AUTHOR=He Wenzhang , Huang He , Chen Xiaoyi , Yu Jianqun , Liu Jing , Li Xue , Yin Hongkun , Zhang Kai , Peng Liqing 

TITLE=Radiomic analysis of enhanced CMR cine images predicts left ventricular remodeling after TAVR in patients with symptomatic severe aortic stenosis

JOURNAL=Frontiers in Cardiovascular Medicine

VOLUME=Volume 9 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2022.1096422

DOI=10.3389/fcvm.2022.1096422

ISSN=2297-055X

ABSTRACT=Objective: To develop enhanced Cine images-based radiomics models for non-invasive prediction of left ventricular adverse remodeling following Transcatheter Aortic Valve Replacement (TAVR) in symptomatic severe aortic stenosis.
Methods: A total of 69 patients (Male: Female = 37: 32, median age 66 years, range 47-83) were retrospectively recruited and the severe aortic stenosis was confirmed via transthoracic echocardiography detection. The enhanced Cine images and clinical variables were collected, and three types of ROIs containing the left ventricular (LV) myocardium from the short-axis view at the basal, middle and apical LV levels were manually labeled, respectively. The radiomics features were extracted and further selected by using the least absolute shrinkage and selection operator (LASSO) regression analysis. Clinical variables were also selected through univariate regression analysis. The predictive models using logistic regression classifier were developed and validated through leave-one-out cross-validation. The model performance was evaluated with respect to discrimination, calibration and clinical usefulness.
Results: Five basal level, seven middle level, eight apical level radiomics features and three clinical factors were finally selected for model development. The radiomics models using features from basal level (Rad I), middle level (Rad II) and apical level (Rad III) had achieved AUCs of 0.761, 0.909 and 0.913 in the training dataset, and 0.718, 0.836 and 0.845 in the validation dataset, respectively. Performance of these radiomics models were improved after integrating clinical factors, with AUCs of the Combined I, Combined II and Combined III models increasing to 0.906, 0.956 and 0.959 in the training dataset, and 0.784, 0.873 and 0.891 in the validation dataset, respectively. All models showed good calibration, and the decision curve analysis indicated the Combined III model had higher net benefit than other models across majority of threshold probabilities.
Conclusion: Radiomics models and combined models at the mid and apical slices showed outstanding and comparable predictive effectiveness of adverse remodeling for symptomatic severe aortic stenosis patients after TAVR, and both were significantly better than the models of basal slices. The CMR radiomics analysis might serve as an effective tool for accurately predicting left ventricular adverse remodeling following TAVR in symptomatic severe aortic stenosis patients.