ArticleiScience2026
Spatial pattern-driven interpretable model and biological correlates in brain glioblastoma-lymphoma differentiation.
Article in iScience, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
22 authors.
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Abstract
Glioblastoma (GBM) and primary central nervous system lymphoma (PCNSL) often exhibit overlapping appearances on routine MRI, complicating pre-treatment diagnosis. In 1,109 patients from five centers, we constructed standard-space tumor probabilistic maps and derived atlas-anchored spatial features to augment conventional radiomics. The spatial radiomics classifier outperformed radiomics alone (external test area under the ROC curve [AUC], 0.98) with acceptable calibration and decision curve benefit, and SHapley Additive exPlanations (SHAP)-enabled anatomy-grounded interpretation. Aligning tumor localization with the Allen Human Brain Atlas and a normative functional connectome linked GBM-enriched territories to developmental-oncogenic programs and network hubness, whereas PCNSL-enriched territories showed immune-inflammatory/proliferative programs, and associations with network hubness did not survive spatial-autocorrelation correction. These results provide shareable reference maps and an interpretable, multicenter-generalizing tool for GBM-PCNSL differentiation, while offering biological context for diagnosis-specific location susceptibility.
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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 Socratic graph.