tetano
Editor, Senior Moderator
Heliyon
. 2021 Aug;7(8):e07743.
doi: 10.1016/j.heliyon.2021.e07743. Epub 2021 Aug 10.
Standardized reporting systems of chest computed tomography in a population with low coronavirus disease 2019 prevalence: A retrospective comparative study
Ryo Kurokawa[SUP] 1 [/SUP], Shohei Inui[SUP] 1 [/SUP], Wataru Gonoi[SUP] 1 [/SUP], Yudai Nakai[SUP] 1 [/SUP], Masanori Ishida[SUP] 1 [/SUP], Yusuke Watanabe[SUP] 1 [/SUP], Takatoshi Kubo[SUP] 1 [/SUP], Yosuke Amano[SUP] 2 [/SUP], Koh Okamoto[SUP] 3 [/SUP], Hidenori Kage[SUP] 2 [/SUP], Sohei Harada[SUP] 4 [/SUP], Goh Tanaka[SUP] 2 [/SUP], Takuya Kawahara[SUP] 5 [/SUP], Takahide Nagase[SUP] 2 [/SUP], Kyoji Moriya[SUP] 3 [/SUP], Osamu Abe[SUP] 1 [/SUP]
Affiliations
Abstract
Purpose: To compare the diagnostic performance and interobserver agreement of three reporting systems for computed tomography findings in coronavirus disease 2019 (COVID-19), namely the COVID-19 Reporting and Data System (CO-RADS), COVID-19 Imaging Reporting and Data System (COVID-RADS), and Radiological Society of North America (RSNA) expert consensus statement, in a low COVID-19 prevalence area.
Method: This institutional review board approval single-institutional retrospective study included 154 hospitalized patients between April 1 and May 21, 2020; 26 (16.9 %; 63.2 ± 14.1 years, 21 men) and 128 (65.7 ± 16.4 years, 87 men) patients were diagnosed with and without COVID-19 according to reverse transcription-polymerase chain reaction results, respectively. Written informed consent was waived due to the retrospective nature of the study. Six radiologists independently classified chest computed tomography images according to each reporting system. The area under receiver operating characteristic curves, sensitivity, specificity, positive predictive value, negative predictive value, accuracy, and interobserver agreements were calculated and compared across the systems using paired t-test and kappa analysis.
Results: Mean area under receiver operating characteristic curves were as follows: CO-RADS, 0.89 (95 % confidence interval [CI], 0.87-0.90); COVID-RADS, 0.78 (0.75-0.80); and RSNA expert consensus statement, 0.88 (0.86-0.90). Average kappa values across observers were 0.52 (95 % CI: 0.45-0.60), 0.51 (0.41-0.61), and 0.57 (0.49-0.64) for CO-RADS, COVID-RADS, and RSNA expert consensus statement, respectively. Sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were the highest at 0.71, 0.53, 0.72, 0.96, and 0.56 in the CO-RADS; 0.56, 0.31, 0.54, 0.95, and 0.35 in the COVID-RADS; 0.83, 0.49, 0.61, 0.96, and 0.55 in the RSNA expert consensus statement, respectively.
Conclusions: The CO-RADS exhibited the highest specificity, positive predictive value, which are especially important in a low-prevalence population, while maintaining high accuracy and negative predictive value, demonstrating the best performance in a low-prevalence population.
Keywords: COVID-19; Chest computed tomography; Diagnostic performance; Interobserver agreement.
. 2021 Aug;7(8):e07743.
doi: 10.1016/j.heliyon.2021.e07743. Epub 2021 Aug 10.
Standardized reporting systems of chest computed tomography in a population with low coronavirus disease 2019 prevalence: A retrospective comparative study
Ryo Kurokawa[SUP] 1 [/SUP], Shohei Inui[SUP] 1 [/SUP], Wataru Gonoi[SUP] 1 [/SUP], Yudai Nakai[SUP] 1 [/SUP], Masanori Ishida[SUP] 1 [/SUP], Yusuke Watanabe[SUP] 1 [/SUP], Takatoshi Kubo[SUP] 1 [/SUP], Yosuke Amano[SUP] 2 [/SUP], Koh Okamoto[SUP] 3 [/SUP], Hidenori Kage[SUP] 2 [/SUP], Sohei Harada[SUP] 4 [/SUP], Goh Tanaka[SUP] 2 [/SUP], Takuya Kawahara[SUP] 5 [/SUP], Takahide Nagase[SUP] 2 [/SUP], Kyoji Moriya[SUP] 3 [/SUP], Osamu Abe[SUP] 1 [/SUP]
Affiliations
- PMID: 34395930
- PMCID: PMC8353969
- DOI: 10.1016/j.heliyon.2021.e07743
Abstract
Purpose: To compare the diagnostic performance and interobserver agreement of three reporting systems for computed tomography findings in coronavirus disease 2019 (COVID-19), namely the COVID-19 Reporting and Data System (CO-RADS), COVID-19 Imaging Reporting and Data System (COVID-RADS), and Radiological Society of North America (RSNA) expert consensus statement, in a low COVID-19 prevalence area.
Method: This institutional review board approval single-institutional retrospective study included 154 hospitalized patients between April 1 and May 21, 2020; 26 (16.9 %; 63.2 ± 14.1 years, 21 men) and 128 (65.7 ± 16.4 years, 87 men) patients were diagnosed with and without COVID-19 according to reverse transcription-polymerase chain reaction results, respectively. Written informed consent was waived due to the retrospective nature of the study. Six radiologists independently classified chest computed tomography images according to each reporting system. The area under receiver operating characteristic curves, sensitivity, specificity, positive predictive value, negative predictive value, accuracy, and interobserver agreements were calculated and compared across the systems using paired t-test and kappa analysis.
Results: Mean area under receiver operating characteristic curves were as follows: CO-RADS, 0.89 (95 % confidence interval [CI], 0.87-0.90); COVID-RADS, 0.78 (0.75-0.80); and RSNA expert consensus statement, 0.88 (0.86-0.90). Average kappa values across observers were 0.52 (95 % CI: 0.45-0.60), 0.51 (0.41-0.61), and 0.57 (0.49-0.64) for CO-RADS, COVID-RADS, and RSNA expert consensus statement, respectively. Sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were the highest at 0.71, 0.53, 0.72, 0.96, and 0.56 in the CO-RADS; 0.56, 0.31, 0.54, 0.95, and 0.35 in the COVID-RADS; 0.83, 0.49, 0.61, 0.96, and 0.55 in the RSNA expert consensus statement, respectively.
Conclusions: The CO-RADS exhibited the highest specificity, positive predictive value, which are especially important in a low-prevalence population, while maintaining high accuracy and negative predictive value, demonstrating the best performance in a low-prevalence population.
Keywords: COVID-19; Chest computed tomography; Diagnostic performance; Interobserver agreement.