Native T1 Mapping Radiomics for Classifying Severity of Pulmonary Regurgitation in Patients with Repaired Tetralogy of Fallot

Motivation: Pulmonary regurgitation (PR) severity is an important prognostic indicator in repaired Tetralogy of Fallot (rTOF) patients. Radiomics analysis may reveal hidden information about cardiomyopathy in these patients. Goal(s): This study aimed to develop a radiomics-based classification model by native T1 mapping to identify rTOF patients with moderate-to-severe PR and severe PR. Approach: A total of 623 radiomic features were extracted from native T1 mapping. We used machine learning for feature selection to identify the best radiomic features that maximize the diagnostic value for classifying cardiac diseases. Results: Optimal performance was achieved in the proposed segmental mid-slice T1 mapping model. Impact: The segmental mid-slice radiomics of native T1 mapping showed better classification performance than conventional native T1 values in identifying rTOF patients with moderate-to-severe PR and severe PR. The discerned tissue characteristics offered additional physiopathological information beyond native T1 values.