Deep Learning Guided Radiogenomic Signatures for Prognostic Stratification in Glioblastoma Multiforme
Bibliographic record
Abstract
Glioblastoma multiforme (GBM) is the most aggressive and lethal primary brain tumor, with an average survival of no more than 15 months despite advances in surgery, chemotherapy, and radiotherapy. Linking imaging phenotypes with genomic frameworks can improve personalized prognosis and treatment planning. This study develops a deep learning-based radiogenomic framework that integrates high-dimensional imaging features extracted from multiparametric MRI using a convolutional neural network (CNN) with key molecular biomarkers, including EGFR amplification, IDH mutation, and MGMT promoter methylation. A multiomics fusion module combined imaging-derived features with genomic alterations to enable stratified survival prediction. The publicly available datasets were used to train and validate the framework, i.e., TCIA and TCGA-GBM. The CNN-based radiogenomic model was more successful than the traditional radiomic and dictionary learning -based approaches, with high prognostic accuracy. Survival stratification into high- and low-risk groups showed significant differences, as confirmed by Kaplan–Meier analysis, C-index, and AUC metrics. The radiogenomic markers based on the model obtained biologically meaningful information on tumor heterogeneity and a better predictive outcome than the traditional methods. Radiogenomic signatures based on deep learning make it possible to prognosticate GBM accurately, non-invasively, and biologically in a manner that is precise, relevant, and now more useful in the field of neuro-oncology. The next step in research involves future multi-institutional validation, explainable AI integration, and adding more omics data to make prognostics more accurate and clinically applicable.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".