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Abstract #3541

Native T1 Mapping Radiomics for Classification of Pulmonary Valve Replacement Risk Factors in Patients with Repaired Tetralogy of Fallot

Jo-Hua Peng1, Ming-Ting Wu2, Nai-Yu Pan3, Teng-Yi Huang3, Yi-Jui Liu4, Ken-Pen Weng5,6, and Hsu-Hsia Peng1
1Department of Biomedical Engineering and Environmental Sciences, National Tsing Hua University, Hsinchu, Taiwan, 2Department of Radiology, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan, 3Department of Electrical Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan, 4Department of Automatic Control Engineering, Feng Chia University, Taichung, Taiwan, 5Department of Pediatrics, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan, 6Dpartment of Pediatrics, National Yang Ming Chiao Tung University, Hsinchu, Taiwan

Synopsis

Keywords: Myocardium, Radiomics

Motivation: Right ventricular (RV) dilation and exercise intolerance are important prognostic indicators in repaired Tetralogy of Fallot (rTOF) patients. The conventional native T1 value may ignore subtle changes of myocardial fibrosis patterns in rTOF patients. Radiomics uncovers concealed insights regarding cardiomyopathy.

Goal(s): To establish a radiomics model using native T1 mapping for identifying rTOF patients with severe RV dilation and exercise intolerance.

Approach: We extracted 623 radiomic features from native T1 mapping and employed machine learning for feature selection and classification that enhance diagnostic accuracy in identifying cardiac involvements.

Results: Optimal performance was achieved in the segmental mid-slice T1 mapping model.

Impact: The radiomic analysis of myocardial native T1 can reveal the different myocardial T1 distribution patterns between different severity of RV dilation and exercise intolerance before substantial changes of conventional native T1 values.

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