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- Title
Radiomics and deep learning for myocardial scar screening in hypertrophic cardiomyopathy.
- Authors
Fahmy, Ahmed S.; Rowin, Ethan J.; Arafati, Arghavan; Al-Otaibi, Talal; Maron, Martin S.; Nezafat, Reza
- Abstract
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 images outperforms DL or Radiomics models alone as a scar screening tool prior to gadolinium administration. Despite its potential, the clinical utility of the model remains limited and further investigation is needed to improve the accuracy and generalizability.
- Subjects
DEEP learning; DIGITAL image processing; RESEARCH; CARDIAC hypertrophy; SCARS; CARDIOMYOPATHIES; MEDICAL screening; ARTIFICIAL intelligence; MAGNETIC resonance imaging; DESCRIPTIVE statistics; DISEASE complications
- Publication
Journal of Cardiovascular Magnetic Resonance (BioMed Central), 2022, Vol 24, Issue 1, p1
- ISSN
1532-429X
- Publication type
Article
- DOI
10.1186/s12968-022-00869-x