CrossScale-GLIO: Topology-Preserving Vision-Language Alignment of MRI and Whole-Slide Histopathology for Diffuse Glioma

Abstract:Magnetic resonance imaging and histopathology observe the same glioma at radically different scales. We present CrossScale-GLIO, a visual multimodal framework that represents MRI as a tumor-habitat graph and histology as a cell-niche graph, then aligns them with a structure-aware optimal transport objective anchored by diagnostic language. Across paired and external glioma cohorts, CrossScale-GLIO achieved a paired-test subtype macro-F1 of 0.789, IDH AUROC of 0.934, 1p/19q AUROC of 0.884, and MGMT AUROC of 0.802. The subtype gain over feature-only transport was 2.8 percentage points (95% CI: 1.2 to 4.4, adjusted p = 0.0019). Bidirectional patient retrieval reached Recall@1 values of 0.286 and 0.278, and Recall@5 values of 0.621 and 0.608. Pathologists rated 81.2% of high-mass habitat-niche pairs as biologically plausible. Deleting the highest-mass pair reduced correct-class probability by 0.184, compared with 0.049 under random deletion. Degree-preserving graph rewiring reduced subtype macro-F1 by 0.034 and retrieval Recall@1 by 0.090, directly confirming that preserved relational topology drives cross-scale correspondence.

Image and Video Processing (eess.IV); Artificial Intelligence (cs.AI); Quantitative Methods (q-bio.QM)

arXiv:2609.28524 [eess.IV]

(or arXiv:2609.28524v1 [eess.IV] for this version)

https://doi.org/10.48550/arXiv.2609.28524 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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