Machine Learning to Automatically Differentiate Hypertrophic Cardiomyopathy, Cardiac Light Chain, and Cardiac Transthyretin Amyloidosis: A Multicenter CMR Study.
Authors
Affiliations (8)
Affiliations (8)
- Department of Cardiology, Angiology and Pneumology, Heidelberg University Hospital, Germany. (L.D.W., A.O., F.a.d.S., J.S., E.G., B.M., M.G.F., N.F., F.A.).
- German Centre for Cardiovascular Research (DZHK), Partner Site Heidelberg (L.D.W., A.O., F.a.d.S., J.S., E.G., B.M., N.F., F.A.).
- Area 19 Medical, Inc, Montreal, Canada (M.B., S.F.).
- Department of Haematology, Oncology and Rheumatology, Amyloidosis Center, Heidelberg University Hospital, Germany. (F.a.d.S., S.S.).
- Pfizer, Inc, Cambridge, MA (C.D., K.M., Y.Z.).
- Pfizer Pharma GmbH, Berlin, Germany (J.M.).
- Informatics for Life, Heidelberg, Germany (B.M.).
- Departments of Medicine and Diagnostic Radiology, McGill University Health Centre, Montreal, Canada (M.G.F.).
Abstract
Cardiac amyloidosis is associated with poor outcomes and is caused by the interstitial deposition of misfolded proteins, typically ATTR (transthyretin) or AL (light chains). Although specific therapies during early disease stages exist, the diagnosis is often only established at an advanced stage. Cardiovascular magnetic resonance (CMR) is the gold standard for imaging suspected myocardial disease. However, differentiating cardiac amyloidosis from hypertrophic cardiomyopathy may be challenging, and a reliable method for an image-based classification of amyloidosis subtypes is lacking. This study sought to investigate a CMR machine learning (ML) algorithm to identify and distinguish cardiac amyloidosis. This retrospective, multicenter, multivendor feasibility study included consecutive patients diagnosed with hypertrophic cardiomyopathy or AL/ATTR amyloidosis and healthy volunteers. Standard clinical information, semiautomated CMR imaging data, and qualitative CMR features were integrated into a trained ML algorithm. Four hundred participants (95 healthy, 94 hypertrophic cardiomyopathy, 95 AL, and 116 ATTR) from 56 institutions were included (269 men aged 58.5 [48.4-69.4] years). A 3-stage ML screening cascade sequentially differentiated healthy volunteers from patients, then hypertrophic cardiomyopathy from amyloidosis, and then AL from ATTR. The ML algorithm resulted in an accurate differentiation at each step (area under the curve, 1.0, 0.99, and 0.92, respectively). After reducing included data to demographics and imaging data alone, the performance remained excellent (area under the curve, 0.99, 0.98, and 0.88, respectively), even after removing late gadolinium enhancement imaging data from the model (area under the curve, 1.0, 0.95, 0.86, respectively). A trained ML model using semiautomated CMR imaging data and patient demographics can accurately identify cardiac amyloidosis and differentiate subtypes.