Machine learning-based MRI radiomics predict IL18 expression and overall survival of low-grade glioma patients
Abstract Interleukin-18 has broad immune regulatory functions. Genomic data and enhanced Magnetic Resonance Imaging data related to LGG patients were downloaded from The Cancer Genome Atlas and Cancer Imaging Archive, and the constructed model was externally validated using hospital MRI enhanced ima...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-06-01
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| Series: | npj Precision Oncology |
| Online Access: | https://doi.org/10.1038/s41698-025-00966-x |
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| author | Zhe Zhang Yao Xiao Jun Liu Feng Xiao Jie Zeng Hong Zhu Wei Tu Hua Guo |
| author_facet | Zhe Zhang Yao Xiao Jun Liu Feng Xiao Jie Zeng Hong Zhu Wei Tu Hua Guo |
| author_sort | Zhe Zhang |
| collection | DOAJ |
| description | Abstract Interleukin-18 has broad immune regulatory functions. Genomic data and enhanced Magnetic Resonance Imaging data related to LGG patients were downloaded from The Cancer Genome Atlas and Cancer Imaging Archive, and the constructed model was externally validated using hospital MRI enhanced images and clinical pathological features. Radiomic feature extraction was performed using “PyRadiomics”, feature selection was conducted using Maximum Relevance Minimum Redundancy and Recursive Feature Elimination methods, and a model was built using the Gradient Boosting Machine algorithm to predict the expression status of IL18. The constructed radiomics model achieved areas under the receiver operating characteristic curve of 0.861, 0.788, and 0.762 in the TCIA training dataset (n = 98), TCIA validation dataset (n = 41), and external validation dataset (n = 50). Calibration curves and decision curve analysis demonstrated the calibration and high clinical utility of the model. The radiomics model based on enhanced MRI can effectively predict the expression status of IL18 and the prognosis of LGG. |
| format | Article |
| id | doaj-art-a41fca7cce04429c8b496685df48a546 |
| institution | DOAJ |
| issn | 2397-768X |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Precision Oncology |
| spelling | doaj-art-a41fca7cce04429c8b496685df48a5462025-08-20T03:22:46ZengNature Portfolionpj Precision Oncology2397-768X2025-06-019111310.1038/s41698-025-00966-xMachine learning-based MRI radiomics predict IL18 expression and overall survival of low-grade glioma patientsZhe Zhang0Yao Xiao1Jun Liu2Feng Xiao3Jie Zeng4Hong Zhu5Wei Tu6Hua Guo7Department of Neurosurgery, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang UniversityDepartment of Neurosurgery, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang UniversityDepartment of Neurosurgery, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang UniversityDepartment of Neurosurgery, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang UniversityDepartment of Neurosurgery, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang UniversityDepartment of Neurosurgery, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang UniversityDepartment of Neurosurgery, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang UniversityDepartment of Neurosurgery, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang UniversityAbstract Interleukin-18 has broad immune regulatory functions. Genomic data and enhanced Magnetic Resonance Imaging data related to LGG patients were downloaded from The Cancer Genome Atlas and Cancer Imaging Archive, and the constructed model was externally validated using hospital MRI enhanced images and clinical pathological features. Radiomic feature extraction was performed using “PyRadiomics”, feature selection was conducted using Maximum Relevance Minimum Redundancy and Recursive Feature Elimination methods, and a model was built using the Gradient Boosting Machine algorithm to predict the expression status of IL18. The constructed radiomics model achieved areas under the receiver operating characteristic curve of 0.861, 0.788, and 0.762 in the TCIA training dataset (n = 98), TCIA validation dataset (n = 41), and external validation dataset (n = 50). Calibration curves and decision curve analysis demonstrated the calibration and high clinical utility of the model. The radiomics model based on enhanced MRI can effectively predict the expression status of IL18 and the prognosis of LGG.https://doi.org/10.1038/s41698-025-00966-x |
| spellingShingle | Zhe Zhang Yao Xiao Jun Liu Feng Xiao Jie Zeng Hong Zhu Wei Tu Hua Guo Machine learning-based MRI radiomics predict IL18 expression and overall survival of low-grade glioma patients npj Precision Oncology |
| title | Machine learning-based MRI radiomics predict IL18 expression and overall survival of low-grade glioma patients |
| title_full | Machine learning-based MRI radiomics predict IL18 expression and overall survival of low-grade glioma patients |
| title_fullStr | Machine learning-based MRI radiomics predict IL18 expression and overall survival of low-grade glioma patients |
| title_full_unstemmed | Machine learning-based MRI radiomics predict IL18 expression and overall survival of low-grade glioma patients |
| title_short | Machine learning-based MRI radiomics predict IL18 expression and overall survival of low-grade glioma patients |
| title_sort | machine learning based mri radiomics predict il18 expression and overall survival of low grade glioma patients |
| url | https://doi.org/10.1038/s41698-025-00966-x |
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