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CT and MRI radiomics in cardiovascular risk prediction: a systematic review and meta-analysis by the EuSoMII
Armando Ugo Cavallo1, Andrea Ponsiglione2, Bernardo Pereira3
1Division of Radiology, Istituto Dermopatico dell'Immacolata (IDI), IRCCS, Rome, Italy.
Insights
Radiomics shows promise in predicting cardiovascular events from CT and MRI scans, achieving a pooled AUC of 0.81. However, studies exhibit moderate methodological quality and significant heterogeneity, highlighting the need for improved research standards.
Area of Science:
- Cardiology
- Radiology
- Artificial Intelligence
Background:
- Radiomics, the extraction of quantitative features from medical images, is increasingly applied to cardiac imaging.
- Predicting cardiovascular events using radiomics requires robust diagnostic accuracy and high-quality methodology.
Purpose of the Study:
- To systematically review radiomics studies in cardiac CT and MRI for cardiovascular event prediction.
- To meta-analyze the diagnostic accuracy and assess the methodological quality of these studies.
Main Methods:
- A systematic literature search was performed across major databases (Scopus, Web of Science, PubMed).
- Studies predicting cardiovascular events (major events or ischemia) were included.
- Methodological quality was assessed using the METRICS tool, and diagnostic accuracy was pooled using AUC.
Main Results:
- 202 studies were included, with 9 eligible for meta-analysis.
- The average METRICS score was 54.52% ± 15.89%, indicating moderate quality.
- The pooled AUC for event prediction was 0.81 (95% CI: 0.75-0.87), with high heterogeneity and evidence of publication bias.
Conclusions:
- Radiomics in cardiac imaging demonstrates potential for cardiovascular event prediction.
- Moderate methodological quality and significant heterogeneity necessitate careful interpretation of current findings.
- Improving research quality is crucial for the clinical translation and reproducibility of radiomics pipelines.
Objectives:
To conduct a comprehensive systematic review of the studies applying radiomics to CT and MRI for the evaluation of cardiac disease, and to perform a meta-analysis of their diagnostic accuracy, focused on cardiovascular events prediction. A secondary aim was to assess the methodological quality of cardiac imaging radiomics studies using the METRICS score.
Materials And Methods:
Four investigators searched multiple medical literature archives (Scopus, Web of Science, and PubMed). The search was conducted from February 7th, 2021, to March 10th, 2025. Papers were also screened to identify studies for the prediction of cardiovascular events, defined as the occurrence of major cardiovascular events or myocardial ischemia. Methodological quality was assessed by using the METRICS tool. Diagnostic accuracy was estimated with pooled area under the curve (AUC).
Results:
A total of 202 studies were included in the final analysis. Seventeen papers were identified for the meta-analysis, of which 9 were considered eligible for analysis. 111 papers (55%) had CT as the imaging modality, and 91 (45%) papers had MRI. Overall, the average METRICS total score was 54.52% ± 15.89%. Meta-analysis showed pooled AUC of 0.81 (95% CI: 0.75-0.87), with a high level of heterogeneity (I² = 83.4%, τ² = 0.0068). Egger's test for funnel plot asymmetry was statistically significant (z = -2.39, p = 0.017), suggesting potential publication bias.
Conclusion:
Radiomics in cardiac imaging holds potential, showing moderate quality and relatively high cumulative performance for the prediction of cardiovascular events.
Key Points:
Question What is the current methodological quality and pooled diagnostic performance of cardiovascular radiomics for predicting clinical events, based on a meta-analysis? Findings The average METRICS quality score was 54.52%. A meta-analysis showed a pooled AUC of 0.81 for event prediction, but with high heterogeneity and publication bias. Clinical relevance Assessing radiomics research methodological quality is crucial to enhance reproducibility and clinical applicability of radiomics pipelines. The evaluation of cumulative evidence for cardiovascular events prediction may guide clinical translation and future study design.
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