Abstract:Background: Radiogenomics allows identification of radiological biomarkers for genomic phenotypes. In glioblastoma, these biomarkers could potentially complement patient stratification strategies. We aim to develop and analytically validate radiological biomarkers that capture immune cell signatures within IDH-wildtype glioblastoma microenvironment using radiogenomic analysis.
Methods: This was a retrospective multicenter study using curated open-access anonymized imaging and genomic data from TCGA-GBM, CPTAC, IvyGAP, REMBRANDT and CGGA datasets. Imaging data consisted of MRI-based radiomic features extracted from necrotic core, enhancing and edema regions of deep learning-based auto-segmented tumors. Radiomic feature selections were performed using nested cross-validated LASSO. Support vector machine and ensemble models were trained using seventeen immune and cell-specific score labels extracted from deconvoluted transcriptomic data using pan-cancer and glioblastoma immune signature matrices as reference standards. Seventeen classifier models trained in three cross-cohort strategies were validated on three held-out datasets assessing stability and generalizability.
Results: One-hundred-and-seventy-six patients were included in the study. The immune-related radiomic signatures obtained after feature selection were shape, first order and higher order radiomic features. Models predicting macrophage subtype immune signature showed stable mean performance on balanced accuracy (0.67) and precision (0.89) metrics for three independent holdout datasets with ensemble model outperforming support vector machine model.
Conclusion: Radiogenomic models non-invasively predicted the macrophage subtype M0 immune signature in IDH-wildtype glioblastoma. These biomarkers have the potential to stratify patients for immunotherapy within prospective glioblastoma clinical trials.
| Comments: | Abstract : 226; Importance of study: 109; Manuscript: 5690 (excluding references) Figures: 4, Tables: 2 Supplemental File: 1 |
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2605.10278 [cs.LG] |
| (or arXiv:2605.10278v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.10278 arXiv-issued DOI via DataCite (pending registration) |
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| Journal reference: | Neuro-Oncology Advances 2026. Published online May 2, 2026 |
| Related DOI: | https://doi.org/10.1093/noajnl/vdag115
DOI(s) linking to related resources |
From: Prajwal Ghimire [view email]
[v1]
Mon, 11 May 2026 09:38:40 UTC (1,855 KB)
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