Key result
Non-contrast Cine-CMR radiomics accurately detects MI with a 0.93 AUC.
Why the study?
Robust differentiation between infarcted and normal tissue is important for clinical diagnosis and precision medicine.
Does radiomics analysis and machine learning on non-contrast Cine-CMR images accurately detect myocardial infarction?
Population
Myocardial infarction and viable tissues or normal cases
Comparison
Myocardial infarction vs viable tissues or normal cases
Design
Machine learning radiomics analysis study
Authors
Loading...
May enable contrast-free MI detection in select patients; extends radiomics validation to Cine-CMR via RCT data.
Observational
Does radiomics analysis and machine learning on non-contrast Cine-CMR images accurately detect myocardial infarction?
Effect estimate: AUC 0.93
Radiomics analysis combined with machine learning on non-contrast Cine-CMR images can accurately detect myocardial infarction, offering a potential alternative to contrast-enhanced LGE-CMR.
Avard et al. (2021) conducted an observational in Myocardial infarction. Radiomics and machine learning on non-contrast Cine-CMR vs. Viable tissues/normal cases was evaluated on Differentiation of myocardial infarction and viable tissues/normal cases (AUC 0.93). Radiomics analysis using Logistic Regression on non-contrast Cine-CMR images accurately detected myocardial infarction with an AUC of 0.93 ± 0.03.
Synapse has enriched 3 closely related papers on similar clinical questions. Consider them for comparative context: