Objectives 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). Materials and 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. Conclusion 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. Key Points <Emphasis Type="BoldItalic">Question</Emphasis> Predicting r-LVR in STEMI patients remains challenging due to limitations in current imaging approaches. <Emphasis Type="BoldItalic">Findings</Emphasis> Integrating LGE-scar radiomics and cardiac magnetic resonance markers in the LightGBM model significantly improves prediction accuracy for r-LVR. <Emphasis Type="BoldItalic">Clinical relevance</Emphasis> This interpretable ML model enhances r-LVR prediction, supporting patient stratification and optimizing treatment strategies to improve patient outcomes. Graphical Abstract

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

期刊论文
发表日期：

2025年4月3日

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Xiuzheng Yue,
Jianing Cui,
Sicong Huang,
Wenjia Liu,
Jing Qi,
Kunlun He,
Tao Li

摘要

Objectives

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).

Materials and 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.

Conclusion

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.

Key Points

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.

Graphical Abstract