ArticleEuropean radiology2023
Development and validation of a deep learning model for prediction of intracranial aneurysm rupture risk based on multi-omics factor.
Article in European radiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 6 of them syntheses that pooled it.
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25 citing papers in PubMed, 6 syntheses or guidelines pooled it, 37 citations in OpenAlex.
- Radiomics for differentiating ruptured intracranial aneurysms: overview, methodological quality evaluation using METRICS and RQS, and feature transportability validation using independent multi-center dataset.European radiology · 2026Pooled 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
- Diagnostic and predictive value of radiomics-based machine learning for intracranial aneurysm rupture status: a systematic review and meta-analysis.Neurosurgical review · 2024Pooled it
- Predictive value of radiomics for intracranial aneurysm rupture: a systematic review and meta-analysis.Frontiers in neuroscience · 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
- Pooled it
- Artificial Intelligence for Cerebral Aneurysm Management: Integrating Imaging, Hemodynamics, and Clinical Decision Support.Journal of imaging informatics in medicine · 2026Review
- Prediction of Posterior Communicating Artery Aneurysm Rupture Risk: A Multivariate Analysis of Aneurysm and Surrounding Arterial Morphological Factors.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
- Transformer-based fusion of radiomics-habitat and deep learning for assessing unruptured intracranial aneurysm instability.Frontiers in neuroscience · 2026Article
- Article
- Development and Validation of a Prediction Model for Intracranial Aneurysm Rupture Risk.JAMA network open · 2025Article
- 3D Quantified Morphologic Predictors of Intracranial Aneurysm Instability: A Longitudinal Study.AJNR. American journal of neuroradiology · 2025Article
- Assessment of the stability of intracranial aneurysms using a deep learning model based on computed tomography angiography.La Radiologia medica · 2025Article
- Integrating Clinical Data and Radiomics and Deep Learning Features for End-to-End Delayed Cerebral Ischemia Prediction on Noncontrast CT.AJNR. American journal of neuroradiology · 2024Article
- For whom the bell tolls - do we overestimate wall enhancement of intracranial aneurysms?European radiology · 2024Article
- Integrating PointNet-Based Model and Machine Learning Algorithms for Classification of Rupture Status of IAs.Bioengineering (Basel, Switzerland) · 2024Article
- Risk factors of unruptured intracranial aneurysms instability in the elderly.Acta neurochirurgica · 2024Article
- Enhancing intracranial aneurysm rupture risk prediction with a novel multivariable logistic regression model incorporating high-resolution vessel wall imaging.Frontiers in neurology · 2024Article
- Predicting Pathological Response of Neoadjuvant Conversion Therapy for Hepatocellular Carcinoma Patients Using CT-Based Radiomics Model.Journal of hepatocellular carcinoma · 2024Article
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13 authors at 4 institutions in 1 country.
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Abstract
objectiveThe clinical ability of radiomics to predict intracranial aneurysm rupture risk remains unexplored. This study aims to investigate the potential uses of radiomics and explore whether deep learning (DL) algorithms outperform traditional statistical methods in predicting aneurysm rupture risk.
methodsThis retrospective study included 1740 patients with 1809 intracranial aneurysms confirmed by digital subtraction angiography at two hospitals in China from January 2014 to December 2018. We randomly divided the dataset (hospital 1) into training (80%) and internal validation (20%). External validation was performed using independent data collected from hospital 2. The prediction models were developed based on clinical, aneurysm morphological, and radiomics parameters by logistic regression (LR). Additionally, the DL model for predicting aneurysm rupture risk using integration parameters was developed and compared with other models.
resultsThe AUCs of LR models A (clinical), B (morphological), and C (radiomics) were 0.678, 0.708, and 0.738, respectively (all p < 0.05). The AUCs of the combined feature models D (clinical and morphological), E (clinical and radiomics), and F (clinical, morphological, and radiomics) were 0.771, 0.839, and 0.849, respectively. The DL model (AUC = 0.929) outperformed the machine learning (ML) (AUC = 0.878) and the LR models (AUC = 0.849). Also, the DL model has shown good performance in the external validation datasets (AUC: 0.876 vs 0.842 vs 0.823, respectively).
conclusionRadiomics signatures play an important role in predicting aneurysm rupture risk. DL methods outperformed conventional statistical methods in prediction models for the rupture risk of unruptured intracranial aneurysms, integrating clinical, aneurysm morphological, and radiomics parameters. KEY POINTS: • Radiomics parameters are associated with the rupture risk of intracranial aneurysms. • The prediction model based on integrating parameters in the deep learning model was significantly better than a conventional model. • The radiomics signature proposed in this study could guide clinicians in selecting appropriate patients for preventive treatment.
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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.