Visual and software-based quantitative chest CT assessment of COVID-19: correlation with clinical findings
PURPOSE The aim of this study was to evaluate visual and software-based quantitative assessment of parenchymal changes and normal lung parenchyma in patients with coronavirus disease 2019 (COVID-19) pneumonia. The secondary aim of the study was to compare the radiologic findings with clinical and la...
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| Veröffentlicht in: | Diagnostic and interventional radiology (Ankara, Turkey) Turkey), 2020-11, Vol.26 (6), p.557-564 |
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| creator | Durhan, Gamze Duzgun, Selin Ardali Demirkazik, Figen Basaran Irmak, Ilim Idilman, Ilkay Akpinar, Meltem Gulsun Akpinar, Erhan Ocal, Serpil Telli, Gulcin Topeli, Arzu Ariyurek, Orhan Macit |
| description | PURPOSE
The aim of this study was to evaluate visual and software-based quantitative assessment of parenchymal changes and normal lung parenchyma in patients with coronavirus disease 2019 (COVID-19) pneumonia. The secondary aim of the study was to compare the radiologic findings with clinical and laboratory data.
METHODS
Patients with COVID-19 who underwentcomputed tomography (CT) between March 11, 2020 and April 15, 2020 were retrospectively evaluated. Clinical and laboratory findings of patients with abnormal findings on chest CT and PCR-evidence of COVID-19 infection were recorded. Visual quantitative assessment score (VQAS) was performed according to the extent of lung opacities. Software-based quantitative assessment of the normal lung parenchyma percentage (SQNLP) was automatically quantified by a deep learning software. The presence of consolidation and crazy paving pattern (CPP) was also recorded. Statistical analyses were performed to evaluate the correlation between quantitative radiologic assessments, and clinical and laboratory findings, as well as to determine the predictive utility of radiologic findings for estimating severe pneumonia and admission to intensive care unit (ICU).
RESULTS
A total of 90 patients were enrolled. Both VQAS and SQNLP were significantly correlated with multiple clinical parameters. While VQAS >8.5 (sensitivity, 84.2%; specificity, 80.3%) and SQNLP 9.5 (sensitivity, 93.3%; specificity, 86.5%) and SQNLP |
| doi_str_mv | 10.5152/dir.2020.20407 |
| format | Article |
| fullrecord | <record><control><sourceid>gale_webof</sourceid><recordid>TN_cdi_gale_infotracacademiconefile_A658223027</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><galeid>A658223027</galeid><doaj_id>oai_doaj_org_article_e6578cc778174de188ca6569dc268951</doaj_id><sourcerecordid>A658223027</sourcerecordid><originalsourceid>FETCH-LOGICAL-c523t-18d99115a97b4fe1de39e13049390acdcbea2a1873efebff6765b790d6acffed3</originalsourceid><addsrcrecordid>eNqNkk1rGzEQhpfS0qRprz2WhV4KZV19rKRVD4HgfhkCuaS5Cq00smXWUiLtxvTfV7ZTk0AOlUASo2deZoa3qt5jNGOYkS_WpxlBBJWjReJFdYopYg3lmLx89D6p3uS8RogxidvX1QklneCMy9NqfePzpIdaB1vn6MatTtD0OoOt7yYdRj_q0d9DbVaQx3p-XeucIecNhLGOrp5f3Sy-NVh-rU1MCYYCx1Bv_biqzeCDN0Xa-WB9WOa31SunhwzvHu6z6veP79fzX83l1c_F_OKyMYzQscGdlRJjpqXoWwfYApVQOmkllUgba3rQRONOUHDQO8dLJ72QyHJtnANLz6rFQddGvVa3yW90-qOi9mofiGmpdBq9GUABZ6IzRogOi9YC7jqjd3OxhvBOMly0zg9at1O_AWtK20kPT0Sf_gS_Ust4rwTnrWhZEfj0IJDi3VRmqDY-GxgGHSBOWZGWSk5I29GCfjygS11K88HFomh2uLrgrCOEIiIKNXuGKtvCxpsYwPkSfy7BpJhzAnesHiO185AqHlI7D6m9h0rCh8c9H_F_pilAdwC20EeXjYdg4IihYjNJWszRbon53kExzOMUxpL6-f9T6V-7E-Mu</addsrcrecordid><sourcetype>Open Website</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2439622483</pqid></control><display><type>article</type><title>Visual and software-based quantitative chest CT assessment of COVID-19: correlation with clinical findings</title><source>MEDLINE</source><source>Web of Science - Science Citation Index Expanded - 2020<img src="https://exlibris-pub.s3.amazonaws.com/fromwos-v2.jpg" /></source><source>EZB-FREE-00999 freely available EZB journals</source><source>PubMed Central</source><creator>Durhan, Gamze ; Duzgun, Selin Ardali ; Demirkazik, Figen Basaran ; Irmak, Ilim ; Idilman, Ilkay ; Akpinar, Meltem Gulsun ; Akpinar, Erhan ; Ocal, Serpil ; Telli, Gulcin ; Topeli, Arzu ; Ariyurek, Orhan Macit</creator><creatorcontrib>Durhan, Gamze ; Duzgun, Selin Ardali ; Demirkazik, Figen Basaran ; Irmak, Ilim ; Idilman, Ilkay ; Akpinar, Meltem Gulsun ; Akpinar, Erhan ; Ocal, Serpil ; Telli, Gulcin ; Topeli, Arzu ; Ariyurek, Orhan Macit ; Division of Intensive Care, Department of Internal Disease, Hacettepe University Faculty of Medicine, Ankara, Turkey ; Department of Pulmonary Medicine, Hacettepe University School of Medicine, Ankara, Turkey ; Department of Radiology, Hacettepe University Faculty of Medicine, Ankara, Turkey ; Department of Infectious Diseases, Hacettepe University Faculty of Medicine, Ankara, Turkey</creatorcontrib><description>PURPOSE
The aim of this study was to evaluate visual and software-based quantitative assessment of parenchymal changes and normal lung parenchyma in patients with coronavirus disease 2019 (COVID-19) pneumonia. The secondary aim of the study was to compare the radiologic findings with clinical and laboratory data.
METHODS
Patients with COVID-19 who underwentcomputed tomography (CT) between March 11, 2020 and April 15, 2020 were retrospectively evaluated. Clinical and laboratory findings of patients with abnormal findings on chest CT and PCR-evidence of COVID-19 infection were recorded. Visual quantitative assessment score (VQAS) was performed according to the extent of lung opacities. Software-based quantitative assessment of the normal lung parenchyma percentage (SQNLP) was automatically quantified by a deep learning software. The presence of consolidation and crazy paving pattern (CPP) was also recorded. Statistical analyses were performed to evaluate the correlation between quantitative radiologic assessments, and clinical and laboratory findings, as well as to determine the predictive utility of radiologic findings for estimating severe pneumonia and admission to intensive care unit (ICU).
RESULTS
A total of 90 patients were enrolled. Both VQAS and SQNLP were significantly correlated with multiple clinical parameters. While VQAS >8.5 (sensitivity, 84.2%; specificity, 80.3%) and SQNLP <82.45% (sensitivity, 83.1%; specificity, 84.2%) were related to severe pneumonia, VQAS >9.5 (sensitivity, 93.3%; specificity, 86.5%) and SQNLP <81.1% (sensitivity, 86.5%; specificity, 86.7%) were predictive of ICU admission. Both consolidation and CPP were more commonly seen in patients with severe pneumonia than patients with nonsevere pneumonia (P = 0.197 for consolidation; P < 0.001 for CPP). Moreover, the presence of CPP showed high specificity (97.2%) for severe pneumonia.
CONCLUSION
Both SQNLP and VQAS were significantly related to the clinical findings, highlightinc their clinical utility in predicting severe pneumonia, ICU admission, length of hospital stay, and management of the disease. On the other hand, presence of CPP has high specificity for severe COVID-19 pneumonia.</description><identifier>ISSN: 1305-3612</identifier><identifier>ISSN: 1305-3825</identifier><identifier>EISSN: 1305-3612</identifier><identifier>DOI: 10.5152/dir.2020.20407</identifier><identifier>PMID: 32876569</identifier><language>eng</language><publisher>ANKARA: TURKISH SOC RADIOLOGY</publisher><subject>Adult ; Bacterial pneumonia ; Betacoronavirus ; Chest Imaging ; Computer-aided medical diagnosis ; Coronavirus Infections - diagnostic imaging ; COVID-19 ; CT imaging ; Diagnosis ; Evaluation Studies as Topic ; Female ; Humans ; Image Interpretation, Computer-Assisted - methods ; Life Sciences & Biomedicine ; Lung - diagnostic imaging ; Male ; Methods ; Middle Aged ; Pandemics ; Pneumonia ; Pneumonia, Viral - diagnostic imaging ; Radiology, Nuclear Medicine & Medical Imaging ; Retrospective Studies ; Risk factors ; SARS-CoV-2 ; Science & Technology ; Tomography, X-Ray Computed - methods</subject><ispartof>Diagnostic and interventional radiology (Ankara, Turkey), 2020-11, Vol.26 (6), p.557-564</ispartof><rights>COPYRIGHT 2020 Galenos Yayinevi Tic. Ltd.</rights><rights>Copyright 2020 by the Turkish Society of Radiology 2020</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>true</woscitedreferencessubscribed><woscitedreferencescount>19</woscitedreferencescount><woscitedreferencesoriginalsourcerecordid>wos000592416000007</woscitedreferencesoriginalsourcerecordid><citedby>FETCH-LOGICAL-c523t-18d99115a97b4fe1de39e13049390acdcbea2a1873efebff6765b790d6acffed3</citedby><cites>FETCH-LOGICAL-c523t-18d99115a97b4fe1de39e13049390acdcbea2a1873efebff6765b790d6acffed3</cites><orcidid>0000-0002-1913-2404</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC7664745/pdf/$$EPDF$$P50$$Gpubmedcentral$$H</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC7664745/$$EHTML$$P50$$Gpubmedcentral$$H</linktohtml><link.rule.ids>230,315,728,781,785,886,27929,27930,28253,53796,53798</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/32876569$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Durhan, Gamze</creatorcontrib><creatorcontrib>Duzgun, Selin Ardali</creatorcontrib><creatorcontrib>Demirkazik, Figen Basaran</creatorcontrib><creatorcontrib>Irmak, Ilim</creatorcontrib><creatorcontrib>Idilman, Ilkay</creatorcontrib><creatorcontrib>Akpinar, Meltem Gulsun</creatorcontrib><creatorcontrib>Akpinar, Erhan</creatorcontrib><creatorcontrib>Ocal, Serpil</creatorcontrib><creatorcontrib>Telli, Gulcin</creatorcontrib><creatorcontrib>Topeli, Arzu</creatorcontrib><creatorcontrib>Ariyurek, Orhan Macit</creatorcontrib><creatorcontrib>Division of Intensive Care, Department of Internal Disease, Hacettepe University Faculty of Medicine, Ankara, Turkey</creatorcontrib><creatorcontrib>Department of Pulmonary Medicine, Hacettepe University School of Medicine, Ankara, Turkey</creatorcontrib><creatorcontrib>Department of Radiology, Hacettepe University Faculty of Medicine, Ankara, Turkey</creatorcontrib><creatorcontrib>Department of Infectious Diseases, Hacettepe University Faculty of Medicine, Ankara, Turkey</creatorcontrib><title>Visual and software-based quantitative chest CT assessment of COVID-19: correlation with clinical findings</title><title>Diagnostic and interventional radiology (Ankara, Turkey)</title><addtitle>DIAGN INTERV RADIOL</addtitle><addtitle>Diagn Interv Radiol</addtitle><description>PURPOSE
The aim of this study was to evaluate visual and software-based quantitative assessment of parenchymal changes and normal lung parenchyma in patients with coronavirus disease 2019 (COVID-19) pneumonia. The secondary aim of the study was to compare the radiologic findings with clinical and laboratory data.
METHODS
Patients with COVID-19 who underwentcomputed tomography (CT) between March 11, 2020 and April 15, 2020 were retrospectively evaluated. Clinical and laboratory findings of patients with abnormal findings on chest CT and PCR-evidence of COVID-19 infection were recorded. Visual quantitative assessment score (VQAS) was performed according to the extent of lung opacities. Software-based quantitative assessment of the normal lung parenchyma percentage (SQNLP) was automatically quantified by a deep learning software. The presence of consolidation and crazy paving pattern (CPP) was also recorded. Statistical analyses were performed to evaluate the correlation between quantitative radiologic assessments, and clinical and laboratory findings, as well as to determine the predictive utility of radiologic findings for estimating severe pneumonia and admission to intensive care unit (ICU).
RESULTS
A total of 90 patients were enrolled. Both VQAS and SQNLP were significantly correlated with multiple clinical parameters. While VQAS >8.5 (sensitivity, 84.2%; specificity, 80.3%) and SQNLP <82.45% (sensitivity, 83.1%; specificity, 84.2%) were related to severe pneumonia, VQAS >9.5 (sensitivity, 93.3%; specificity, 86.5%) and SQNLP <81.1% (sensitivity, 86.5%; specificity, 86.7%) were predictive of ICU admission. Both consolidation and CPP were more commonly seen in patients with severe pneumonia than patients with nonsevere pneumonia (P = 0.197 for consolidation; P < 0.001 for CPP). Moreover, the presence of CPP showed high specificity (97.2%) for severe pneumonia.
CONCLUSION
Both SQNLP and VQAS were significantly related to the clinical findings, highlightinc their clinical utility in predicting severe pneumonia, ICU admission, length of hospital stay, and management of the disease. On the other hand, presence of CPP has high specificity for severe COVID-19 pneumonia.</description><subject>Adult</subject><subject>Bacterial pneumonia</subject><subject>Betacoronavirus</subject><subject>Chest Imaging</subject><subject>Computer-aided medical diagnosis</subject><subject>Coronavirus Infections - diagnostic imaging</subject><subject>COVID-19</subject><subject>CT imaging</subject><subject>Diagnosis</subject><subject>Evaluation Studies as Topic</subject><subject>Female</subject><subject>Humans</subject><subject>Image Interpretation, Computer-Assisted - methods</subject><subject>Life Sciences & Biomedicine</subject><subject>Lung - diagnostic imaging</subject><subject>Male</subject><subject>Methods</subject><subject>Middle Aged</subject><subject>Pandemics</subject><subject>Pneumonia</subject><subject>Pneumonia, Viral - diagnostic imaging</subject><subject>Radiology, Nuclear Medicine & Medical Imaging</subject><subject>Retrospective Studies</subject><subject>Risk factors</subject><subject>SARS-CoV-2</subject><subject>Science & Technology</subject><subject>Tomography, X-Ray Computed - methods</subject><issn>1305-3612</issn><issn>1305-3825</issn><issn>1305-3612</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>AOWDO</sourceid><sourceid>EIF</sourceid><sourceid>DOA</sourceid><recordid>eNqNkk1rGzEQhpfS0qRprz2WhV4KZV19rKRVD4HgfhkCuaS5Cq00smXWUiLtxvTfV7ZTk0AOlUASo2deZoa3qt5jNGOYkS_WpxlBBJWjReJFdYopYg3lmLx89D6p3uS8RogxidvX1QklneCMy9NqfePzpIdaB1vn6MatTtD0OoOt7yYdRj_q0d9DbVaQx3p-XeucIecNhLGOrp5f3Sy-NVh-rU1MCYYCx1Bv_biqzeCDN0Xa-WB9WOa31SunhwzvHu6z6veP79fzX83l1c_F_OKyMYzQscGdlRJjpqXoWwfYApVQOmkllUgba3rQRONOUHDQO8dLJ72QyHJtnANLz6rFQddGvVa3yW90-qOi9mofiGmpdBq9GUABZ6IzRogOi9YC7jqjd3OxhvBOMly0zg9at1O_AWtK20kPT0Sf_gS_Ust4rwTnrWhZEfj0IJDi3VRmqDY-GxgGHSBOWZGWSk5I29GCfjygS11K88HFomh2uLrgrCOEIiIKNXuGKtvCxpsYwPkSfy7BpJhzAnesHiO185AqHlI7D6m9h0rCh8c9H_F_pilAdwC20EeXjYdg4IihYjNJWszRbon53kExzOMUxpL6-f9T6V-7E-Mu</recordid><startdate>20201101</startdate><enddate>20201101</enddate><creator>Durhan, Gamze</creator><creator>Duzgun, Selin Ardali</creator><creator>Demirkazik, Figen Basaran</creator><creator>Irmak, Ilim</creator><creator>Idilman, Ilkay</creator><creator>Akpinar, Meltem Gulsun</creator><creator>Akpinar, Erhan</creator><creator>Ocal, Serpil</creator><creator>Telli, Gulcin</creator><creator>Topeli, Arzu</creator><creator>Ariyurek, Orhan Macit</creator><general>TURKISH SOC RADIOLOGY</general><general>Galenos Yayinevi Tic. Ltd</general><general>Turkish Society of Radiology</general><general>Galenos Publishing House</general><scope>AOWDO</scope><scope>BLEPL</scope><scope>DTL</scope><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>7X8</scope><scope>5PM</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0002-1913-2404</orcidid></search><sort><creationdate>20201101</creationdate><title>Visual and software-based quantitative chest CT assessment of COVID-19: correlation with clinical findings</title><author>Durhan, Gamze ; Duzgun, Selin Ardali ; Demirkazik, Figen Basaran ; Irmak, Ilim ; Idilman, Ilkay ; Akpinar, Meltem Gulsun ; Akpinar, Erhan ; Ocal, Serpil ; Telli, Gulcin ; Topeli, Arzu ; Ariyurek, Orhan Macit</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c523t-18d99115a97b4fe1de39e13049390acdcbea2a1873efebff6765b790d6acffed3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Adult</topic><topic>Bacterial pneumonia</topic><topic>Betacoronavirus</topic><topic>Chest Imaging</topic><topic>Computer-aided medical diagnosis</topic><topic>Coronavirus Infections - diagnostic imaging</topic><topic>COVID-19</topic><topic>CT imaging</topic><topic>Diagnosis</topic><topic>Evaluation Studies as Topic</topic><topic>Female</topic><topic>Humans</topic><topic>Image Interpretation, Computer-Assisted - methods</topic><topic>Life Sciences & Biomedicine</topic><topic>Lung - diagnostic imaging</topic><topic>Male</topic><topic>Methods</topic><topic>Middle Aged</topic><topic>Pandemics</topic><topic>Pneumonia</topic><topic>Pneumonia, Viral - diagnostic imaging</topic><topic>Radiology, Nuclear Medicine & Medical Imaging</topic><topic>Retrospective Studies</topic><topic>Risk factors</topic><topic>SARS-CoV-2</topic><topic>Science & Technology</topic><topic>Tomography, X-Ray Computed - methods</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Durhan, Gamze</creatorcontrib><creatorcontrib>Duzgun, Selin Ardali</creatorcontrib><creatorcontrib>Demirkazik, Figen Basaran</creatorcontrib><creatorcontrib>Irmak, Ilim</creatorcontrib><creatorcontrib>Idilman, Ilkay</creatorcontrib><creatorcontrib>Akpinar, Meltem Gulsun</creatorcontrib><creatorcontrib>Akpinar, Erhan</creatorcontrib><creatorcontrib>Ocal, Serpil</creatorcontrib><creatorcontrib>Telli, Gulcin</creatorcontrib><creatorcontrib>Topeli, Arzu</creatorcontrib><creatorcontrib>Ariyurek, Orhan Macit</creatorcontrib><creatorcontrib>Division of Intensive Care, Department of Internal Disease, Hacettepe University Faculty of Medicine, Ankara, Turkey</creatorcontrib><creatorcontrib>Department of Pulmonary Medicine, Hacettepe University School of Medicine, Ankara, Turkey</creatorcontrib><creatorcontrib>Department of Radiology, Hacettepe University Faculty of Medicine, Ankara, Turkey</creatorcontrib><creatorcontrib>Department of Infectious Diseases, Hacettepe University Faculty of Medicine, Ankara, Turkey</creatorcontrib><collection>Web of Science - Science Citation Index Expanded - 2020</collection><collection>Web of Science Core Collection</collection><collection>Science Citation Index Expanded</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>Diagnostic and interventional radiology (Ankara, Turkey)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Durhan, Gamze</au><au>Duzgun, Selin Ardali</au><au>Demirkazik, Figen Basaran</au><au>Irmak, Ilim</au><au>Idilman, Ilkay</au><au>Akpinar, Meltem Gulsun</au><au>Akpinar, Erhan</au><au>Ocal, Serpil</au><au>Telli, Gulcin</au><au>Topeli, Arzu</au><au>Ariyurek, Orhan Macit</au><aucorp>Division of Intensive Care, Department of Internal Disease, Hacettepe University Faculty of Medicine, Ankara, Turkey</aucorp><aucorp>Department of Pulmonary Medicine, Hacettepe University School of Medicine, Ankara, Turkey</aucorp><aucorp>Department of Radiology, Hacettepe University Faculty of Medicine, Ankara, Turkey</aucorp><aucorp>Department of Infectious Diseases, Hacettepe University Faculty of Medicine, Ankara, Turkey</aucorp><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Visual and software-based quantitative chest CT assessment of COVID-19: correlation with clinical findings</atitle><jtitle>Diagnostic and interventional radiology (Ankara, Turkey)</jtitle><stitle>DIAGN INTERV RADIOL</stitle><addtitle>Diagn Interv Radiol</addtitle><date>2020-11-01</date><risdate>2020</risdate><volume>26</volume><issue>6</issue><spage>557</spage><epage>564</epage><pages>557-564</pages><issn>1305-3612</issn><issn>1305-3825</issn><eissn>1305-3612</eissn><abstract>PURPOSE
The aim of this study was to evaluate visual and software-based quantitative assessment of parenchymal changes and normal lung parenchyma in patients with coronavirus disease 2019 (COVID-19) pneumonia. The secondary aim of the study was to compare the radiologic findings with clinical and laboratory data.
METHODS
Patients with COVID-19 who underwentcomputed tomography (CT) between March 11, 2020 and April 15, 2020 were retrospectively evaluated. Clinical and laboratory findings of patients with abnormal findings on chest CT and PCR-evidence of COVID-19 infection were recorded. Visual quantitative assessment score (VQAS) was performed according to the extent of lung opacities. Software-based quantitative assessment of the normal lung parenchyma percentage (SQNLP) was automatically quantified by a deep learning software. The presence of consolidation and crazy paving pattern (CPP) was also recorded. Statistical analyses were performed to evaluate the correlation between quantitative radiologic assessments, and clinical and laboratory findings, as well as to determine the predictive utility of radiologic findings for estimating severe pneumonia and admission to intensive care unit (ICU).
RESULTS
A total of 90 patients were enrolled. Both VQAS and SQNLP were significantly correlated with multiple clinical parameters. While VQAS >8.5 (sensitivity, 84.2%; specificity, 80.3%) and SQNLP <82.45% (sensitivity, 83.1%; specificity, 84.2%) were related to severe pneumonia, VQAS >9.5 (sensitivity, 93.3%; specificity, 86.5%) and SQNLP <81.1% (sensitivity, 86.5%; specificity, 86.7%) were predictive of ICU admission. Both consolidation and CPP were more commonly seen in patients with severe pneumonia than patients with nonsevere pneumonia (P = 0.197 for consolidation; P < 0.001 for CPP). Moreover, the presence of CPP showed high specificity (97.2%) for severe pneumonia.
CONCLUSION
Both SQNLP and VQAS were significantly related to the clinical findings, highlightinc their clinical utility in predicting severe pneumonia, ICU admission, length of hospital stay, and management of the disease. On the other hand, presence of CPP has high specificity for severe COVID-19 pneumonia.</abstract><cop>ANKARA</cop><pub>TURKISH SOC RADIOLOGY</pub><pmid>32876569</pmid><doi>10.5152/dir.2020.20407</doi><tpages>8</tpages><orcidid>https://orcid.org/0000-0002-1913-2404</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Adult Bacterial pneumonia Betacoronavirus Chest Imaging Computer-aided medical diagnosis Coronavirus Infections - diagnostic imaging COVID-19 CT imaging Diagnosis Evaluation Studies as Topic Female Humans Image Interpretation, Computer-Assisted - methods Life Sciences & Biomedicine Lung - diagnostic imaging Male Methods Middle Aged Pandemics Pneumonia Pneumonia, Viral - diagnostic imaging Radiology, Nuclear Medicine & Medical Imaging Retrospective Studies Risk factors SARS-CoV-2 Science & Technology Tomography, X-Ray Computed - methods |
| title | Visual and software-based quantitative chest CT assessment of COVID-19: correlation with clinical findings |
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