Predictive Radiomics for Evaluation of Cancer Immune SignaturE in Glioblastoma: the PRECISE-GBM study
2026-05-11 • Machine Learning
Machine Learning
AI summaryⓘ
The authors used MRI scans and genetic data from multiple sources to find patterns in brain tumors called glioblastomas. They focused on identifying features in images that relate to immune cells, especially a type called macrophages, inside the tumor. By training computer models, they could predict the presence of these immune cells non-invasively. Their findings suggest these image-based clues might help in selecting patients for treatments targeting the immune system in future clinical trials.
RadiogenomicsGlioblastomaIDH-wildtypeMRIRadiomic featuresMacrophageImmune signatureLASSOSupport vector machineImmunotherapy
Authors
Prajwal Ghimire, Junjie Li, Liu Yaou, Marc Modat, Thomas Booth
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.