Radiomics Analysis Derived From LGE-MRI Predict Sudden Cardiac Death in Participants With Hypertrophic Cardiomyopathy

Jie Wang; Laura Bravo; Jinquan Zhang; Wen Liu; Ke Wan; Jiayu Sun; Yanjie Zhu; Yuchi Han; Georgios V Gkoutos; Yucheng Chen

doi:10.3389/fcvm.2021.766287

Radiomics Analysis Derived From LGE-MRI Predict Sudden Cardiac Death in Participants With Hypertrophic Cardiomyopathy

Front Cardiovasc Med. 2021 Dec 10:8:766287. doi: 10.3389/fcvm.2021.766287. eCollection 2021.

Authors

Jie Wang^{1

2}, Laura Bravo², Jinquan Zhang³, Wen Liu³, Ke Wan⁴, Jiayu Sun⁵, Yanjie Zhu⁶, Yuchi Han⁷, Georgios V Gkoutos^{2

8

9}, Yucheng Chen^{1

10}

Affiliations

¹ Department of Cardiology, West China Hospital, Sichuan University, Chengdu, China.
² College of Medical and Dental Sciences, Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, United Kingdom.
³ West China School of Public Health, Sichuan University, Chengdu, China.
⁴ Department of Geriatrics, West China Hospital, Sichuan University, Chengdu, China.
⁵ Department of Radiology, West China Hospital, Sichuan University, Chengdu, China.
⁶ Paul C. Lauterbur Research Centre for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences (CAS), Shenzhen, China.
⁷ Department of Medicine (Cardiovascular Division), University of Pennsylvania, Philadelphia, PA, United States.
⁸ Institute of Translational Medicine, University Hospitals Birmingham NHS Foundation Trust, Birmingham, United Kingdom.
⁹ Health Data Research UK (HDR), Midlands Site, United Kingdom.
¹⁰ Center of Rare Diseases, West China Hospital, Sichuan University, Chengdu, China.

Abstract

Objectives: To identify significant radiomics features derived from late gadolinium enhancement (LGE) images in participants with hypertrophic cardiomyopathy (HCM) and assess their prognostic value in predicting sudden cardiac death (SCD) endpoint. Method: The 157 radiomic features of 379 sequential participants with HCM who underwent cardiovascular magnetic resonance imaging (MRI) were extracted. CoxNet (Least Absolute Shrinkage and Selection Operator (LASSO) and Elastic Net) and Random Forest models were applied to optimize feature selection for the SCD risk prediction and cross-validation was performed. Results: During a median follow-up of 29 months (interquartile range, 20-42 months), 27 participants with HCM experienced SCD events. Cox analysis revealed that two selected features, local binary patterns (LBP) (19) (hazard ratio (HR), 1.028, 95% CI: 1.032-1.134; P = 0.001) and Moment (1) (HR, 1.212, 95%CI: 1.032-1.423; P = 0.02) provided significant prognostic value to predict the SCD endpoints after adjustment for the clinical risk predictors and late gadolinium enhancement. Furthermore, the univariately significant risk predictor was improved by the addition of the selected radiomics features, LBP (19) and Moment (1), to predict SCD events (P < 0.05). Conclusion: The radiomics features of LBP (19) and Moment (1) extracted from LGE images, reflecting scar heterogeneity, have independent prognostic value in identifying high SCD risk patients with HCM.

Keywords: hypertrophic cardiomyopathy; late gadolinium enhancement; machine learning; radiomics; sudden cardiac death.