Radiomics and deep learning for myocardial scar screening in hypertrophic cardiomyopathy

作者：Ahmed S. Fahmy, Ethan J. Rowin, Arghavan Arafati, Talal Khalid Al-Otaibi, Martin S. Maron, Reza Nezafat · 发表于：Journal of Cardiovascular Magnetic Resonance · 年份：2022 · DOI：10.1186/s12968-022-00869-x · 被引用次数：51 · 研究领域：Cardiac Imaging and Diagnostics、Radiomics and Machine Learning in Medical Imaging、Cardiovascular Disease and Adiposity

BACKGROUND: Myocardial scar burden quantified using late gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR), has important prognostic value in hypertrophic cardiomyopathy (HCM). However, nearly 50% of HCM patients have no scar but undergo repeated gadolinium-based CMR over their life span. We sought to develop an artificial intelligence (AI)-based screening model using radiomics and deep learning (DL) features extracted from balanced steady state free precession (bSSFP) cine sequences to identify HCM patients without scar. METHODS: We evaluated three AI-based screening models using bSSFP cine image features extracted by radiomics, DL, or combined DL-Radiomics. Images for 759 HCM patients (50 ± 16 years, 66% men) in a multi-center/vendor study were used to develop and test model performance. An external dataset of 100 HCM patients (53 ± 14 years, 70% men) was used to assess model generalizability. Model performance was evaluated using area-under-receiver-operating curve (AUC). RESULTS: The DL-Radiomics model demonstrated higher AUC compared to DL and Radiomics in the internal (0.83 vs 0.77, p = 0.006 and 0.78, p = 0.05; n = 159) and external (0.74 vs 0.64, p = 0.006 and 0.71, p = 0.27; n = 100) datasets. The DL-Radiomics model correctly identified 43% and 28% of patients without scar in the internal and external datasets compared to 42% and 16% by Radiomics model and 42% and 23% by DL model, respectively. CONCLUSIONS: A DL-Radiomics AI model using bSSFP cine ...