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Review
. 2024 Oct 22;14(21):2354.
doi: 10.3390/diagnostics14212354.

Radiomic Features as Artificial Intelligence Prognostic Models in Glioblastoma: A Systematic Review and Meta-Analysis

Dewa Putu Wisnu Wardhana  1 , Sri Maliawan  2 , Tjokorda Gde Bagus Mahadewa  2 , Rohadi Muhammad Rosyidi  3 , Sinta Wiranata  4
Affiliations
Review

Radiomic Features as Artificial Intelligence Prognostic Models in Glioblastoma: A Systematic Review and Meta-Analysis

Dewa Putu Wisnu Wardhana et al. Diagnostics (Basel). .

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.

Keywords: artificial intelligence; glioblastoma; overall survival; progression-free survival; radiomic features.

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
PRISMA diagram of the selection process.
Figure 2
Figure 2
Overall survival (OS) analysis of the included studies [30,31,32,33,34,35,36,39,40,41,42,43].
Figure 3
Figure 3
Progression-free survival (PFS) analysis of the included studies [30,33,34,36,37,38,39,42].
See this image and copyright information in PMC

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