Diagnostic performance of CO-RADS for COVID-19: a systematic review and meta-analysis

To investigate the diagnostic performance of the coronavirus disease 2019 (COVID-19) Reporting and Data System (CO-RADS) for detecting COVID-19.

We searched PubMed, EMBASE, MEDLINE, Web of Science, Cochrane Library, and Scopus database until September 21, 2021. Statistical analysis included data pooling, forest plot construction, heterogeneity testing, meta-regression, and subgroup analyses.

We included 24 studies with 8382 patients. The pooled sensitivity and specificity and the area under the curve (AUC) of CO-RADS ≥ 3 for detecting COVID-19 were 0.89 (95% confidence interval (CI) 0.85–0.93), 0.68 (95% CI 0.60–0.75), and 0.87 (95% CI 0.84–0.90), respectively. The pooled sensitivity and specificity and AUC of CO-RADS ≥ 4 were 0.83 (95% CI 0.79–0.87), 0.84 (95% CI 0.78–0.88), and 0.90 (95% CI 0.87–0.92), respectively. Cochran’s Q test (p < 0.01) and Higgins I² heterogeneity index revealed considerable heterogeneity. Studies with both symptomatic and asymptomatic patients had higher specificity than those with only symptomatic patients using CO-RADS ≥ 3 and CO-RADS ≥ 4. Using CO-RADS ≥ 4, studies with participants aged < 60 years had higher sensitivity (0.88 vs. 0.80, p = 0.02) and lower specificity (0.77 vs. 0.87, p = 0.01) than studies with participants aged > 60 years.

CO-RADS has favorable performance in detecting COVID-19. CO-RADS ≥ 3/4 might be applied as cutoff values given their high sensitivity and specificity. However, there is a need for more well-designed studies on CO-RADS.