Cardiac Computed Tomography for Quantification of Myocardial Extracellular Volume Fraction: A Systematic Review and Meta-Analysis

Donghee Han; Andrew Lin; Keiichiro Kuronuma; Heidi Gransar; Damini Dey; John D Friedman; Daniel S Berman; Balaji K Tamarappoo

doi:10.1016/j.jcmg.2023.03.021

Cardiac Computed Tomography for Quantification of Myocardial Extracellular Volume Fraction: A Systematic Review and Meta-Analysis

JACC Cardiovasc Imaging. 2023 Oct;16(10):1306-1317. doi: 10.1016/j.jcmg.2023.03.021. Epub 2023 May 31.

Authors

Donghee Han¹, Andrew Lin², Keiichiro Kuronuma¹, Heidi Gransar¹, Damini Dey², John D Friedman¹, Daniel S Berman³, Balaji K Tamarappoo⁴

Affiliations

¹ Department of Imaging and Medicine, Cedars-Sinai Medical Center, Los Angeles, California, USA.
² Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA.
³ Department of Imaging and Medicine, Cedars-Sinai Medical Center, Los Angeles, California, USA. Electronic address: Daniel.Berman@cshs.org.
⁴ Cardiovascular Institute, Indiana University School of Medicine, Indianapolis, Indiana, USA.

PMID: 37269267
DOI: 10.1016/j.jcmg.2023.03.021

Abstract

Background: Extracellular volume (ECV) is a quantitative measure of extracellular compartment expansion, and an increase in ECV is a marker of myocardial fibrosis. Although cardiac magnetic resonance (CMR) is considered the standard imaging tool for ECV quantification, cardiac computed tomography (CT) has also been used for ECV assessment.

Objectives: The aim of this meta-analysis was to evaluate the correlation and agreement in the quantification of myocardial ECV by CT and CMR.

Methods: PubMed and Web of Science were searched for relevant publications reporting on the use of CT for ECV quantification compared with CMR as the reference standard. The authors employed a meta-analysis using the restricted maximum-likelihood estimator with a random-effects method to estimate summary correlation and mean difference. A subgroup analysis was performed to compare the correlation and mean differences between single-energy CT (SECT) and dual-energy CT (DECT) techniques for the ECV quantification.

Results: Of 435 papers, 13 studies comprising 383 patients were identified. The mean age range was 57.3 to 82 years, and 65% of patients were male. Overall, there was an excellent correlation between CT-derived ECV and CMR-derived ECV (mean: 0.90 [95% CI: 0.86-0.95]). The pooled mean difference between CT and CMR was 0.96% (95% CI: 0.14%-1.78%). Seven studies reported correlation values using SECT, and 4 studies reported those using DECT. The pooled correlation from studies utilizing DECT for ECV quantification was significantly higher compared with those with SECT (mean: 0.94 [95% CI: 0.91-0.98] vs 0.87 [95% CI: 0.80-0.94], respectively; P = 0.01). There was no significant difference in pooled mean differences between SECT vs DECT (P = 0.85).

Conclusions: CT-derived ECV showed an excellent correlation and mean difference of <1% with CMR-derived ECV. However, the overall quality of the included studies was low, and larger, prospective studies are needed to examine the accuracy and diagnostic and prognostic utility of CT-derived ECV.

Keywords: cardiac magnetic resonance; computed tomography; extracellular volume; meta-analysis.

Publication types

Meta-Analysis
Systematic Review
Research Support, Non-U.S. Gov't

MeSH terms

Aged
Aged, 80 and over
Cardiomyopathies* / pathology
Contrast Media
Female
Fibrosis
Heart
Humans
Magnetic Resonance Imaging
Male
Middle Aged
Myocardium* / pathology
Predictive Value of Tests

Substances

Contrast Media