Key Takeaways & Executive Findings
- •• Radiomics models demonstrate good performance (pooled AUC 0.86) for predicting intracranial aneurysm rupture risk. • The overall quality of radiomics studies on intracranial aneurysms is moderate, with a median Radiomics Quality Score (RQS) of 10. • External validation and higher RQS are associated with improved model performance, highlighting the need for rigorous methodology. • Current radiomics studies on intracranial aneurysms face challenges including small sample sizes, lack of standardization, and limited clinical integration.
Abstract
Background: Intracranial aneurysms (IAs) are a significant cause of subarachnoid hemorrhage, with high morbidity and mortality. Radiomics, a non-invasive imaging analysis method, has shown promise in evaluating IA characteristics, including rupture risk and morphological features. However, the quality and clinical applicability of radiomics studies on IAs remain unclear. Purpose: To systematically review and meta-analyze the current literature on radiomics of IAs, assess the radiomics quality score (RQS), and evaluate the clinical utility of radiomics models. Methods: A comprehensive search of PubMed, Embase, and Web of Science was conducted up to March 2023. Studies that applied radiomics to IAs and reported diagnostic or prognostic performance were included. Data on study characteristics, radiomics workflow, model performance, and RQS were extracted. The RQS was calculated for each study, and a meta-analysis was performed to pool the area under the curve (AUC) for rupture risk prediction. Results: A total of 23 studies met the inclusion criteria. The median RQS was 10 (range 2-18), indicating overall moderate quality. The pooled AUC for rupture risk prediction was 0.86 (95% CI: 0.82-0.90), demonstrating good discriminative ability. However, significant heterogeneity was observed (I² = 78%). Subgroup analyses revealed that studies with external validation and higher RQS had better performance. Common limitations included lack of external validation, small sample sizes, and inadequate feature selection. Conclusion: Radiomics shows potential in the assessment of IAs, particularly for rupture risk stratification. However, the current evidence is limited by methodological heterogeneity and insufficient validation. Future studies should adhere to standardized protocols and incorporate external validation to enhance clinical translation.
1. Introduction
Intracranial aneurysms (IAs) are pathological dilations of the cerebral arterial wall, affecting approximately 3-5% of the general population. While many IAs remain asymptomatic, their rupture leads to subarachnoid hemorrhage (SAH), a devastating condition with high mortality and morbidity rates. The management of unruptured IAs is a clinical dilemma, as the risk of rupture must be weighed against the risks of intervention. Traditional risk stratification relies on aneurysm size, location, and morphology, but these factors are insufficient to accurately predict rupture risk. Therefore, there is a critical need for more precise risk assessment tools to guide clinical decision-making.
Radiomics, an emerging field that extracts high-throughput quantitative features from medical images, offers a non-invasive approach to characterize tissue heterogeneity and has been increasingly applied in neuro-oncology and cerebrovascular diseases. By converting medical images into mineable data, radiomics can capture subtle patterns that are invisible to the naked eye, potentially providing valuable insights into aneurysm biology and rupture risk. However, the clinical translation of radiomics in IAs is still in its infancy, with studies varying widely in methodology and reporting quality. This systematic review and meta-analysis aims to evaluate the current state of radiomics research on IAs, assess the quality of published studies using the Radiomics Quality Score (RQS), and synthesize the evidence on the diagnostic performance of radiomics models for rupture risk prediction.
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ZHANG Wei, LI Ming, WANG Fang, CHEN Jing, LIU Yang, ZHAO Lei, SUN Hong, ZHOU Qiang (2026). Radiomics Analysis of Intracranial Aneurysms: A Systematic Review and Meta-Analysis of Radiomics Quality Score and Clinical Applications. Chinese Journal of New Drugs. https://doi.org/pub_80__articleID_520
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Frequently Asked Questions
What is the radiomics quality score (RQS) and why is it important?
The Radiomics Quality Score (RQS) is a tool developed to assess the methodological quality of radiomics studies. It evaluates key aspects such as image acquisition, segmentation, feature extraction, model validation, and open data. A higher RQS indicates better adherence to standardized protocols, which is crucial for reproducibility and clinical translation. In our review, the median RQS was 10, suggesting moderate quality and highlighting areas for improvement.
How accurate are radiomics models in predicting intracranial aneurysm rupture?
Our meta-analysis found a pooled area under the curve (AUC) of 0.86 for radiomics models in predicting rupture risk, indicating good discriminative ability. However, there was significant heterogeneity among studies, and performance varied depending on factors such as external validation and RQS. Therefore, while promising, these models are not yet ready for routine clinical use without further validation.
What are the main limitations of current radiomics studies on intracranial aneurysms?
The main limitations include small sample sizes, lack of external validation, heterogeneity in imaging protocols and feature extraction methods, and insufficient reporting of model performance. Many studies also lack standardized segmentation and feature selection, which can lead to overfitting and poor generalizability. These issues contribute to the moderate RQS and hinder clinical adoption.
What recommendations can be made for future radiomics research on intracranial aneurysms?
Future studies should adhere to the Radiomics Quality Score (RQS) guidelines, include external validation cohorts, use standardized imaging protocols, and perform robust feature selection and model validation. Additionally, collaboration across institutions to create large, diverse datasets is essential to improve model generalizability. Open data and code sharing can also enhance reproducibility and facilitate clinical translation.
Can radiomics replace traditional risk factors for aneurysm rupture?
Radiomics should not replace traditional risk factors but rather complement them. While radiomics can provide additional quantitative information about aneurysm morphology and texture, integrating radiomics with clinical and morphological factors may improve risk stratification. Future models should aim to combine these data to provide a more comprehensive assessment for personalized patient management.
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