Cine-cardiac magnetic resonance to distinguish between ischemic and non-ischemic cardiomyopathies: a machine learning approach

Riccardo Cau; Francesco Pisu; Alessandra Pintus; Vitanio Palmisano; Roberta Montisci; Jasjit S Suri; Rodrigo Salgado; Luca Saba

doi:10.1007/s00330-024-10640-8

Cine-cardiac magnetic resonance to distinguish between ischemic and non-ischemic cardiomyopathies: a machine learning approach

Eur Radiol. 2024 Mar 7. doi: 10.1007/s00330-024-10640-8. Online ahead of print.

Authors

Riccardo Cau¹, Francesco Pisu¹, Alessandra Pintus¹, Vitanio Palmisano², Roberta Montisci³, Jasjit S Suri⁴, Rodrigo Salgado⁵, Luca Saba⁶

Affiliations

¹ Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), di Cagliari - Polo di Monserrato s.s. 554 Monserrato, 09045, Cagliari, Italy.
² Ospedale General Regionale F. Miulli, Acquaviva Delle Fonti, Italy.
³ Department of Cardiology, Azienda Ospedaliero Universitaria (A.O.U.), di Cagliari - Polo di Monserrato s.s. 554 Monserrato, 09045, Cagliari, Italy.
⁴ Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, USA.
⁵ Universitair Ziekenhuis Antwerpen, Edegem, Belgium.
⁶ Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), di Cagliari - Polo di Monserrato s.s. 554 Monserrato, 09045, Cagliari, Italy. lucasaba@tiscali.it.

PMID: 38451322
DOI: 10.1007/s00330-024-10640-8

Abstract

Objective: This work aimed to derive a machine learning (ML) model for the differentiation between ischemic cardiomyopathy (ICM) and non-ischemic cardiomyopathy (NICM) on non-contrast cardiovascular magnetic resonance (CMR).

Methods: This retrospective study evaluated CMR scans of 107 consecutive patients (49 ICM, 58 NICM), including atrial and ventricular strain parameters. We used these data to compare an explainable tree-based gradient boosting additive model with four traditional ML models for the differentiation of ICM and NICM. The models were trained and internally validated with repeated cross-validation according to discrimination and calibration. Furthermore, we examined important variables for distinguishing between ICM and NICM.

Results: A total of 107 patients and 38 variables were available for the analysis. Of those, 49 were ICM (34 males, mean age 60 ± 9 years) and 58 patients were NICM (38 males, mean age 56 ± 19 years). After 10 repetitions of the tenfold cross-validation, the proposed model achieved the highest area under curve (0.82, 95% CI [0.47-1.00]) and lowest Brier score (0.19, 95% CI [0.13-0.27]), showing competitive diagnostic accuracy and calibration. At the Youden's index, sensitivity was 0.72 (95% CI [0.68-0.76]), the highest of all. Analysis of predictions revealed that both atrial and ventricular strain CMR parameters were important for the identification of ICM patients.

Conclusion: The current study demonstrated that using a ML model, multi chamber myocardial strain, and function on non-contrast CMR parameters enables the discrimination between ICM and NICM with competitive diagnostic accuracy.

Clinical relevance statement: A machine learning model based on non-contrast cardiovascular magnetic resonance parameters may discriminate between ischemic and non-ischemic cardiomyopathy enabling wider access to cardiovascular magnetic resonance examinations with lower costs and faster imaging acquisition.

Key points: • The exponential growth in cardiovascular magnetic resonance examinations may require faster and more cost-effective protocols. • Artificial intelligence models can be utilized to distinguish between ischemic and non-ischemic etiologies. • Machine learning using non-contrast CMR parameters can effectively distinguish between ischemic and non-ischemic cardiomyopathies.

Keywords: Artificial intelligence; Cardiomyopathy; Cardiovascular diseases; Cine magnetic resonance imaging; Machine learning.