Machine learning phenotyping of scarred myocardium from cine in hypertrophic cardiomyopathy.

Autor:	Mancio J; Department of Medicine, Beth Israel Deaconess Medical Centre and Harvard Medical School, 330 Brookline Avenue, Boston, MA 02215, USA., Pashakhanloo F; Department of Medicine, Beth Israel Deaconess Medical Centre and Harvard Medical School, 330 Brookline Avenue, Boston, MA 02215, USA., El-Rewaidy H; Department of Medicine, Beth Israel Deaconess Medical Centre and Harvard Medical School, 330 Brookline Avenue, Boston, MA 02215, USA.; Department of Computer Science, Technical University of Munich, Arcisstraße 21, 80333 Munich, Germany., Jang J; Department of Medicine, Beth Israel Deaconess Medical Centre and Harvard Medical School, 330 Brookline Avenue, Boston, MA 02215, USA.; Department of Computer Science, Technical University of Munich, Arcisstraße 21, 80333 Munich, Germany., Joshi G; Department of Medicine, Beth Israel Deaconess Medical Centre and Harvard Medical School, 330 Brookline Avenue, Boston, MA 02215, USA., Csecs I; Department of Medicine, Beth Israel Deaconess Medical Centre and Harvard Medical School, 330 Brookline Avenue, Boston, MA 02215, USA., Ngo L; Department of Medicine, Beth Israel Deaconess Medical Centre and Harvard Medical School, 330 Brookline Avenue, Boston, MA 02215, USA.; Department of Biostatistics, Harvard T.H. Chan School of Public Health, 677 Huntington Ave, Boston, MA 02115, USA., Rowin E; HCM Institute, Division of Cardiology, Tufts Medical Centre, 860 Washington St Building, 6th Floor, Boston, MA 02111, USA., Manning W; Department of Medicine, Beth Israel Deaconess Medical Centre and Harvard Medical School, 330 Brookline Avenue, Boston, MA 02215, USA.; Department of Radiology, Beth Israel Deaconess Medical Centre and Harvard Medical School, 330 Brookline Avenue, Boston, MA 02215, USA., Maron M; HCM Institute, Division of Cardiology, Tufts Medical Centre, 860 Washington St Building, 6th Floor, Boston, MA 02111, USA., Nezafat R; Department of Medicine, Beth Israel Deaconess Medical Centre and Harvard Medical School, 330 Brookline Avenue, Boston, MA 02215, USA.
Jazyk:	angličtina
Zdroj:	European heart journal. Cardiovascular Imaging [Eur Heart J Cardiovasc Imaging] 2022 Mar 22; Vol. 23 (4), pp. 532-542.
DOI:	10.1093/ehjci/jeab056
Abstrakt:	Aims: Cardiovascular magnetic resonance (CMR) with late-gadolinium enhancement (LGE) is increasingly being used in hypertrophic cardiomyopathy (HCM) for diagnosis, risk stratification, and monitoring. However, recent data demonstrating brain gadolinium deposits have raised safety concerns. We developed and validated a machine-learning (ML) method that incorporates features extracted from cine to identify HCM patients without fibrosis in whom gadolinium can be avoided. Methods and Results: An XGBoost ML model was developed using regional wall thickness and thickening, and radiomic features of myocardial signal intensity, texture, size, and shape from cine. A CMR dataset containing 1099 HCM patients collected using 1.5T CMR scanners from different vendors and centres was used for model development (n=882) and validation (n=217). Among the 2613 radiomic features, we identified 7 features that provided best discrimination between +LGE and -LGE using 10-fold stratified cross-validation in the development cohort. Subsequently, an XGBoost model was developed using these radiomic features, regional wall thickness and thickening. In the independent validation cohort, the ML model yielded an area under the curve of 0.83 (95% CI: 0.77-0.89), sensitivity of 91%, specificity of 62%, F1-score of 77%, true negatives rate (TNR) of 34%, and negative predictive value (NPV) of 89%. Optimization for sensitivity provided sensitivity of 96%, F2-score of 83%, TNR of 19% and NPV of 91%; false negatives halved from 4% to 2%. Conclusion: An ML model incorporating novel radiomic markers of myocardium from cine can rule-out myocardial fibrosis in one-third of HCM patients referred for CMR reducing unnecessary gadolinium administration. (Published on behalf of the European Society of Cardiology. All rights reserved. © The Author(s) 2021. For permissions, please email: journals.permissions@oup.com.)
Databáze:	MEDLINE
Externí odkaz:	Zobrazit plný text záznamu