JACC. Cardiovascular imaging · 2024
Coronary Plaque Radiomic Phenotypes Predict Fatal or Nonfatal Myocardial Infarction: Analysis of the SCOT-HEART Trial.
СтатьяМногоцентровое исследованиеРандомизированное исследованиеНазвание и аннотация: Перевод в очереди
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Kolossváry Márton, Lin Andrew, Kwiecinski Jacek, Cadet Sebastien, Slomka Piotr J, Newby David E, Dweck Marc R, Williams Michelle C, Dey Damini
Аннотация
BACKGROUND: Coronary computed tomography (CT) angiography-derived attenuation-based plaque burden assessments can identify patients at risk of myocardial infarction.
OBJECTIVES: This study sought to assess whether more detailed plaque morphology assessment using patient-based radiomic characterization could further enhance the identification of patients at risk of myocardial infarction during long-term follow-up.
METHODS: Post hoc analysis of coronary CT angiography was performed within the SCOT-HEART (Scottish Computed Tomography of the HEART) clinical trial. Coronary plaque segmentations were used to calculate plaque burdens and eigen radiomic features that described plaque morphology. Univariable and multivariable Cox proportional hazard models were used to evaluate the association between clinical and image-based features and fatal or nonfatal myocardial infarction, whereas Harrell's C-statistic and cumulative/dynamic area under the curve (AUC) values with cross-validation were used to evaluate prognostic performance.
RESULTS: Scans from 1,750 patients (aged 58 ± 9 years; 56% male) were analyzed. Over a median of 8.6 years of follow-up, 82 patients had a fatal or nonfatal myocardial infarction. Among the eigen radiomic features, 15 were associated with myocardial infarction in univariable analysis, and 8 features retained their association following adjustment for cardiovascular risk score and plaque burden metrics. Adding plaque burden metrics to a clinical model incorporating cardiovascular risk score, Agatston score and presence of obstructive coronary artery disease had similar prediction performance (C-statistic 0.70 vs 0.70), whereas further addition of eigen radiomic features improved model performance (C-statistic 0.74). In temporal analysis, the model including eigen radiomic features had higher cumulative/dynamic AUC values following the fifth year of follow-up.
CONCLUSIONS: Radiomics-based precision phenotyping of coronary plaque morphology provided improvements to long-term prediction of myocardial infarction by CT angiography over and above clinical factors and plaque burden. (Scottish Computed Tomography of the HEART [SCOT-HEART]; NCT01149590).
OBJECTIVES: This study sought to assess whether more detailed plaque morphology assessment using patient-based radiomic characterization could further enhance the identification of patients at risk of myocardial infarction during long-term follow-up.
METHODS: Post hoc analysis of coronary CT angiography was performed within the SCOT-HEART (Scottish Computed Tomography of the HEART) clinical trial. Coronary plaque segmentations were used to calculate plaque burdens and eigen radiomic features that described plaque morphology. Univariable and multivariable Cox proportional hazard models were used to evaluate the association between clinical and image-based features and fatal or nonfatal myocardial infarction, whereas Harrell's C-statistic and cumulative/dynamic area under the curve (AUC) values with cross-validation were used to evaluate prognostic performance.
RESULTS: Scans from 1,750 patients (aged 58 ± 9 years; 56% male) were analyzed. Over a median of 8.6 years of follow-up, 82 patients had a fatal or nonfatal myocardial infarction. Among the eigen radiomic features, 15 were associated with myocardial infarction in univariable analysis, and 8 features retained their association following adjustment for cardiovascular risk score and plaque burden metrics. Adding plaque burden metrics to a clinical model incorporating cardiovascular risk score, Agatston score and presence of obstructive coronary artery disease had similar prediction performance (C-statistic 0.70 vs 0.70), whereas further addition of eigen radiomic features improved model performance (C-statistic 0.74). In temporal analysis, the model including eigen radiomic features had higher cumulative/dynamic AUC values following the fifth year of follow-up.
CONCLUSIONS: Radiomics-based precision phenotyping of coronary plaque morphology provided improvements to long-term prediction of myocardial infarction by CT angiography over and above clinical factors and plaque burden. (Scottish Computed Tomography of the HEART [SCOT-HEART]; NCT01149590).