An interpretable radiomics-based machine learning model for predicting reverse left ventricular remodeling in STEMI patients using late gadolinium enhancement of myocardial scar.

Objective: To evaluate the added value of the late gadolinium enhancement (LGE)-scar radiomics features in predicting reverse left ventricular remodeling (r-LVR) in ST-segment elevation myocardial infarction (STEMI) patients using machine learning (ML).

Methods: This retrospective study included 105 STEMI patients who underwent CMR within 7 days and 5 months post-percutaneous coronary intervention (PCI) on 1.5-T or 3.0-T MRI scanners (January 2014-2023). Radiomics features from LGE scar images and routine CMR markers were analyzed using a LightGBM model enhanced by Shapley Additive exPlanations (SHAP) for interpretability. Patients were divided into training (80) and test (25) sets. Three predictive models were developed: traditional CMR, LGE-scar radiomics, and a combined model integrating both. Model performance was assessed using ROC curves and AUC analysis.

Results: In the training set, the traditional CMR model achieved an AUC of 0.745 (95% CI: 0.62-0.86), the LGE-scar radiomics model had an AUC of 0.712 (95% CI: 0.58-0.83), and the combined model showed the highest AUC of 0.754 (95% CI: 0.63-0.86). In the test set, the traditional CMR model's AUC decreased to 0.656 (95% CI: 0.42-0.88), while the LGE-scar radiomics model improved to 0.818 (95% CI: 0.59-1.00). The combined model achieved the highest AUC of 0.890 (95% CI: 0.75-1.00). SHAP analysis highlighted significant predictors such as infarct percentage of LV mass and wavelet-transformed texture features.

Conclusions: Integrating LGE scar radiomics features with traditional CMR parameters in a LightGBM model enhances predictive accuracy for r-LVR in STEMI patients, potentially improving patient stratification and treatment personalization. Conclusions: Question Predicting r-LVR in STEMI patients remains challenging due to limitations in current imaging approaches. Findings Integrating LGE-scar radiomics and cardiac magnetic resonance markers in the LightGBM model significantly improves prediction accuracy for r-LVR. Clinical relevance This interpretable ML model enhances r-LVR prediction, supporting patient stratification and optimizing treatment strategies to improve patient outcomes.