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Background <p>There is a certain correlation between left ventricular non-compaction (LVNC) and ischemic stroke (IS); however, there are currently no predictive models available to assess the risk of IS in LVNC patients.</p> Methods <p>A multicenter retrospective study included 309 LVNC patients from two institutions. Institution 1 (228 patients) provided a training and internal validation set, while institution 2 (81 patients) served as the external validation set. The deep transfer learning features were extracted using a ResNet101-based model, and the radiomics features were extracted using Pyradiomics. Feature selection was done with LASSO, the selected features were input into XGBoost to construct the Res101-RAD-XGB model. Model performance was evaluated using receiver operating characteristic (ROC) curves and decision curve analysis (DCA), and the model’s interpretability was assessed using SHAP analysis and the Grad-CAM method. Finally, the performance of the Res101-RAD-XGB model was compared with that of clinical criteria to assess its effectiveness.</p> Results <p>The Res101-RAD-XGB model achieved area under the curve (AUCs) of 0.942, 0.913, and 0.921 for the training, internal validation, and external validation sets, outperforming models using only one type of feature and clinical criteria.</p> Conclusion <p>The Res101-RAD-XGB model can accurately identify LVNC patients who are at risk of IS, enabling specialists to develop targeted preventive strategies for high-risk individuals.</p>

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Identification of ischemic stroke risk in patients with left ventricular non-compaction using echocardiography, deep learning, and radiomics

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月1日

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  • Jiangtao Wang,
  • Wensheng Tao,
  • Zhenzhen Wang,
  • Biaohu Liu

摘要

Background

There is a certain correlation between left ventricular non-compaction (LVNC) and ischemic stroke (IS); however, there are currently no predictive models available to assess the risk of IS in LVNC patients.

Methods

A multicenter retrospective study included 309 LVNC patients from two institutions. Institution 1 (228 patients) provided a training and internal validation set, while institution 2 (81 patients) served as the external validation set. The deep transfer learning features were extracted using a ResNet101-based model, and the radiomics features were extracted using Pyradiomics. Feature selection was done with LASSO, the selected features were input into XGBoost to construct the Res101-RAD-XGB model. Model performance was evaluated using receiver operating characteristic (ROC) curves and decision curve analysis (DCA), and the model’s interpretability was assessed using SHAP analysis and the Grad-CAM method. Finally, the performance of the Res101-RAD-XGB model was compared with that of clinical criteria to assess its effectiveness.

Results

The Res101-RAD-XGB model achieved area under the curve (AUCs) of 0.942, 0.913, and 0.921 for the training, internal validation, and external validation sets, outperforming models using only one type of feature and clinical criteria.

Conclusion

The Res101-RAD-XGB model can accurately identify LVNC patients who are at risk of IS, enabling specialists to develop targeted preventive strategies for high-risk individuals.