Radiomic Features as Artificial Intelligence Prognostic Models in Glioblastoma: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: Glioblastoma, the predominant primary tumor among all central nervous systems, accounts for around 80% of cases. Prognosis in neuro-oncology involves assessing the disease's progression in different individuals, considering the time between the initial pathological diagnosis and the time until the disease worsens. A noninvasive therapeutic approach called radiomic features (RFs), which involves the application of artificial intelligence in MRI, has been developed to address this issue. This study aims to systematically gather evidence and evaluate the prognosis significance of radiomics in glioblastoma using RFs. METHODS: We conducted an extensive search across the PubMed, ScienceDirect, EMBASE, Web of Science, and Cochrane databases to identify relevant original studies examining the use of RFs to evaluate the prognosis of patients with glioblastoma. This thorough search was completed on 25 July 2024. Our search terms included glioblastoma, MRI, magnetic resonance imaging, radiomics, and survival or prognosis. We included only English-language studies involving human subjects, excluding case reports, case series, and review studies. The studies were classified into two quality categories: those rated 4-6 were considered moderate-, whereas those rated 7-9 were high-quality using the Newcastle-Ottawa Scale (NOS). Hazard ratios (HRs) and their 95% confidence intervals (CIs) for OS and PFS were combined using random effects models. RESULTS: In total, 253 studies were found in the initial search across the five databases. After screening the articles, 40 were excluded due to not meeting the eligibility criteria, and we included only 14 studies. All twelve OS and eight PFS trials were considered, involving 1.639 and 747 patients, respectively. The random effects model was used to calculate the pooled HRs for OS and PFS. The HR for OS was 3.59 (95% confidence interval [CI], 1.80-7.17), while the HR for PFS was 4.20 (95% CI, 1.02-17.32). CONCLUSIONS: An RF-AI-based approach offers prognostic significance for OS and PFS in patients with glioblastoma.
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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 | 0.012 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.029 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".