Using radiomics analysis and machine learning algorithms to detect post-infarction cardiosclerosis in patients with ischemic cardiomyopathy based on non-contrast cardiac magnetic resonance imaging

Aim . To study and compare the informative value of machine learning models based on radiomics parameters of non-contrast cine-cardiac magnetic resonance imaging (CMI) for differentiating between areas of post-infarction cardiosclerosis and intact myocardium in patients with ischemic cardiomyopathy (ICM). Material and methods. This retrospective study included 88 patients with indications for surgical treatment of ICM. Preoperatively, all patients underwent contrastenhanced cardiac MRI. Radiomics analysis was applied to unenhanced cine MRI images. All images were segmented using 3D slicer software (version 5.2.2), and radiomic features were extracted using the SlicerRadiomics module. Texture analysis was performed on 176 cine cardiac MRI image regions, with 107 texture features identified for each one. The following machine learning algorithms were used: random forest, logistic regression, and gradient boosting with and without feature selection. Statistical data processing and machine learning model generation were performed using the Python programming language. R e sults . Collinearity diagrams were constructed. Non-valuable features were identified. The cumulative value of features was estimated based on their total number, and the parameters with the least value were identified. Thirty-four features were found to contribute nothing to the total value. Lasso regularization resulted in the selection of the 10 most informative parameters (three shape features, two firstorder features, and five texture characteristics). Along with the logistic regression model (AUC=0,83), the CatBoost gradient boosting algorithm demonstrated high performance (AUC=0,8), and feature selection improved all models used in the study, with the highest CatBoost scores (AUC=0,83). The random forest algorithm demonstrated less reliable results compared to other models and can be considered as an additional method when feature interpretation is required. Conclusion . Radiomics analysis has demonstrated high potential for differentiating scarred and viable left ventricular myocardium in patients with ICM. This opens up the prospect of using it as an alternative to traditional delayed contrast-enhanced methods in these patients.