ArticleAnnals of clinical and translational neurology2026
Development of a Prediction Model for Progression Risk in High-Grade Gliomas Based on Habitat Radiomics and Pathomics.
Article in Annals of clinical and translational neurology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Post-Treatment MRI Features on First Follow-Up Imaging in Diffuse Gliomas After Near-Total Resection: A Real-World Exploratory Cohort Study.Medicina (Kaunas, Lithuania) · 2026Article
- Development of a Prediction Model for Progression Risk in High-Grade Gliomas Based on Habitat Radiomics and Pathomics.Annals of clinical and translational neurology · 2026Article
- Radiomics model integrating MRI and ECV enhances prediction accuracy for progression in high-grade glioma.NPJ precision oncology · 2026Article
- AI-driven radiomics and radiogenomics: supporting the assessment and differentiation of pseudoprogression in cellular immunotherapy for glioblastoma.Frontiers in immunology · 2026Review
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Authors and funding
15 authors.
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Abstract
objectiveTo investigate the value of constructing models based on habitat radiomics and pathomics for predicting the risk of progression in high-grade gliomas.
methodsThis study conducted a retrospective analysis of preoperative magnetic resonance (MR) images and pathological sections from 72 patients diagnosed with high-grade gliomas (52 cases as a train cohort and 20 cases as a test cohort). The regions of interest (ROIs) were annotated accordingly. In MRI processing, the ROI was further divided into clusters to extract habitat radiomics features. For whole slide imaging (WSI), the ROI was cropped into equal-sized image patches for weakly supervised learning and deep learning using various network architectures. The optimal model architecture was selected, and pathological features were extracted. After feature selection, four independent models were constructed: habitat radiomics model, pathomics-based model, clinical model, and combined model integrating all information. Model performance was evaluated using the concordance index (C-index) and the area under the receiver operating characteristic curve (AUC).
resultsThe combined model demonstrated the best predictive performance, with a C-index of 0.883 and an AUC of 0.965 in the train cohort. In the test cohort, the C-index was 0.840, and the AUC was 0.927. Based on the combined model, patients with high-grade gliomas were divided into high-risk and low-risk groups, with median progression-free survival (mPFS) of 9 months and 77 months, respectively (p < 0.001).
conclusionCompared with the habitat radiomics model or the pathomics-based model alone, the combined model can better predict the risk of progression in high-grade gliomas and provides valuable guidance for personalized treatment of high-grade gliomas.
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