AI Summary & Key Takeaways
AI InsightCONCLUSION COVID-19 S CT classification system may meet the needs of radiologists in distinguishing COVID-19 from pneumonia of other etiologies and help optimize patient management and disease control in this pandemic by the use of structured reporting.
📌 3 Key Takeaways
Özet
PURPOSE Because of the widespread use of computed tomography (CT) in the diagnosis of COVID‑19, indeterminate presentations such as single, few or unilateral lesions amount to a considerable number. We aimed to develop a new classification and structured reporting system on CT imaging (COVID-19 S) that would facilitate the diagnosis of COVID-19 in the most accurate way. METHODS Our retrospective cohort included 803 patients with a chest CT scan upon suspicion of COVID‑19. The patients’ history, physical examination, CT findings, RT‑PCR, and other laboratory test results were reviewed, and a final diagnosis was made as COVID‑19 or non-COVID‑19. Chest CT scans were classified according to the COVID‑19 S CT diagnosis criteria. Cohen’s kappa analysis was used. RESULTS Final clinical diagnosis was COVID-19 in 98 patients (12%). According to the COVID-19 S CT diagnosis criteria, the number of patients in the normal, compatible with COVID‑19, indeterminate and alternative diagnosis groups were 581 (72.3%), 97 (12.1%), 16 (2.0%) and 109 (13.6%). When the indeterminate group was combined with the group compatible with COVID‑19, the sensitivity and specificity of COVID-19 S were 99.0% and 87.1%, with 85.8% positive predictive value (PPV) and 99.1% negative predictive value (NPV). When the indeterminate group was combined with the alternative diagnosis group, the sensitivity and specificity of COVID-19 S were 93.9% and 96.0%, with 94.8% PPV and 95.2% NPV. CONCLUSION COVID-19 S CT classification system may meet the needs of radiologists in distinguishing COVID-19 from pneumonia of other etiologies and help optimize patient management and disease control in this pandemic by the use of structured reporting.