ArticleNPJ precision oncology2025
Machine learning-based MRI radiomics predict IL18 expression and overall survival of low-grade glioma patients.
Article in NPJ precision oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed.
- Feature Selection and Machine Learning Strategies for CT Radiomics-Based Survival Prediction in Non-Small Cell Lung Cancer: A Comparative Study.Diagnostics (Basel, Switzerland) · 2026Article
- A stacked multi-classifier for multi-modal data fusion in transcranial sonography-based Parkinson's disease assessment.NPJ Parkinson's disease · 2026Article
- Radiogenomic landscape of the hallmarks of cancer.Biomarker research · 2026Review
- Article
- Review
- Predicting postoperative recurrence in colorectal cancer using MRI-derived radiomics features and their correlation with the tumor immune microenvironment.Gastroenterology report · 2026Article
- Multimodal artificial intelligence in glioma management: integrating neuroimaging and hematologic biomarkers for precision oncology.Frontiers in oncology · 2026Review
Corrections and comments
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Authors and funding
8 authors.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.