SynthesisFrontiers in neuroscience2024
Predictive value of radiomics for intracranial aneurysm rupture: a systematic review and meta-analysis.
Synthesis in Frontiers in neuroscience, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
3 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The roles of radiomics and deep learning for automatic detection, stability assessment, and rupture risk prediction in intracranial aneurysms: a systematic review.European journal of medical research · 2025Pooled it
- Association of A1 Segment Morphology with the Rupture Risk and Morphology of Anterior Communicating Artery Aneurysms: A Retrospective, Single-Center Study.Journal of clinical medicine · 2026Article
- Radiomics and deep learning for intracranial aneurysm rupture-status assessment on CTA/MRA: a multicenter retrospective study.Frontiers in neuroscience · 2026Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: To systematically review the literature on radiomics for predicting intracranial aneurysm rupture and conduct a meta-analysis to obtain evidence confirming the value of radiomics in this prediction. Methods: A systematic literature search was conducted in PubMed, Web of Science, Embase, and The Cochrane Library databases up to March 2024. The QUADAS-2 tool was used to assess study quality. Stata 15.0 and Review Manager 5.4.1 were used for statistical analysis. Outcomes included combined sensitivity (Sen), specificity (Spe), positive likelihood ratio (+LR), negative likelihood ratio (-LR), diagnostic odds ratio (DOR), and their 95% confidence intervals (CI), as well as pre-test and post-test probabilities. The SROC curve was plotted, and the area under the curve (AUC) was calculated. Publication bias and small-study effects were assessed using the Deeks' funnel plot. Results: The 9 included studies reported 4,284 patients, with 1,411 patients with intracranial aneurysm rupture (prevalence 32.9%). The overall performance of radiomics for predicting intracranial aneurysm rupture showed a combined Sen of 0.78 (95% CI: 0.74-0.82), Spe of 0.74 (95% CI: 0.70-0.78), +LR of 3.0 (95% CI: 2.7-3.4), -LR of 0.29 (95% CI: 0.25-0.35), DOR of 10 (95% CI: 9-12), and AUC of 0.83 (95% CI: 0.79-0.86). Significant heterogeneity was observed in both Sen (I Conclusion: Radiomics can improve the diagnostic efficacy of intracranial aneurysm rupture. More large-sample, prospective, multicenter clinical studies are needed to further evaluate its predictive value. Systematic review registration: https://www.crd.york.ac.uk/PROSPERO/.
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Registered trials
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