ArticleCNS neuroscience & therapeutics2026
Integrating Multimodal MRI Habitat and Transformer-Based Pathomics to Predict High-Risk Molecular Subtypes and Explore Biological Mechanisms in Adult Diffuse Gliomas.
Article in CNS neuroscience & therapeutics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
Abstract
backgroundThis 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.
methodsThis 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.
resultsThe 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.
conclusionThe 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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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 OpenQuestion graph.