Machine learning-based MRI radiomics predict IL18 expression and overall survival of low-grade glioma patients.
Authors
Affiliations (16)
Affiliations (16)
- Department of Neurosurgery, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China.
- Institute of Neuroscience, Nanchang University, Jiangxi, China.
- Jiangxi Province Key Laboratory of Neurological Diseases, Jiangxi, China.
- JXHC Key Laboratory of Neurological medicine, Jiangxi, China.
- Department of Neurosurgery, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China. [email protected].
- Institute of Neuroscience, Nanchang University, Jiangxi, China. [email protected].
- Jiangxi Province Key Laboratory of Neurological Diseases, Jiangxi, China. [email protected].
- JXHC Key Laboratory of Neurological medicine, Jiangxi, China. [email protected].
- Department of Neurosurgery, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China. [email protected].
- Institute of Neuroscience, Nanchang University, Jiangxi, China. [email protected].
- Jiangxi Province Key Laboratory of Neurological Diseases, Jiangxi, China. [email protected].
- JXHC Key Laboratory of Neurological medicine, Jiangxi, China. [email protected].
- Department of Neurosurgery, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China. [email protected].
- Institute of Neuroscience, Nanchang University, Jiangxi, China. [email protected].
- Jiangxi Province Key Laboratory of Neurological Diseases, Jiangxi, China. [email protected].
- JXHC Key Laboratory of Neurological medicine, Jiangxi, China. [email protected].
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.