Using partially shared radiomics features to simultaneously identify isocitrate dehydrogenase mutation status and epilepsy in glioma patients from MRI images
Abstract Prediction of isocitrate dehydrogenase (IDH) mutation status and epilepsy occurrence are important to glioma patients. Although machine learning models have been constructed for both issues, the correlation between them has not been explored. Our study aimed to exploit this correlation to i...
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Nature Portfolio
2025-01-01
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| author | Yida Wang Ankang Gao Hongxi Yang Jie Bai Guohua Zhao Huiting Zhang Yang Song Chenglong Wang Yong Zhang Jingliang Cheng Guang Yang |
| author_facet | Yida Wang Ankang Gao Hongxi Yang Jie Bai Guohua Zhao Huiting Zhang Yang Song Chenglong Wang Yong Zhang Jingliang Cheng Guang Yang |
| author_sort | Yida Wang |
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| description | Abstract Prediction of isocitrate dehydrogenase (IDH) mutation status and epilepsy occurrence are important to glioma patients. Although machine learning models have been constructed for both issues, the correlation between them has not been explored. Our study aimed to exploit this correlation to improve the performance of both of the IDH mutation status identification and epilepsy diagnosis models in patients with glioma II-IV. 399 patients were retrospectively enrolled and divided into a training (n = 279) and an independent test (n = 120) cohort. Multi-center dataset (n = 228) from The Cancer Imaging Archive (TCIA) was used for external test for identification of IDH mutation status. Region of interests comprising the entire tumor and peritumoral edema were automatically segmented using a pre-trained deep learning model. Radiomic features were extracted from T1-weighted, T2-weighted, post-Gadolinium T1 weighted, and T2 fluid-attenuated inversion recovery images. We proposed an iterative approach derived from LASSO to select features shared by two tasks and features specific to each task, before using them to construct the final models. Receiver operating characteristic (ROC) analysis was employed to evaluate the model. The IDH mutation identification model achieved area under the ROC curve (AUC) values of 0.948, 0.946 and 0.860 on the training, internal test, and external test cohorts, respectively. The epilepsy diagnosis model achieved AUCs of 0.924 and 0.880 on the training and internal test cohorts, respectively. The proposed models can identify IDH status and epilepsy with fewer features, thus having better interpretability and lower risk of overfitting. This not only improves its chance of application in clinical settings, but also provides a new scheme to study multiple correlated clinical tasks. |
| format | Article |
| id | doaj-art-11c684554eb54478855aabf3c76bba54 |
| institution | Kabale University |
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| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
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| series | Scientific Reports |
| spelling | doaj-art-11c684554eb54478855aabf3c76bba542025-02-02T12:21:25ZengNature PortfolioScientific Reports2045-23222025-01-0115111110.1038/s41598-025-87778-yUsing partially shared radiomics features to simultaneously identify isocitrate dehydrogenase mutation status and epilepsy in glioma patients from MRI imagesYida Wang0Ankang Gao1Hongxi Yang2Jie Bai3Guohua Zhao4Huiting Zhang5Yang Song6Chenglong Wang7Yong Zhang8Jingliang Cheng9Guang Yang10Shanghai Key Laboratory of Magnetic Resonance, School of Physics and Electronic Science, East China Normal UniversityDepartment of MRI, the First Affiliated Hospital of Zhengzhou UniversityShanghai Key Laboratory of Magnetic Resonance, School of Physics and Electronic Science, East China Normal UniversityDepartment of MRI, the First Affiliated Hospital of Zhengzhou UniversityDepartment of MRI, the First Affiliated Hospital of Zhengzhou UniversityMR Scientific Marketing, Siemens Healthineers ChinaMR Scientific Marketing, Siemens Healthineers ChinaShanghai Key Laboratory of Magnetic Resonance, School of Physics and Electronic Science, East China Normal UniversityDepartment of MRI, the First Affiliated Hospital of Zhengzhou UniversityDepartment of MRI, the First Affiliated Hospital of Zhengzhou UniversityShanghai Key Laboratory of Magnetic Resonance, School of Physics and Electronic Science, East China Normal UniversityAbstract Prediction of isocitrate dehydrogenase (IDH) mutation status and epilepsy occurrence are important to glioma patients. Although machine learning models have been constructed for both issues, the correlation between them has not been explored. Our study aimed to exploit this correlation to improve the performance of both of the IDH mutation status identification and epilepsy diagnosis models in patients with glioma II-IV. 399 patients were retrospectively enrolled and divided into a training (n = 279) and an independent test (n = 120) cohort. Multi-center dataset (n = 228) from The Cancer Imaging Archive (TCIA) was used for external test for identification of IDH mutation status. Region of interests comprising the entire tumor and peritumoral edema were automatically segmented using a pre-trained deep learning model. Radiomic features were extracted from T1-weighted, T2-weighted, post-Gadolinium T1 weighted, and T2 fluid-attenuated inversion recovery images. We proposed an iterative approach derived from LASSO to select features shared by two tasks and features specific to each task, before using them to construct the final models. Receiver operating characteristic (ROC) analysis was employed to evaluate the model. The IDH mutation identification model achieved area under the ROC curve (AUC) values of 0.948, 0.946 and 0.860 on the training, internal test, and external test cohorts, respectively. The epilepsy diagnosis model achieved AUCs of 0.924 and 0.880 on the training and internal test cohorts, respectively. The proposed models can identify IDH status and epilepsy with fewer features, thus having better interpretability and lower risk of overfitting. This not only improves its chance of application in clinical settings, but also provides a new scheme to study multiple correlated clinical tasks.https://doi.org/10.1038/s41598-025-87778-yEpilepsyIDH mutation statusGliomaMulti-task learningRadiomics |
| spellingShingle | Yida Wang Ankang Gao Hongxi Yang Jie Bai Guohua Zhao Huiting Zhang Yang Song Chenglong Wang Yong Zhang Jingliang Cheng Guang Yang Using partially shared radiomics features to simultaneously identify isocitrate dehydrogenase mutation status and epilepsy in glioma patients from MRI images Scientific Reports Epilepsy IDH mutation status Glioma Multi-task learning Radiomics |
| title | Using partially shared radiomics features to simultaneously identify isocitrate dehydrogenase mutation status and epilepsy in glioma patients from MRI images |
| title_full | Using partially shared radiomics features to simultaneously identify isocitrate dehydrogenase mutation status and epilepsy in glioma patients from MRI images |
| title_fullStr | Using partially shared radiomics features to simultaneously identify isocitrate dehydrogenase mutation status and epilepsy in glioma patients from MRI images |
| title_full_unstemmed | Using partially shared radiomics features to simultaneously identify isocitrate dehydrogenase mutation status and epilepsy in glioma patients from MRI images |
| title_short | Using partially shared radiomics features to simultaneously identify isocitrate dehydrogenase mutation status and epilepsy in glioma patients from MRI images |
| title_sort | using partially shared radiomics features to simultaneously identify isocitrate dehydrogenase mutation status and epilepsy in glioma patients from mri images |
| topic | Epilepsy IDH mutation status Glioma Multi-task learning Radiomics |
| url | https://doi.org/10.1038/s41598-025-87778-y |
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