Artificial intelligence in Glioblastoma Diagnostics: Integrating MRI, histopathology, and molecular profiling
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: Gliomas are among the most aggressive and diagnostically challenging brain tumors. Conventional pathways (MRI, histopathology, clinical assessment) have limited sensitivity for early or low-grade disease and introduce delays. Artificial intelligence (AI)-particularly deep learning (e.g., CNNs)-may enhance diagnostic precision and efficiency. METHODS: We systematically searched IEEE Xplore, Scopus, Web of Science, and PubMed through July 2025 for studies on AI in brain tumor diagnostics, emphasizing MRI, fMRI, and PET. We examined AI contributions to grading, subtype differentiation, and prediction, including integrations with radiomics, multimodal fusion, transfer learning, and molecular profiling. Records were deduplicated (EndNote 21); two reviewers screened and quality-appraised studies (Newcastle-Ottawa; Cochrane). Owing to heterogeneity, we performed a narrative synthesis. RESULTS: While AI systems achieve strong performance on public benchmarks (e.g., the Brain Tumor Segmentation [BraTS] Challenge), translation into routine clinical care remains limited. Key barriers include limited model interpretability, cross-site and cross-scanner data heterogeneity, and reduced external generalizability. Inconsistent reporting and the scarcity of prospective, multi-center validation further impede adoption. Moreover, opaque decision pathways and variability in data quality and calibration across institutions undermine reliability and clinician trust. CONCLUSIONS: AI is a promising decision-support adjunct to MRI, histopathology, and molecular profiling, with potential to improve diagnostic accuracy and efficiency. Routine adoption requires prospective multi-center validation with external cohorts, standardized reporting, bias mitigation using diverse datasets, and clinically meaningful explainability, alongside regulatory clearance, workflow integration, clinician training, and post-deployment monitoring. Future work should quantify cost-effectiveness and patient-outcome benefits to justify clinical implementation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".