Radiomics-based machine learning models in STEMI: a promising tool for the prediction of major adverse cardiac events

ConclusionThe radiomics-based ML models incorporating clinical and conventional MRI parameters are promising for predicting MACE occurrence in STEMI patients in the follow-up period.Key Points•Acute coronary occlusion results in variable changes at the cellular level ranging from myocyte swelling to myonecrosis depending on the duration of the ischemia and the metabolic state of the heart, which causes subtle heterogeneous signal changes that are imperceptible to the human eye with cardiac MRI.•Radiomics-based machine learning analysis of cardiac MR images is promising for risk prediction.•Combining MRI-derived parameters and clinical variables increases the accuracy of predictive models.
Source: European Radiology - Category: Radiology Source Type: research