Radiomics prediction of MGMT promoter methylation in adult diffuse gliomas: a combination of structural MRI, DCE, and DTI
PurposeTo assess the predictive value of radiomics features extracted from structural MRI, dynamic contrast enhanced (DCE), and diffusion tensor imaging (DTI) in detecting O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation in patients with diffuse gliomas.MethodsRetrospective MRI dat...
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Neurology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fneur.2025.1493666/full |
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| author | Yuying Liu Yuying Liu Zhengyang Zhu Jianan Zhou Han Wang Huiquan Yang Jinfeng Yin Yitong Wang Xin Li Futao Chen Qian Li Zhuoru Jiang Xi Wu Danni Ge Yi Zhang Xin Zhang Bing Zhang Bing Zhang Bing Zhang Bing Zhang Bing Zhang |
| author_facet | Yuying Liu Yuying Liu Zhengyang Zhu Jianan Zhou Han Wang Huiquan Yang Jinfeng Yin Yitong Wang Xin Li Futao Chen Qian Li Zhuoru Jiang Xi Wu Danni Ge Yi Zhang Xin Zhang Bing Zhang Bing Zhang Bing Zhang Bing Zhang Bing Zhang |
| author_sort | Yuying Liu |
| collection | DOAJ |
| description | PurposeTo assess the predictive value of radiomics features extracted from structural MRI, dynamic contrast enhanced (DCE), and diffusion tensor imaging (DTI) in detecting O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation in patients with diffuse gliomas.MethodsRetrospective MRI data of 110 patients were enrolled in this study. The training dataset included 88 patients (mean age 52.84 ± 14.71, 47 females). The test dataset included 22 patients (mean age 50.64 ± 12.58, 12 females). A total of 2,782 radiomic features were extracted from structural MRI, DCE, and DTI within two region of interests (ROIs). Feature section was conducted using Pearson correlation and least absolute shrinkage and selection operator. Principal component analysis was utilized for dimensionality reduction. Support vector machine was employed for model construction. Two radiologists with 1 year and 5 years of experience evaluated the MGMT status in the test dataset as a comparison with the models. The chi-square test and independent samples t-test were used for assessing the statistical differences in patients’ clinical characteristics.ResultsOn the training dataset, the model structural MRI + DCE achieved the highest AUC of 0.906. On the test dataset, the model structural MRI + DCE + DTI achieved the highest AUC of 0.868, outperforming two radiologists.ConclusionThe radiomics models have obtained promising performance in predicting MGMT promoter methylation status. Adding DCE and DTI features can provide extra information to structural MRI in detecting MGMT promoter methylation. |
| format | Article |
| id | doaj-art-27d840c3ec0349c09eb2992c593aa9c9 |
| institution | Kabale University |
| issn | 1664-2295 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neurology |
| spelling | doaj-art-27d840c3ec0349c09eb2992c593aa9c92025-01-29T06:45:34ZengFrontiers Media S.A.Frontiers in Neurology1664-22952025-01-011610.3389/fneur.2025.14936661493666Radiomics prediction of MGMT promoter methylation in adult diffuse gliomas: a combination of structural MRI, DCE, and DTIYuying Liu0Yuying Liu1Zhengyang Zhu2Jianan Zhou3Han Wang4Huiquan Yang5Jinfeng Yin6Yitong Wang7Xin Li8Futao Chen9Qian Li10Zhuoru Jiang11Xi Wu12Danni Ge13Yi Zhang14Xin Zhang15Bing Zhang16Bing Zhang17Bing Zhang18Bing Zhang19Bing Zhang20Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, ChinaNational Key Laboratory for Novel Software Technology, Department of Computer Science and Technology, Nanjing University, Nanjing, ChinaDepartment of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, ChinaDepartment of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, ChinaNanjing Center for Applied Mathematics, Nanjing, ChinaDepartment of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, ChinaDepartment of Medical Imaging, Shandong Second Medical University, Jinan, ChinaDepartment of Medical Imaging, Shandong First Medical University, Jinan, ChinaDepartment of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, ChinaDepartment of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, ChinaDepartment of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, ChinaDepartment of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, ChinaDepartment of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, ChinaDepartment of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, ChinaDepartment of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, ChinaDepartment of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, ChinaDepartment of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, ChinaMedical Imaging Center, Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, ChinaInstitute of Medical Imaging and Artificial Intelligence, Nanjing University, Nanjing, ChinaJiangsu Key Laboratory of Molecular Medicine, Nanjing, ChinaInstitute of Brain Science, Nanjing University, Nanjing, ChinaPurposeTo assess the predictive value of radiomics features extracted from structural MRI, dynamic contrast enhanced (DCE), and diffusion tensor imaging (DTI) in detecting O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation in patients with diffuse gliomas.MethodsRetrospective MRI data of 110 patients were enrolled in this study. The training dataset included 88 patients (mean age 52.84 ± 14.71, 47 females). The test dataset included 22 patients (mean age 50.64 ± 12.58, 12 females). A total of 2,782 radiomic features were extracted from structural MRI, DCE, and DTI within two region of interests (ROIs). Feature section was conducted using Pearson correlation and least absolute shrinkage and selection operator. Principal component analysis was utilized for dimensionality reduction. Support vector machine was employed for model construction. Two radiologists with 1 year and 5 years of experience evaluated the MGMT status in the test dataset as a comparison with the models. The chi-square test and independent samples t-test were used for assessing the statistical differences in patients’ clinical characteristics.ResultsOn the training dataset, the model structural MRI + DCE achieved the highest AUC of 0.906. On the test dataset, the model structural MRI + DCE + DTI achieved the highest AUC of 0.868, outperforming two radiologists.ConclusionThe radiomics models have obtained promising performance in predicting MGMT promoter methylation status. Adding DCE and DTI features can provide extra information to structural MRI in detecting MGMT promoter methylation.https://www.frontiersin.org/articles/10.3389/fneur.2025.1493666/fullMGMTgliomaradiomicsDCEDTI |
| spellingShingle | Yuying Liu Yuying Liu Zhengyang Zhu Jianan Zhou Han Wang Huiquan Yang Jinfeng Yin Yitong Wang Xin Li Futao Chen Qian Li Zhuoru Jiang Xi Wu Danni Ge Yi Zhang Xin Zhang Bing Zhang Bing Zhang Bing Zhang Bing Zhang Bing Zhang Radiomics prediction of MGMT promoter methylation in adult diffuse gliomas: a combination of structural MRI, DCE, and DTI Frontiers in Neurology MGMT glioma radiomics DCE DTI |
| title | Radiomics prediction of MGMT promoter methylation in adult diffuse gliomas: a combination of structural MRI, DCE, and DTI |
| title_full | Radiomics prediction of MGMT promoter methylation in adult diffuse gliomas: a combination of structural MRI, DCE, and DTI |
| title_fullStr | Radiomics prediction of MGMT promoter methylation in adult diffuse gliomas: a combination of structural MRI, DCE, and DTI |
| title_full_unstemmed | Radiomics prediction of MGMT promoter methylation in adult diffuse gliomas: a combination of structural MRI, DCE, and DTI |
| title_short | Radiomics prediction of MGMT promoter methylation in adult diffuse gliomas: a combination of structural MRI, DCE, and DTI |
| title_sort | radiomics prediction of mgmt promoter methylation in adult diffuse gliomas a combination of structural mri dce and dti |
| topic | MGMT glioma radiomics DCE DTI |
| url | https://www.frontiersin.org/articles/10.3389/fneur.2025.1493666/full |
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