Related Experiment Video
Updated: Sep 29, 2026
Digital Spatial Profiling for Characterization of the Microenvironment in Adult-Type Diffusely Infiltrating Glioma
Published on: September 13, 2022
Integrating Multimodal MRI Habitat and Transformer-Based Pathomics to Predict High-Risk Molecular Subtypes and
Wenju Niu1,2, Xin Duan1,2, Xuan Li1,2
1Department of Radiology, First Hospital of Shanxi Medical University, Taiyuan, Shanxi Province, China.
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.
