Diagnostic performance of Radiological Society of North America structured reporting language for chest computed tomography findings in patients with COVID-19
Open accessAbstract
Purpose To evaluate the diagnostic performance of the RSNA structured reporting language for chest CT findings in patients with COVID-19.Material and methods Patients with suspected COVID-19 who underwent chest CT and RT-PCR tests were enrolled consecutively in this retrospective study, regardless of symptoms. Imaging findings were categorized as "typical", "indeterminate", "atypical", or "negative" according to RSNA reporting language and compared to RT-PCR. "Single, round GGO" and "single, peripheral GGO," do not fit the reporting language, were also analyzed as "indeterminate" patterns.Results Of the 1186 patients included in the analysis, the diagnosis of COVID-19 was confirmed in 388 patients. Of the 388 patients, CT findings were categorized as "typical" in 248, "indeterminate" in 77, and "negative" in 63. The sensitivity, specificity, and accuracy of "typical" findings were 63.9, 99.0, and 87.5% for COVID-19, respectively. In addition to the "typical" findings, the highest diagnostic accuracy of 92.2% was achieved when the "single, peripheral GGO" and "single, round GGO" were considered to be CT-positive.Conclusion The RSNA reporting language has significant diagnostic performance for identifying COVID-19 pneumonia. CT findings that do not exactly fit the RSNA reporting language, such as "single, round GGO" and "single, peripheral GGO" improve diagnostic performance.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The COVID-19 pandemic increased the number of COVID-19-related studies from Japan, with a significant increase in such research focusing on AI.
“Furthermore, owing to the coronavirus disease 2019 (COVID-19) pandemic, the number of COVID-19-related studies from Japan has increased [54–61], with a significant increase in such research focusing on AI [62–64].”
From Fairness of artificial intelligence in healthcare: review and recommendations (2024) · cites this paper as [58]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China
Huang, C., Wang, Y., Li, X., Ren, L., Zhao, J., Hu, Y., Zhang, L., Fan, G., Xu, J., Gu, X., et al. (2020) · The Lancet · doi:10.1016/s0140-6736(20)30183-5 · Cited by 48,884 · see citing works
Early transmission dynamics in Wuhan, China, of novel coronavirus-infected pneumonia
Li, Q., Guan, X., Wu, P., Wang, X., Zhou, L., Tong, Y., Ren, R., Leung, K. S. M., Lau, E. H. Y., Wong, J. Y., et al. (2020) · New England Journal of Medicine · doi:10.1056/nejmoa2001316 · Cited by 16,575 · see citing works
Gorbalenya, A. E., Baker, S. C., Baric, R. S., de Groot, R. J., Drosten, C., Gulyaeva, A. A., Haagmans, B. L., Lauber, C., Leontovich, A. M., Neuman, B. W., et al. (2020) · Nature Microbiology · doi:10.1038/s41564-020-0695-z · Cited by 8,436 · see citing works
Detection of 2019 novel coronavirus (2019-nCoV) by real-time RT-PCR
Corman, V. M., Landt, O., Kaiser, M., Molenkamp, R., Meijer, A., Chu, D. K. W., Bleicker, T., Brünink, S., Schneider, J. A., Schmidt, M. L., et al. (2020) · Eurosurveillance · doi:10.2807/1560-7917.es.2020.25.3.2000045 · Cited by 7,266 · see citing works
Ai, T., Yang, Z., Hou, H., Zhan, C., Chen, C., Lv, W., Tao, Q., Sun, Z., & Xia, L. (2020) · Radiology · doi:10.1148/radiol.2020200642 · Cited by 5,697 · see citing works
SARS-CoV-2 Viral Load in Upper Respiratory Specimens of Infected Patients
Zou, L., Ruan, F., Huang, M.-X., Liang, L.-J., Huang, H., Hong, Z.-S., Yu, J.-X., Kang, M., Song, Y.-C., Xia, J., et al. (2020) · New England Journal of Medicine · doi:10.1056/nejmc2001737 · Cited by 4,590 · see citing works
Fleischner Society: Glossary of Terms for Thoracic Imaging
Hansell, D. M., Bankier, A. A., MacMahon, H., McLoud, T. C., Müller, N. L., & Rémy, J. (2008) · Radiology · doi:10.1148/radiol.2462070712 · Cited by 4,189 · see citing works
Sensitivity of Chest CT for COVID-19: Comparison to RT-PCR
Fang, Y., Zhang, H., Xie, J., Lin, M., Ying, L., Pang, P., & Ji, W. (2020) · Radiology · doi:10.1148/radiol.2020200432 · Cited by 3,340 · see citing works
Time Course of Lung Changes at Chest CT during Recovery from Coronavirus Disease 2019 (COVID-19)
Pan, F., Ye, T., Sun, P., Gui, S., Liang, B., Li, L., Zheng, D., Wang, J., Hesketh, R. L., Yang, L., et al. (2020) · Radiology · doi:10.1148/radiol.2020200370 · Cited by 2,862 · see citing works
Chest CT Findings in Coronavirus Disease-19 (COVID-19): Relationship to Duration of Infection
Bernheim, A., Mei, X., Huang, M., Yang, Y., Fayad, Z. A., Zhang, N., Diao, K., Lin, B., Zhu, X., Li, K., et al. (2020) · Radiology · doi:10.1148/radiol.2020200463 · Cited by 2,658 · see citing works
Epidemiology of Covid-19 in a Long-Term Care Facility in King County, Washington
McMichael, T. M., Currie, D. W., Clark, S., Pogosjans, S., Kay, M. K., Schwartz, N. G., Lewis, J., Baer, A., Kawakami, V., Lukoff, M. D., et al. (2020) · New England Journal of Medicine · doi:10.1056/nejmoa2005412 · Cited by 1,322 · see citing works
Diagnosis of the Coronavirus disease (COVID-19): rRT-PCR or CT?
Long, C.-Q., Xu, H., Shen, Q., Zhang, X., Fan, B., Wang, C., Zeng, B., Li, Z., Li, X., & Li, H. (2020) · European Journal of Radiology · doi:10.1016/j.ejrad.2020.108961 · Cited by 964 · see citing works
Prokop, M., van Everdingen, W. M., Vellinga, T. V. R., van Ufford, H. M. E. Q., Stöger, J. L., Beenen, L. F. M., Geurts, B. H. J., Gietema, H. A., Krdzalic, J., Schaefer‐Prokop, C. M., et al. (2020) · Radiology · doi:10.1148/radiol.2020201473 · Cited by 922 · see citing works
Simpson, S. A., Kay, F. U., Abbara, S., Bhalla, S., Chung, J. H., Chung, M., Henry, T. S., Kanne, J. P., Kligerman, S. J., Ko, J. P., et al. (2020) · Radiology Cardiothoracic Imaging · doi:10.1148/ryct.2020200152 · Cited by 764 · see citing works
Kim, H., Hong, H., & Yoon, S. H. (2020) · Radiology · doi:10.1148/radiol.2020201343 · Cited by 534 · see citing works
Casto, A. M., Huang, M.-L., Nalla, A., et al. (2020) · Journal of Clinical Microbiology · doi:10.1128/jcm.00557-20 · Cited by 439 · see citing works
Chest CT Findings in Cases from the Cruise Ship Diamond Princess with Coronavirus Disease (COVID-19)
Inui, S., Fujikawa, A., Jitsu, M., Kunishima, N., Watanabe, S., Suzuki, Y., Umeda, S., & Uwabe, Y. (2020) · Radiology Cardiothoracic Imaging · doi:10.1148/ryct.2020200110 · Cited by 438 · see citing works
Early Clinical and CT Manifestations of Coronavirus Disease 2019 (COVID-19) Pneumonia
Han, R., Huang, L., Jiang, H. X., Dong, J., Peng, H., & Zhang, D. (2020) · American Journal of Roentgenology · doi:10.2214/ajr.20.22961 · Cited by 389 · see citing works
Chest CT and Coronavirus Disease (COVID-19): A Critical Review of the Literature to Date
Raptis, C. A., Hammer, M. M., Short, R. G., Shah, A. B., Bhalla, S., Bierhals, A. J., Filev, P. D., Hope, M. D., Jeudy, J. J., Kligerman, S. J., et al. (2020) · American Journal of Roentgenology · doi:10.2214/ajr.20.23202 · Cited by 214 · see citing works
Salehi, S., Abedi, A., Balakrishnan, S., & Gholamrezanezhad, A. (2020) · European Radiology · doi:10.1007/s00330-020-06863-0 · Cited by 168 · see citing works
Use of Chest Imaging in the Diagnosis and Management of COVID-19: A WHO Rapid Advice Guide
Akl, E. A., Blazic, I. M., Yaacoub, S. G., Frija, G. G., Chou, R., Appiah, J. A., Fatehi, M., Flor, N., Hitti, E. A., Jafri, H., et al. (2020) · Radiology · doi:10.1148/radiol.2020203173 · Cited by 162 · see citing works
Waller, J. V., Kaur, P., Tucker, A., Lin, K. K., Diaz, M. J., Henry, T. S., & Hope, M. D. (2020) · American Journal of Roentgenology · doi:10.2214/ajr.20.23418 · Cited by 138 · see citing works
Chen, Z., Fan, H., Cai, J., Li, Y., Wu, B., Hou, Y., Xu, S., Zhou, F., Liu, Y., Xuan, W., et al. (2020) · European Journal of Radiology · doi:10.1016/j.ejrad.2020.108972 · Cited by 130 · see citing works
Long, D. R., Gombar, S., Hogan, C. A., Greninger, A. L., O’Reilly-Shah, V. N., Bryson‐Cahn, C., Stevens, B., Rustagi, A., Jerome, K. R., Kong, C. S., et al. (2020) · Clinical Infectious Diseases · doi:10.1093/cid/ciaa722 · Cited by 125 · see citing works
Smet, K. D., Smet, D. D., Demedts, I., Bouckaert, B., Ryckaert, T., Laridon, E., Heremans, B., Vandenbulcke, R., Gryspeerdt, S. S., & Martens, G. A. (2020) · Radiology · doi:10.1148/radiol.2020202708 · Cited by 111 · see citing works
Himoto, Y., Sakata, A., Kirita, M., Hiroi, T., Kobayashi, K., Kubo, K., Kim, H., Nishimoto, A., Maeda, C., Kawamura, A., et al. (2020) · Japanese Journal of Radiology · doi:10.1007/s11604-020-00958-w · Cited by 81 · see citing works
Radiological approach to COVID-19 pneumonia with an emphasis on chest CT
Güneyli, S., Atçeken, Z., Doğan, H., Altınmakas, E., & Atasoy, K. C. (2020) · Diagnostic and Interventional Radiology · doi:10.5152/dir.2020.20260 · Cited by 76 · see citing works
Falaschi, Z., Danna, P. S. C., Arioli, R., Paschè, A., Zagaria, D., Percivale, I., Tricca, S., Barini, M., Aquilini, F., Andreoni, S., et al. (2020) · European Journal of Radiology · doi:10.1016/j.ejrad.2020.109192 · Cited by 74 · see citing works
Occurrence and Timing of Subsequent SARS-CoV-2 RT-PCR Positivity Among Initially Negative PatientsPreprint
Long, D. R., Gombar, S., Hogan, C. A., et al. (2020) · medRxiv · doi:10.1101/2020.05.03.20089151 · Cited by 51 · see citing works
Gezer, N. S., Ergan, B., BARIŞ, M. M., Appak, Ö., Sayıner, A. A., Balci, P., Kuruüzüm, Z., Çavuş, S. A., & Kılınç, O. (2020) · Diagnostic and Interventional Radiology · doi:10.5152/dir.2020.20351 · Cited by 48 · see citing works
Ciccarese, F., Coppola, F., Spinelli, D., Galletta, G. L., Lucidi, V., Paccapelo, A., Benedittis, C. D., Balacchi, C., & Golfieri, R. (2020) · Radiology Cardiothoracic Imaging · doi:10.1148/ryct.2020200312 · Cited by 39 · see citing works
Clinical and CT findings of COVID-19: differences among three age groups
Wang, J., Zhu, X., Xu, Z., Yang, G., Mao, G., Jia, Y., Xie, Z., Wang, J., & Ao, W. (2020) · BMC Infectious Diseases · doi:10.1186/s12879-020-05154-9 · Cited by 37 · see citing works
Som, A., Lang, M., Yeung, T., Carey, D. E., Garrana, S. H., Mendoza, D. P., Flores, E. J., Li, M., Sharma, A., McDermott, S., et al. (2020) · Radiology Cardiothoracic Imaging · doi:10.1148/ryct.2020200276 · Cited by 32 · see citing works
Comparison of Chest CT Grading Systems in COVID-19 Pneumonia
Inui, S., Kurokawa, R., Nakai, Y., Watanabe, Y., Kurokawa, M., Sakurai, K., Fujikawa, A., Sugiura, H., Kawahara, T., Yoon, S. H., et al. (2020) · Radiology Cardiothoracic Imaging · doi:10.1148/ryct.2020200492 · Cited by 32 · see citing works
Castelli, M., Maurin, A., Bartoli, A., Dassa, M., Marchi, B., Finance, J., Lagier, J., Million, M., Parola, P., Brouqui, P., et al. (2020) · Insights into Imaging · doi:10.1186/s13244-020-00939-7 · Cited by 14 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Fairness of artificial intelligence in healthcare: review and recommendations
Ueda, D., Kakinuma, T., Fujita, S., Kamagata, K., Fushimi, Y., Ito, R., Matsui, Y., Nozaki, T., Nakaura, T., Fujima, N., et al. (2024) · Japanese Journal of Radiology · doi:10.1007/s11604-023-01474-3 · Cited by 607 · see citing works
- Authors
- Özer, H., Kılınçer, A., Uysal, E., Yormaz, B., Cebeci, H., Durmaz, M. S., & Koplay, M.
- Affiliations
- Selçuk University (TR)
- Corresponding author
- Özer, Halil
- ORCID
- Venue
- Japanese Journal of Radiology
- Published
- 2021-05-06
- Volume
- 39
- Issue
- 9
- Pages
- 877-888
- ISSN
- 1867-1071 (ISSN-L)
- 1867-108X
- PMC
- PMC8101342
- OpenAlex
- W3158174368
- Cited by
- 7
- References
- 35
- Citations by year
- 2026: 2 · 2023: 3 · 2022: 1 · 2021: 1
- FWCI
- 0.40
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Japanese Journal of Radiology (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Infectious Diseases › COVID-19 Clinical Research Studies
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › COVID-19 diagnosis using AI
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Keywords
- COVID-19 pandemic, chest CT, diagnostic accuracy, ground-glass opacities, RT-PCR validation, COVID-19 pneumonia, indeterminate results
- MeSH terms
- COVID-19, SARS-CoV-2, Humans, Language, Lung, North America, Retrospective Studies, Tomography, X-Ray Computed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
