Jie Wang1,2, Fuyao Yang1, Wentao Liu3, Jiayu Sun4, Yuchi Han5, Dong Li6, Georgios V Gkoutos2,7, Yanjie Zhu8, Yucheng Chen1,4,9. 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, UK. 3. Medical Big Data Center, West China Hospital, Sichuan University, Chengdu, P. R. China. 4. Department of Radiology, West China Hospital, Sichuan University, Chengdu, P. R. China. 5. Department of Medicine (Cardiovascular Division), University of Pennsylvania, Philadelphia, Pennsylvania, USA. 6. Division of Hospital Medicine, Emory University School of Medicine, Atlanta, Georgia, USA. 7. MRC Health Data Research UK (HDR UK), London, UK. 8. Paul C. Lauterbur Research Centre for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, P. R. China. 9. Center of Rare diseases, West China Hospital, Sichuan University, Chengdu, P. R. China.
Abstract
BACKGROUND: The phenotype via conventional cardiac MRI analysis of MYH7 (β-myosin heavy chain)- and MYBPC3 (β-myosin-binding protein C)-associated hypertrophic cardiomyopathy (HCM) groups is similar. Few studies exist on the genotypic-phenotypic association as assessed by machine learning in HCM patients. PURPOSE: To explore the phenotypic differences based on radiomics analysis of T1 mapping images between MYH7 and MYBPC3-associated HCM subgroups. STUDY TYPE: Prospective observational study. SUBJECTS: In all, 102 HCM patients with pathogenic, or likely pathogenic mutation, in MYH7 (n = 68) or MYBPC3 (n = 34) genes. FIELD STRENGTH/SEQUENCE: Cardiac MRI was performed at 3.0T with balanced steady-state free precession (bSSFP), phase-sensitive inversion recovery (PSIR) late gadolinium enhancement (LGE), and modified Look-Locker inversion recovery (MOLLI) T1 mapping sequences. ASSESSMENT: All patients underwent next-generation sequencing and Sanger genetic sequencing. Left ventricular native T1 and LGE were analyzed. One hundred and fifty-seven radiomic features were extracted and modeled using a support vector machine (SVM) combined with principal component analysis (PCA). Each subgroup was randomly split 4:1 (feature selection / test validation). STATISTICAL TESTS: Mann-Whitney U-tests and Student's t-tests were performed to assess differences between subgroups. A receiver operating characteristic (ROC) curve was used to assess the model's ability to stratify patients based on radiomic features. RESULTS: There were no significant differences between MYH7- and MYBPC3-associated HCM subgroups based on traditional native T1 values (global, basal, and middle short-axis slice native T1 ; P = 0.760, 0.914, and 0.178, respectively). However, the SVM model combined with PCA achieved an accuracy and area under the curve (AUC) of 92.0% and 0.968 (95% confidence interval [CI]: 0.968-0.971), respectively. For the test validation dataset, the accuracy and AUC were 85.5% and 0.886 (95% CI: 0.881-0.901), respectively. DATA CONCLUSION: Radiomic analysis of native T1 mapping images may be able to discriminate between MYH7- and MYBPC3-associated HCM patients, exceeding the performance of conventional native T1 values. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY STAGE: 2 J. MAGN. RESON. IMAGING 2020;52:1714-1721.
BACKGROUND: The phenotype via conventional cardiac MRI analysis of MYH7 (β-myosin heavy chain)- and MYBPC3 (β-myosin-binding protein C)-associated hypertrophic cardiomyopathy (HCM) groups is similar. Few studies exist on the genotypic-phenotypic association as assessed by machine learning in HCM patients. PURPOSE: To explore the phenotypic differences based on radiomics analysis of T1 mapping images between MYH7 and MYBPC3-associated HCM subgroups. STUDY TYPE: Prospective observational study. SUBJECTS: In all, 102 HCM patients with pathogenic, or likely pathogenic mutation, in MYH7 (n = 68) or MYBPC3 (n = 34) genes. FIELD STRENGTH/SEQUENCE: Cardiac MRI was performed at 3.0T with balanced steady-state free precession (bSSFP), phase-sensitive inversion recovery (PSIR) late gadolinium enhancement (LGE), and modified Look-Locker inversion recovery (MOLLI) T1 mapping sequences. ASSESSMENT: All patients underwent next-generation sequencing and Sanger genetic sequencing. Left ventricular native T1 and LGE were analyzed. One hundred and fifty-seven radiomic features were extracted and modeled using a support vector machine (SVM) combined with principal component analysis (PCA). Each subgroup was randomly split 4:1 (feature selection / test validation). STATISTICAL TESTS: Mann-Whitney U-tests and Student's t-tests were performed to assess differences between subgroups. A receiver operating characteristic (ROC) curve was used to assess the model's ability to stratify patients based on radiomic features. RESULTS: There were no significant differences between MYH7- and MYBPC3-associated HCM subgroups based on traditional native T1 values (global, basal, and middle short-axis slice native T1 ; P = 0.760, 0.914, and 0.178, respectively). However, the SVM model combined with PCA achieved an accuracy and area under the curve (AUC) of 92.0% and 0.968 (95% confidence interval [CI]: 0.968-0.971), respectively. For the test validation dataset, the accuracy and AUC were 85.5% and 0.886 (95% CI: 0.881-0.901), respectively. DATA CONCLUSION: Radiomic analysis of native T1 mapping images may be able to discriminate between MYH7- and MYBPC3-associated HCM patients, exceeding the performance of conventional native T1 values. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY STAGE: 2 J. MAGN. RESON. IMAGING 2020;52:1714-1721.
Authors: Viktoria Höller; Heidelis Seebacher; David Zach; Nora Schwegel; Klemens Ablasser; Ewald Kolesnik; Johannes Gollmer; Gert Waltl; Peter P Rainer; Sarah Verheyen; Andreas Zirlik; Nicolas Verheyen Journal: Genes (Basel) Date: 2021-09-23 Impact factor: 4.096
Authors: Andrea Baggiano; Alberico Del Torto; Marco Guglielmo; Giuseppe Muscogiuri; Laura Fusini; Mario Babbaro; Ada Collevecchio; Rocco Mollace; Stefano Scafuri; Saima Mushtaq; Edoardo Conte; Andrea Daniele Annoni; Alberto Formenti; Maria Elisabetta Mancini; Giulia Mostardini; Daniele Andreini; Andrea Igoren Guaricci; Mauro Pepi; Marianna Fontana; Gianluca Pontone Journal: Diagnostics (Basel) Date: 2020-09-29
Authors: Jie Wang; Laura Bravo; Jinquan Zhang; Wen Liu; Ke Wan; Jiayu Sun; Yanjie Zhu; Yuchi Han; Georgios V Gkoutos; Yucheng Chen Journal: Front Cardiovasc Med Date: 2021-12-10