Integrating Multimodal MRI Habitat and Transformer-Based Pathomics to Predict High-Risk Molecular Subtypes and Explore Biological Mechanisms in Adult Diffuse Gliomas

Abstract

Background

This study aims to achieve accurate prediction of high-risk molecular subtypes of gliomas through a cross-scale Combined model, matching the model's classification metrics with patient risk stratification and exploring the underlying biological mechanisms.

Methods

This study retrospectively collected preoperative MRI, postoperative whole-slide pathological images, molecular markers, and clinical data from 456 adult diffuse glioma patients. We separately constructed an MRI habitat prediction model, a WSI Transformer-based deep learning pathomics (PDL) model, and a Combined model. A dynamic nomogram web page for predicting high-risk molecular subtypes was developed based on the Combined model. Patients were stratified into risk groups according to the output scores of the Combined model, and Kaplan–Meier survival analysis and the Log-rank test were employed to evaluate survival differences between the groups. Additionally, differential expression and GO/KEGG enrichment analyses were further performed in the test set with available RNA-seq data to explore transcriptional features and biological processes associated with model-based risk stratification.

Results

The Combined model demonstrated the highest AUC (Training set: 0.888, Test set: 0.836) compared to the Habitat model (Training set: 0.832, Test set: 0.798) and the PDL model (Training set: 0.852, Test set: 0.821). The high-risk and low-risk groups, stratified based on the cutoff value derived from the Combined model output scores, exhibited significant survival differences. Exploratory transcriptomic analysis showed that differentially expressed genes between the high- and low-risk groups were mainly enriched in biological processes and pathways related to the extracellular matrix and cell–matrix interactions.

Conclusion

The cross-scale Combined model not only enabled identification of high-risk molecular subtypes and risk stratification but also showed associations with biologically relevant transcriptional features.