ArticleHeliyon2023
A novel clinical-radscore nomogram for predicting ruptured intracranial aneurysm.
Article in Heliyon, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 3 of them syntheses that pooled it.
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Who cites it
8 citing papers in PubMed, 3 syntheses or guidelines pooled it, 7 citations in OpenAlex.
- 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
- Diagnostic and predictive value of radiomics-based machine learning for intracranial aneurysm rupture status: a systematic review and meta-analysis.Neurosurgical review · 2024Pooled it
- Systematic Review of Radiomics and Artificial Intelligence in Intracranial Aneurysm Management.Journal of neuroimaging : official journal of the American Society of NeuroimagingPooled it
- Radiomics model for risk stratification of intracranial aneurysm: a high-resolution vessel wall imaging-based study.BMC medical imaging · 2026Article
- Prediction of depression development in Parkinson's disease patients: a radiomics-based machine learning study.BMC neurology · 2026Article
- CT-based habitat imaging integrated with radiomics and clinicopathology for noninvasive prediction of microvascular invasion in hepatocellular carcinoma.Frontiers in oncology · 2026Article
- Development and validation of nomograms for aneurysm rupture risk and prognosis in Moyamoya disease with intracranial aneurysms.Scientific reports · 2025Article
- Correlation of plasma plectin levels with small intracranial aneurysms instability and prognosis.Neurosurgical review · 2025Article
Corrections and comments
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Authors and funding
9 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objectives: Our study aims to find the more practical and powerful method to predict intracranial aneurysm (IA) rupture through verification of predictive power of different models. Methods: Clinical and imaging data of 576 patients with IAs including 192 ruptured IAs and matched 384 unruptured IAs was retrospectively analyzed. Radiomics features derived from computed tomography angiography (CTA) images were selected by Results: Five inflammatory features, 2 radiological characteristics and 7 radiomics features were significantly associated with IA rupture. The areas under ROCs of the radscore, inflammatory, clinical and C-R models were 0.814, 0.935, 0.970 and 0.975 in the training cohort and 0.805, 0.927, 0.952 and 0.962 in the validation cohort, respectively. Conclusion: The inflammatory model performs particularly well in predicting the risk of IA rupture, and its predictive power is further improved by combining with radiological and radiomics features and the C-R model performs the best. The C-R nomogram is a more stable and effective tool than PHASES and ELAPSS for individually predicting the risk of rupture for patients with IA.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.