Comparison of chest CT severity scoring systems for COVID-19
Purpose To compare the diagnostic performance and inter-observer agreement of five different CT chest severity scoring systems for COVID-19 to find the most precise one with the least interpretation time. Methods and materials This retrospective study included 85 patients (54 male and 31 female) wit...
Gespeichert in:
| Veröffentlicht in: | European radiology 2022-05, Vol.32 (5), p.3501-3512 |
|---|---|
| Hauptverfasser: | , , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 3512 |
|---|---|
| container_issue | 5 |
| container_start_page | 3501 |
| container_title | European radiology |
| container_volume | 32 |
| creator | Elmokadem, Ali H. Mounir, Ahmad M. Ramadan, Zainab A. Elsedeiq, Mahmoud Saleh, Gehad A. |
| description | Purpose
To compare the diagnostic performance and inter-observer agreement of five different CT chest severity scoring systems for COVID-19 to find the most precise one with the least interpretation time.
Methods and materials
This retrospective study included 85 patients (54 male and 31 female) with PCR-confirmed COVID-19. They underwent CT to assess the severity of pulmonary involvement. Three readers were asked to assess the pulmonary abnormalities and score the severity using five different systems, including chest CT severity score (CT-SS), chest CT score, total severity score (TSS), modified total severity score (m-TSS), and 3-level chest CT severity score. Time consumption on reporting of each system was calculated.
Results
Two hundred fifty-five observations were reported for each system. There was a statistically significant inter-observer agreement in assessing qualitative lung involvement using the m-TSS and the other four quantitative systems. The ROC curves revealed excellent and very good diagnostic accuracy for all systems when cutoff values for detection severe cases were > 22, > 17, > 12, and > 26 for CT-SS, chest CT score, TSS, and 3-level CT severity score. The AUC was very good (0.86), excellent (0.90), very good (0.89), and very good (0.86), respectively. Chest CT score showed the highest specificity (95.2%) in discrimination of severe cases. Time consumption on reporting was significantly different ( 3L-CT-SS > chest CT score > TSS.
Conclusion
All chest CT severity scoring systems in this study demonstrated excellent inter-observer agreement and reasonable performance to assess COVID-19 in relation to the clinical severity. CT-SS and TSS had the highest specificity and least time for interpretation.
Key Points
• All chest CT severity scoring systems discussed in this study revealed excellent inter-observer agreement and reasonable performance to assess COVID-19 in relation to the clinical severity.
• Chest CT scoring system and TSS had the highest specificity.
• Both TSS and m-TSS consumed the least time compared to the other three scoring systems. |
| doi_str_mv | 10.1007/s00330-021-08432-5 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_pubme</sourceid><recordid>TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_8760133</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2654822482</sourcerecordid><originalsourceid>FETCH-LOGICAL-c404t-1b90a304fb8e4e40fa5d1db7c823249605739eb04c648796e16cac047820f85d3</originalsourceid><addsrcrecordid>eNp9kV1LwzAUhoMobk7_gBdS8Mab6slH2xREkPoJg91Mb0OapVtH29SkG-zfm7k5pxdehJyQ57wnb16EzjFcY4DkxgFQCiEQHAJnlITRAerjdYH9-XCv7qET5-YAkGKWHKMejYBiznAf3WambqUtnWkCUwRqpl0XZOPA6aW2ZbcKnDK2bKaBW7lO1y4ojA2y0fvrQ4jTU3RUyMrps-0-QG9Pj-PsJRyOnl-z-2GoGLAuxHkKkgIrcq6ZZlDIaIIneaI4oYSlMUQJTXUOTMWMJ2mscaykApZwAgWPJnSA7ja67SKv9UTpprOyEq0ta2lXwshS_L5pypmYmqXgSQyYUi9wtRWw5mPhLYq6dEpXlWy0WThBYgLAecwTj17-QedmYRtvz1MR44T45SmyoZQ1zlld7B6DQazDEZtwhA9HfIUjIt90sW9j1_KdhgfoBnDt-s-1_Zn9j-wnCQ6YCQ</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2654822482</pqid></control><display><type>article</type><title>Comparison of chest CT severity scoring systems for COVID-19</title><source>MEDLINE</source><source>SpringerNature Journals</source><creator>Elmokadem, Ali H. ; Mounir, Ahmad M. ; Ramadan, Zainab A. ; Elsedeiq, Mahmoud ; Saleh, Gehad A.</creator><creatorcontrib>Elmokadem, Ali H. ; Mounir, Ahmad M. ; Ramadan, Zainab A. ; Elsedeiq, Mahmoud ; Saleh, Gehad A.</creatorcontrib><description>Purpose
To compare the diagnostic performance and inter-observer agreement of five different CT chest severity scoring systems for COVID-19 to find the most precise one with the least interpretation time.
Methods and materials
This retrospective study included 85 patients (54 male and 31 female) with PCR-confirmed COVID-19. They underwent CT to assess the severity of pulmonary involvement. Three readers were asked to assess the pulmonary abnormalities and score the severity using five different systems, including chest CT severity score (CT-SS), chest CT score, total severity score (TSS), modified total severity score (m-TSS), and 3-level chest CT severity score. Time consumption on reporting of each system was calculated.
Results
Two hundred fifty-five observations were reported for each system. There was a statistically significant inter-observer agreement in assessing qualitative lung involvement using the m-TSS and the other four quantitative systems. The ROC curves revealed excellent and very good diagnostic accuracy for all systems when cutoff values for detection severe cases were > 22, > 17, > 12, and > 26 for CT-SS, chest CT score, TSS, and 3-level CT severity score. The AUC was very good (0.86), excellent (0.90), very good (0.89), and very good (0.86), respectively. Chest CT score showed the highest specificity (95.2%) in discrimination of severe cases. Time consumption on reporting was significantly different (< 0.001): CT-SS > 3L-CT-SS > chest CT score > TSS.
Conclusion
All chest CT severity scoring systems in this study demonstrated excellent inter-observer agreement and reasonable performance to assess COVID-19 in relation to the clinical severity. CT-SS and TSS had the highest specificity and least time for interpretation.
Key Points
• All chest CT severity scoring systems discussed in this study revealed excellent inter-observer agreement and reasonable performance to assess COVID-19 in relation to the clinical severity.
• Chest CT scoring system and TSS had the highest specificity.
• Both TSS and m-TSS consumed the least time compared to the other three scoring systems.</description><identifier>ISSN: 1432-1084</identifier><identifier>ISSN: 0938-7994</identifier><identifier>EISSN: 1432-1084</identifier><identifier>DOI: 10.1007/s00330-021-08432-5</identifier><identifier>PMID: 35031841</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>Abnormalities ; Agreements ; Chest ; Coronaviruses ; COVID-19 ; Diagnostic Radiology ; Diagnostic systems ; Female ; Humans ; Imaging ; Internal Medicine ; Interventional Radiology ; Lung - diagnostic imaging ; Male ; Medicine ; Medicine & Public Health ; Neuroradiology ; Qualitative analysis ; Radiology ; Retrospective Studies ; SARS-CoV-2 ; Statistical analysis ; Thorax ; Tomography, X-Ray Computed - methods ; Ultrasound</subject><ispartof>European radiology, 2022-05, Vol.32 (5), p.3501-3512</ispartof><rights>The Author(s), under exclusive licence to European Society of Radiology 2021</rights><rights>2021. The Author(s), under exclusive licence to European Society of Radiology.</rights><rights>The Author(s), under exclusive licence to European Society of Radiology 2021.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c404t-1b90a304fb8e4e40fa5d1db7c823249605739eb04c648796e16cac047820f85d3</citedby><cites>FETCH-LOGICAL-c404t-1b90a304fb8e4e40fa5d1db7c823249605739eb04c648796e16cac047820f85d3</cites><orcidid>0000-0001-5119-9548 ; 0000-0001-6462-8778 ; 0000-0001-7851-689X ; 0000-0002-4817-4478 ; 0000-0002-3322-7960</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s00330-021-08432-5$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s00330-021-08432-5$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>230,315,782,786,887,27931,27932,41495,42564,51326</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/35031841$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Elmokadem, Ali H.</creatorcontrib><creatorcontrib>Mounir, Ahmad M.</creatorcontrib><creatorcontrib>Ramadan, Zainab A.</creatorcontrib><creatorcontrib>Elsedeiq, Mahmoud</creatorcontrib><creatorcontrib>Saleh, Gehad A.</creatorcontrib><title>Comparison of chest CT severity scoring systems for COVID-19</title><title>European radiology</title><addtitle>Eur Radiol</addtitle><addtitle>Eur Radiol</addtitle><description>Purpose
To compare the diagnostic performance and inter-observer agreement of five different CT chest severity scoring systems for COVID-19 to find the most precise one with the least interpretation time.
Methods and materials
This retrospective study included 85 patients (54 male and 31 female) with PCR-confirmed COVID-19. They underwent CT to assess the severity of pulmonary involvement. Three readers were asked to assess the pulmonary abnormalities and score the severity using five different systems, including chest CT severity score (CT-SS), chest CT score, total severity score (TSS), modified total severity score (m-TSS), and 3-level chest CT severity score. Time consumption on reporting of each system was calculated.
Results
Two hundred fifty-five observations were reported for each system. There was a statistically significant inter-observer agreement in assessing qualitative lung involvement using the m-TSS and the other four quantitative systems. The ROC curves revealed excellent and very good diagnostic accuracy for all systems when cutoff values for detection severe cases were > 22, > 17, > 12, and > 26 for CT-SS, chest CT score, TSS, and 3-level CT severity score. The AUC was very good (0.86), excellent (0.90), very good (0.89), and very good (0.86), respectively. Chest CT score showed the highest specificity (95.2%) in discrimination of severe cases. Time consumption on reporting was significantly different (< 0.001): CT-SS > 3L-CT-SS > chest CT score > TSS.
Conclusion
All chest CT severity scoring systems in this study demonstrated excellent inter-observer agreement and reasonable performance to assess COVID-19 in relation to the clinical severity. CT-SS and TSS had the highest specificity and least time for interpretation.
Key Points
• All chest CT severity scoring systems discussed in this study revealed excellent inter-observer agreement and reasonable performance to assess COVID-19 in relation to the clinical severity.
• Chest CT scoring system and TSS had the highest specificity.
• Both TSS and m-TSS consumed the least time compared to the other three scoring systems.</description><subject>Abnormalities</subject><subject>Agreements</subject><subject>Chest</subject><subject>Coronaviruses</subject><subject>COVID-19</subject><subject>Diagnostic Radiology</subject><subject>Diagnostic systems</subject><subject>Female</subject><subject>Humans</subject><subject>Imaging</subject><subject>Internal Medicine</subject><subject>Interventional Radiology</subject><subject>Lung - diagnostic imaging</subject><subject>Male</subject><subject>Medicine</subject><subject>Medicine & Public Health</subject><subject>Neuroradiology</subject><subject>Qualitative analysis</subject><subject>Radiology</subject><subject>Retrospective Studies</subject><subject>SARS-CoV-2</subject><subject>Statistical analysis</subject><subject>Thorax</subject><subject>Tomography, X-Ray Computed - methods</subject><subject>Ultrasound</subject><issn>1432-1084</issn><issn>0938-7994</issn><issn>1432-1084</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNp9kV1LwzAUhoMobk7_gBdS8Mab6slH2xREkPoJg91Mb0OapVtH29SkG-zfm7k5pxdehJyQ57wnb16EzjFcY4DkxgFQCiEQHAJnlITRAerjdYH9-XCv7qET5-YAkGKWHKMejYBiznAf3WambqUtnWkCUwRqpl0XZOPA6aW2ZbcKnDK2bKaBW7lO1y4ojA2y0fvrQ4jTU3RUyMrps-0-QG9Pj-PsJRyOnl-z-2GoGLAuxHkKkgIrcq6ZZlDIaIIneaI4oYSlMUQJTXUOTMWMJ2mscaykApZwAgWPJnSA7ja67SKv9UTpprOyEq0ta2lXwshS_L5pypmYmqXgSQyYUi9wtRWw5mPhLYq6dEpXlWy0WThBYgLAecwTj17-QedmYRtvz1MR44T45SmyoZQ1zlld7B6DQazDEZtwhA9HfIUjIt90sW9j1_KdhgfoBnDt-s-1_Zn9j-wnCQ6YCQ</recordid><startdate>20220501</startdate><enddate>20220501</enddate><creator>Elmokadem, Ali H.</creator><creator>Mounir, Ahmad M.</creator><creator>Ramadan, Zainab A.</creator><creator>Elsedeiq, Mahmoud</creator><creator>Saleh, Gehad A.</creator><general>Springer Berlin Heidelberg</general><general>Springer Nature B.V</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7QO</scope><scope>7RV</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AO</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>KB0</scope><scope>LK8</scope><scope>M0S</scope><scope>M1P</scope><scope>M7P</scope><scope>NAPCQ</scope><scope>P5Z</scope><scope>P62</scope><scope>P64</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0001-5119-9548</orcidid><orcidid>https://orcid.org/0000-0001-6462-8778</orcidid><orcidid>https://orcid.org/0000-0001-7851-689X</orcidid><orcidid>https://orcid.org/0000-0002-4817-4478</orcidid><orcidid>https://orcid.org/0000-0002-3322-7960</orcidid></search><sort><creationdate>20220501</creationdate><title>Comparison of chest CT severity scoring systems for COVID-19</title><author>Elmokadem, Ali H. ; Mounir, Ahmad M. ; Ramadan, Zainab A. ; Elsedeiq, Mahmoud ; Saleh, Gehad A.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c404t-1b90a304fb8e4e40fa5d1db7c823249605739eb04c648796e16cac047820f85d3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Abnormalities</topic><topic>Agreements</topic><topic>Chest</topic><topic>Coronaviruses</topic><topic>COVID-19</topic><topic>Diagnostic Radiology</topic><topic>Diagnostic systems</topic><topic>Female</topic><topic>Humans</topic><topic>Imaging</topic><topic>Internal Medicine</topic><topic>Interventional Radiology</topic><topic>Lung - diagnostic imaging</topic><topic>Male</topic><topic>Medicine</topic><topic>Medicine & Public Health</topic><topic>Neuroradiology</topic><topic>Qualitative analysis</topic><topic>Radiology</topic><topic>Retrospective Studies</topic><topic>SARS-CoV-2</topic><topic>Statistical analysis</topic><topic>Thorax</topic><topic>Tomography, X-Ray Computed - methods</topic><topic>Ultrasound</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Elmokadem, Ali H.</creatorcontrib><creatorcontrib>Mounir, Ahmad M.</creatorcontrib><creatorcontrib>Ramadan, Zainab A.</creatorcontrib><creatorcontrib>Elsedeiq, Mahmoud</creatorcontrib><creatorcontrib>Saleh, Gehad A.</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Biotechnology Research Abstracts</collection><collection>Proquest Nursing & Allied Health Source</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>Natural Science Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Nursing & Allied Health Database (Alumni Edition)</collection><collection>ProQuest Biological Science Collection</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Biological Science Database</collection><collection>Nursing & Allied Health Premium</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>European radiology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Elmokadem, Ali H.</au><au>Mounir, Ahmad M.</au><au>Ramadan, Zainab A.</au><au>Elsedeiq, Mahmoud</au><au>Saleh, Gehad A.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Comparison of chest CT severity scoring systems for COVID-19</atitle><jtitle>European radiology</jtitle><stitle>Eur Radiol</stitle><addtitle>Eur Radiol</addtitle><date>2022-05-01</date><risdate>2022</risdate><volume>32</volume><issue>5</issue><spage>3501</spage><epage>3512</epage><pages>3501-3512</pages><issn>1432-1084</issn><issn>0938-7994</issn><eissn>1432-1084</eissn><abstract>Purpose
To compare the diagnostic performance and inter-observer agreement of five different CT chest severity scoring systems for COVID-19 to find the most precise one with the least interpretation time.
Methods and materials
This retrospective study included 85 patients (54 male and 31 female) with PCR-confirmed COVID-19. They underwent CT to assess the severity of pulmonary involvement. Three readers were asked to assess the pulmonary abnormalities and score the severity using five different systems, including chest CT severity score (CT-SS), chest CT score, total severity score (TSS), modified total severity score (m-TSS), and 3-level chest CT severity score. Time consumption on reporting of each system was calculated.
Results
Two hundred fifty-five observations were reported for each system. There was a statistically significant inter-observer agreement in assessing qualitative lung involvement using the m-TSS and the other four quantitative systems. The ROC curves revealed excellent and very good diagnostic accuracy for all systems when cutoff values for detection severe cases were > 22, > 17, > 12, and > 26 for CT-SS, chest CT score, TSS, and 3-level CT severity score. The AUC was very good (0.86), excellent (0.90), very good (0.89), and very good (0.86), respectively. Chest CT score showed the highest specificity (95.2%) in discrimination of severe cases. Time consumption on reporting was significantly different (< 0.001): CT-SS > 3L-CT-SS > chest CT score > TSS.
Conclusion
All chest CT severity scoring systems in this study demonstrated excellent inter-observer agreement and reasonable performance to assess COVID-19 in relation to the clinical severity. CT-SS and TSS had the highest specificity and least time for interpretation.
Key Points
• All chest CT severity scoring systems discussed in this study revealed excellent inter-observer agreement and reasonable performance to assess COVID-19 in relation to the clinical severity.
• Chest CT scoring system and TSS had the highest specificity.
• Both TSS and m-TSS consumed the least time compared to the other three scoring systems.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><pmid>35031841</pmid><doi>10.1007/s00330-021-08432-5</doi><tpages>12</tpages><orcidid>https://orcid.org/0000-0001-5119-9548</orcidid><orcidid>https://orcid.org/0000-0001-6462-8778</orcidid><orcidid>https://orcid.org/0000-0001-7851-689X</orcidid><orcidid>https://orcid.org/0000-0002-4817-4478</orcidid><orcidid>https://orcid.org/0000-0002-3322-7960</orcidid><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1432-1084 |
| ispartof | European radiology, 2022-05, Vol.32 (5), p.3501-3512 |
| issn | 1432-1084 0938-7994 1432-1084 |
| language | eng |
| recordid | cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_8760133 |
| source | MEDLINE; SpringerNature Journals |
| subjects | Abnormalities Agreements Chest Coronaviruses COVID-19 Diagnostic Radiology Diagnostic systems Female Humans Imaging Internal Medicine Interventional Radiology Lung - diagnostic imaging Male Medicine Medicine & Public Health Neuroradiology Qualitative analysis Radiology Retrospective Studies SARS-CoV-2 Statistical analysis Thorax Tomography, X-Ray Computed - methods Ultrasound |
| title | Comparison of chest CT severity scoring systems for COVID-19 |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-07T00%3A11%3A47IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_pubme&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Comparison%20of%20chest%20CT%20severity%20scoring%20systems%20for%20COVID-19&rft.jtitle=European%20radiology&rft.au=Elmokadem,%20Ali%20H.&rft.date=2022-05-01&rft.volume=32&rft.issue=5&rft.spage=3501&rft.epage=3512&rft.pages=3501-3512&rft.issn=1432-1084&rft.eissn=1432-1084&rft_id=info:doi/10.1007/s00330-021-08432-5&rft_dat=%3Cproquest_pubme%3E2654822482%3C/proquest_pubme%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2654822482&rft_id=info:pmid/35031841&rfr_iscdi=true |