Prognostic Value of AI-derived Ventricular Imaging Phenotypes from Cardiac CTA for Long-term Risk of Myocardial Infarction --- Analysis of SCOT-HEART Trial

Abstract Background Cardiac computed tomography angiography (CTA) is widely used to assess coronary artery disease, but the prognostic value of myocardial radiomic phenotypes beyond coronary artery findings remains unknown. Objectives To investigate whether myocardial radiomic phenotypes derived from cardiac CTA provide incremental prognostic value for fatal or nonfatal myocardial infarction (MI) during long-term follow-up. Methods Post hoc analysis of CTA was performed within Scottish Computed Tomography of the Heart (SCOT-HEART) trial. A multivariable linear regression model was built to assess associations between myocardial radiomic phenotypes and cardiovascular risk factors, including hypertension, diabetes, and calcium score. Univariable and multivariable Cox proportional hazards models were used to determine the associations between myocardial radiomic phenotypes and MI. The prognostic model incorporating myocardial radiomic phenotypes was compared to clinical models based on cardiovascular risk factors, calcium score, and presence of obstructive disease to determine the improvement in predictive performance. Results Over a median follow-up of 8.6 years, 82 of 1736 participants (4.7%) experienced a fatal or nonfatal MI. Myocardial radiomic phenotypes were associated with age, sex and hypertension, and diabetes (P<0.01 for all), with older age, male sex, and cardiometabolic risk linked to increased myocardial volume and density. Among myocardial radiomic features associated with cardiovascular risk factors, univariable analysis identified 22 features associated with MI. Incorporation of myocardial radiomic phenotypes into a clinical model (including cardiovascular risk score, CAC score and presence of obstructive disease) improved discrimination for future MI events (C-index from 0.693 to 0.722, Δ=0.029, P<0.001) and improved model fit (likelihood ratio test, χ2=13.5, P=0.009). Conclusion Myocardial radiomic phenotypes based on CTA may offer incremental prognostic value for long-term myocardial infarction prediction, beyond conventional established cardiovascular risk factors and imaging features. Trial Registration: ClinicalTrials.gov Identifier: NCT01149590

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Journal
European Heart Journal - Cardiovascular Imaging
Published
2026-09-10
DOI
https://doi.org/10.1093/ehjci/jeag252
Primary Topic
Radiomics and Machine Learning in Medical Imaging
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article
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article

Prognostic Value of AI-derived Ventricular Imaging Phenotypes from Cardiac CTA for Long-term Risk of Myocardial Infarction --- Analysis of SCOT-HEART Trial

James Rudd, Michelle C. Williams, L. Escudero, Jonathan Weir‐McCall et al.
European Heart Journal - Cardiovascular Imaging
Radiomics and Machine Learning in Medical Imaging
article

Prognostic Value of AI-derived Ventricular Imaging Phenotypes from Cardiac CTA for Long-term Risk of Myocardial Infarction --- Analysis of SCOT-HEART Trial

James Rudd, Michelle C. Williams, L. Escudero, Jonathan Weir‐McCall, David E. Newby, Marc Dewey, Jian Chen
article en

Abstract

Abstract Background Cardiac computed tomography angiography (CTA) is widely used to assess coronary artery disease, but the prognostic value of myocardial radiomic phenotypes beyond coronary artery findings remains unknown. Objectives To investigate whether myocardial radiomic phenotypes derived from cardiac CTA provide incremental prognostic value for fatal or nonfatal myocardial infarction (MI) during long-term follow-up. Methods Post hoc analysis of CTA was performed within Scottish Computed Tomography of the Heart (SCOT-HEART) trial. A multivariable linear regression model was built to assess associations between myocardial radiomic phenotypes and cardiovascular risk factors, including hypertension, diabetes, and calcium score. Univariable and multivariable Cox proportional hazards models were used to determine the associations between myocardial radiomic phenotypes and MI. The prognostic model incorporating myocardial radiomic phenotypes was compared to clinical models based on cardiovascular risk factors, calcium score, and presence of obstructive disease to determine the improvement in predictive performance. Results Over a median follow-up of 8.6 years, 82 of 1736 participants (4.7%) experienced a fatal or nonfatal MI. Myocardial radiomic phenotypes were associated with age, sex and hypertension, and diabetes (P<0.01 for all), with older age, male sex, and cardiometabolic risk linked to increased myocardial volume and density. Among myocardial radiomic features associated with cardiovascular risk factors, univariable analysis identified 22 features associated with MI. Incorporation of myocardial radiomic phenotypes into a clinical model (including cardiovascular risk score, CAC score and presence of obstructive disease) improved discrimination for future MI events (C-index from 0.693 to 0.722, Δ=0.029, P<0.001) and improved model fit (likelihood ratio test, χ2=13.5, P=0.009). Conclusion Myocardial radiomic phenotypes based on CTA may offer incremental prognostic value for long-term myocardial infarction prediction, beyond conventional established cardiovascular risk factors and imaging features. Trial Registration: ClinicalTrials.gov Identifier: NCT01149590

European Heart Journal - Cardiovascular Imaging
King's College London (GB), Papworth Hospital (GB), British Heart Foundation (GB), Cambridge School (PT), Royal Brompton Hospital (GB)
Peace, Justice and strong institutions
Openalex Percentile: Top 11%
Radiomics and Machine Learning in Medical Imaging
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