| url | https://doi.org/10.1186/s40644-024-00817-1 |
| work_keys_str_mv | AT xuanyu assessmentofmgmtpromotermethylationstatusinglioblastomausingdeeplearningfeaturesfrommultisequencemriofintratumoralandperitumoralregions AT jingzhou assessmentofmgmtpromotermethylationstatusinglioblastomausingdeeplearningfeaturesfrommultisequencemriofintratumoralandperitumoralregions AT yapingwu assessmentofmgmtpromotermethylationstatusinglioblastomausingdeeplearningfeaturesfrommultisequencemriofintratumoralandperitumoralregions AT yanbai assessmentofmgmtpromotermethylationstatusinglioblastomausingdeeplearningfeaturesfrommultisequencemriofintratumoralandperitumoralregions AT nanmeng assessmentofmgmtpromotermethylationstatusinglioblastomausingdeeplearningfeaturesfrommultisequencemriofintratumoralandperitumoralregions AT qingxiawu assessmentofmgmtpromotermethylationstatusinglioblastomausingdeeplearningfeaturesfrommultisequencemriofintratumoralandperitumoralregions AT shutingjin assessmentofmgmtpromotermethylationstatusinglioblastomausingdeeplearningfeaturesfrommultisequencemriofintratumoralandperitumoralregions AT huanhuanliu assessmentofmgmtpromotermethylationstatusinglioblastomausingdeeplearningfeaturesfrommultisequencemriofintratumoralandperitumoralregions AT panlongli assessmentofmgmtpromotermethylationstatusinglioblastomausingdeeplearningfeaturesfrommultisequencemriofintratumoralandperitumoralregions AT meiyunwang assessmentofmgmtpromotermethylationstatusinglioblastomausingdeeplearningfeaturesfrommultisequencemriofintratumoralandperitumoralregions |