ReviewDiagnostics (Basel, Switzerland)2024
Radiomic Features as Artificial Intelligence Prognostic Models in Glioblastoma: A Systematic Review and Meta-Analysis.
Review in Diagnostics (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- PET|MR Open Source Radiomics Models for Treatment Outcome Prediction in Recurrent GBM.Cancers · 2026Article
- ChatNSG: An Overview of Contemporary and Emerging Artificial Intelligence Models for the Neurosurgeon.Journal of neurological surgery. Part B, Skull base · 2026Article
- Neurosurgical and neuro-oncological outcomes of confirmatory brain biopsies in patients with glioblastoma: a real-life monocentric experience.Brain & spine · 2026Article
- Late enhancement and wash-out maps for differentiation of glioblastoma and metastases.BMC medical imaging · 2025Article
- Artificial Intelligence in Glioblastoma-Transforming Diagnosis and Treatment.Chinese neurosurgical journal · 2025Review
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Authors and funding
5 authors.
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Abstract
backgroundGlioblastoma, 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.
methodsWe 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.
resultsIn 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).
conclusionsAn RF-AI-based approach offers prognostic significance for OS and PFS in patients with glioblastoma.
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