Evolution of CT Findings and Lung Residue in Patients with COVID-19 Pneumonia: Quantitative Analysis of the Disease with a Computer Automatic Tool
Purpose: the purpose of this study was to assess the evolution of computed tomography (CT) findings and lung residue in patients with COVID-19 pneumonia, via quantified evaluation of the disease, using a computer aided tool. Materials and methods: we retrospectively evaluated 341 CT examinations of...
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Veröffentlicht in: | Journal of personalized medicine 2021-07, Vol.11 (7), p.641 |
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creator | Grassi, Roberto Cappabianca, Salvatore Urraro, Fabrizio Granata, Vincenza Giacobbe, Giuliana Magliocchetti, Simona Cozzi, Diletta Fusco, Roberta Galdiero, Roberta Picone, Carmine Belfiore, Maria Paola Reginelli, Alfonso Atripaldi, Umberto Picascia, Ornella Coppola, Michele Bignardi, Elio Grassi, Roberta Miele, Vittorio |
description | Purpose: the purpose of this study was to assess the evolution of computed tomography (CT) findings and lung residue in patients with COVID-19 pneumonia, via quantified evaluation of the disease, using a computer aided tool. Materials and methods: we retrospectively evaluated 341 CT examinations of 140 patients (68 years of median age) infected with COVID-19 (confirmed by real-time reverse transcriptase polymerase chain reaction (RT-PCR)), who were hospitalized, and who received clinical and CT examinations. All CTs were evaluated by two expert radiologists, in consensus, at the same reading session, using a computer-aided tool for quantification of the pulmonary disease. The parameters obtained using the computer tool included the healthy residual parenchyma, ground glass opacity, consolidation, and total lung volume. Results: statistically significant differences (p value ≤ 0.05) were found among quantified volumes of healthy residual parenchyma, ground glass opacity (GGO), consolidation, and total lung volume, considering different clinical conditions (stable, improved, and worsened). Statistically significant differences were found among quantified volumes for healthy residual parenchyma, GGO, and consolidation (p value ≤ 0.05) between dead patients and discharged patients. CT was not performed on cadavers; the death was an outcome, which was retrospectively included to differentiate findings of patients who survived vs. patients who died during hospitalization. Among discharged patients, complete disease resolutions on CT scans were observed in 62/129 patients with lung disease involvement ≤5%; lung disease involvement from 5% to 15% was found in 40/129 patients, while 27/129 patients had lung disease involvement between 16 and 30%. Moreover, 8–21 days (after hospital admission) was an “advanced period” with the most severe lung disease involvement. After the extent of involvement started to decrease—particularly after 21 days—the absorption was more obvious. Conclusions: a complete disease resolution on chest CT scans was observed in 48.1% of discharged patients using a computer-aided tool to quantify the GGO and consolidation volumes; after 16 days of hospital admission, the abnormalities identified by chest CT began to improve; in particular, the absorption was more obvious after 21 days. |
doi_str_mv | 10.3390/jpm11070641 |
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Materials and methods: we retrospectively evaluated 341 CT examinations of 140 patients (68 years of median age) infected with COVID-19 (confirmed by real-time reverse transcriptase polymerase chain reaction (RT-PCR)), who were hospitalized, and who received clinical and CT examinations. All CTs were evaluated by two expert radiologists, in consensus, at the same reading session, using a computer-aided tool for quantification of the pulmonary disease. The parameters obtained using the computer tool included the healthy residual parenchyma, ground glass opacity, consolidation, and total lung volume. Results: statistically significant differences (p value ≤ 0.05) were found among quantified volumes of healthy residual parenchyma, ground glass opacity (GGO), consolidation, and total lung volume, considering different clinical conditions (stable, improved, and worsened). Statistically significant differences were found among quantified volumes for healthy residual parenchyma, GGO, and consolidation (p value ≤ 0.05) between dead patients and discharged patients. CT was not performed on cadavers; the death was an outcome, which was retrospectively included to differentiate findings of patients who survived vs. patients who died during hospitalization. Among discharged patients, complete disease resolutions on CT scans were observed in 62/129 patients with lung disease involvement ≤5%; lung disease involvement from 5% to 15% was found in 40/129 patients, while 27/129 patients had lung disease involvement between 16 and 30%. Moreover, 8–21 days (after hospital admission) was an “advanced period” with the most severe lung disease involvement. After the extent of involvement started to decrease—particularly after 21 days—the absorption was more obvious. Conclusions: a complete disease resolution on chest CT scans was observed in 48.1% of discharged patients using a computer-aided tool to quantify the GGO and consolidation volumes; after 16 days of hospital admission, the abnormalities identified by chest CT began to improve; in particular, the absorption was more obvious after 21 days.</description><identifier>ISSN: 2075-4426</identifier><identifier>EISSN: 2075-4426</identifier><identifier>DOI: 10.3390/jpm11070641</identifier><identifier>PMID: 34357108</identifier><language>eng</language><publisher>Basel: MDPI AG</publisher><subject>Cadavers ; Chest ; Computed tomography ; Coronaviruses ; COVID-19 ; Lung diseases ; Medical equipment ; Parenchyma ; Patients ; Pneumonia ; Polymerase chain reaction ; Precision medicine ; Quantitative analysis ; RNA-directed DNA polymerase ; Software ; Statistical analysis</subject><ispartof>Journal of personalized medicine, 2021-07, Vol.11 (7), p.641</ispartof><rights>2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2021 by the authors. 2021</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c386t-78414a436529e36baa6b900eb8575c624261917c597f486e7b543ea772b227233</citedby><cites>FETCH-LOGICAL-c386t-78414a436529e36baa6b900eb8575c624261917c597f486e7b543ea772b227233</cites><orcidid>0000-0002-0469-9969 ; 0000-0001-7028-9047 ; 0000-0002-7848-1567 ; 0000-0003-0566-3199</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/PMC8305822/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC8305822/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,881,27903,27904,53769,53771</link.rule.ids></links><search><creatorcontrib>Grassi, Roberto</creatorcontrib><creatorcontrib>Cappabianca, Salvatore</creatorcontrib><creatorcontrib>Urraro, Fabrizio</creatorcontrib><creatorcontrib>Granata, Vincenza</creatorcontrib><creatorcontrib>Giacobbe, Giuliana</creatorcontrib><creatorcontrib>Magliocchetti, Simona</creatorcontrib><creatorcontrib>Cozzi, Diletta</creatorcontrib><creatorcontrib>Fusco, Roberta</creatorcontrib><creatorcontrib>Galdiero, Roberta</creatorcontrib><creatorcontrib>Picone, Carmine</creatorcontrib><creatorcontrib>Belfiore, Maria Paola</creatorcontrib><creatorcontrib>Reginelli, Alfonso</creatorcontrib><creatorcontrib>Atripaldi, Umberto</creatorcontrib><creatorcontrib>Picascia, Ornella</creatorcontrib><creatorcontrib>Coppola, Michele</creatorcontrib><creatorcontrib>Bignardi, Elio</creatorcontrib><creatorcontrib>Grassi, Roberta</creatorcontrib><creatorcontrib>Miele, Vittorio</creatorcontrib><title>Evolution of CT Findings and Lung Residue in Patients with COVID-19 Pneumonia: Quantitative Analysis of the Disease with a Computer Automatic Tool</title><title>Journal of personalized medicine</title><description>Purpose: the purpose of this study was to assess the evolution of computed tomography (CT) findings and lung residue in patients with COVID-19 pneumonia, via quantified evaluation of the disease, using a computer aided tool. Materials and methods: we retrospectively evaluated 341 CT examinations of 140 patients (68 years of median age) infected with COVID-19 (confirmed by real-time reverse transcriptase polymerase chain reaction (RT-PCR)), who were hospitalized, and who received clinical and CT examinations. All CTs were evaluated by two expert radiologists, in consensus, at the same reading session, using a computer-aided tool for quantification of the pulmonary disease. The parameters obtained using the computer tool included the healthy residual parenchyma, ground glass opacity, consolidation, and total lung volume. Results: statistically significant differences (p value ≤ 0.05) were found among quantified volumes of healthy residual parenchyma, ground glass opacity (GGO), consolidation, and total lung volume, considering different clinical conditions (stable, improved, and worsened). Statistically significant differences were found among quantified volumes for healthy residual parenchyma, GGO, and consolidation (p value ≤ 0.05) between dead patients and discharged patients. CT was not performed on cadavers; the death was an outcome, which was retrospectively included to differentiate findings of patients who survived vs. patients who died during hospitalization. Among discharged patients, complete disease resolutions on CT scans were observed in 62/129 patients with lung disease involvement ≤5%; lung disease involvement from 5% to 15% was found in 40/129 patients, while 27/129 patients had lung disease involvement between 16 and 30%. Moreover, 8–21 days (after hospital admission) was an “advanced period” with the most severe lung disease involvement. After the extent of involvement started to decrease—particularly after 21 days—the absorption was more obvious. Conclusions: a complete disease resolution on chest CT scans was observed in 48.1% of discharged patients using a computer-aided tool to quantify the GGO and consolidation volumes; after 16 days of hospital admission, the abnormalities identified by chest CT began to improve; in particular, the absorption was more obvious after 21 days.</description><subject>Cadavers</subject><subject>Chest</subject><subject>Computed tomography</subject><subject>Coronaviruses</subject><subject>COVID-19</subject><subject>Lung diseases</subject><subject>Medical equipment</subject><subject>Parenchyma</subject><subject>Patients</subject><subject>Pneumonia</subject><subject>Polymerase chain reaction</subject><subject>Precision medicine</subject><subject>Quantitative analysis</subject><subject>RNA-directed DNA polymerase</subject><subject>Software</subject><subject>Statistical analysis</subject><issn>2075-4426</issn><issn>2075-4426</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNpdkdtqGzEQhkVpaUKaq76AoDeFsq3O0vaiYDaHBgxJi9tboV2PbZldyV1JDnmNPnHWdShp52YG5puf-WcQekvJR85r8mm7GyglmihBX6BTRrSshGDq5bP6BJ2ntCVTGMmYIq_RCRdcakrMKfp9uY99yT4GHFe4WeArH5Y-rBN2YYnnJazxd0h-WQD7gO9c9hBywvc-b3Bz-_PmoqI1vgtQhhi8-4y_FReyzxO3BzwLrn9IPh2k8wbwhU_gEhynHW7isCsZRjwrOQ7TSIcXMfZv0KuV6xOcP-Uz9OPqctF8rea31zfNbF513KhcaSOocIIryWrgqnVOtTUh0BqpZafY5JzWVHey1ithFOhWCg5Oa9YyphnnZ-jLUXdX2gGW3WRsdL3djX5w44ONztt_O8Fv7DrureFEGsYmgfdPAmP8VSBlO_jUQd-7ALEky6SsBWfU0Al99x-6jWWczvOHEtJoIw_UhyPVjTGlEVZ_l6HEHt5tn72bPwLJ1pr3</recordid><startdate>20210706</startdate><enddate>20210706</enddate><creator>Grassi, Roberto</creator><creator>Cappabianca, Salvatore</creator><creator>Urraro, Fabrizio</creator><creator>Granata, Vincenza</creator><creator>Giacobbe, Giuliana</creator><creator>Magliocchetti, Simona</creator><creator>Cozzi, Diletta</creator><creator>Fusco, Roberta</creator><creator>Galdiero, Roberta</creator><creator>Picone, Carmine</creator><creator>Belfiore, Maria Paola</creator><creator>Reginelli, Alfonso</creator><creator>Atripaldi, Umberto</creator><creator>Picascia, Ornella</creator><creator>Coppola, Michele</creator><creator>Bignardi, Elio</creator><creator>Grassi, Roberta</creator><creator>Miele, Vittorio</creator><general>MDPI AG</general><general>MDPI</general><scope>AAYXX</scope><scope>CITATION</scope><scope>8FE</scope><scope>8FH</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>COVID</scope><scope>DWQXO</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>LK8</scope><scope>M7P</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0002-0469-9969</orcidid><orcidid>https://orcid.org/0000-0001-7028-9047</orcidid><orcidid>https://orcid.org/0000-0002-7848-1567</orcidid><orcidid>https://orcid.org/0000-0003-0566-3199</orcidid></search><sort><creationdate>20210706</creationdate><title>Evolution of CT Findings and Lung Residue in Patients with COVID-19 Pneumonia: Quantitative Analysis of the Disease with a Computer Automatic Tool</title><author>Grassi, Roberto ; 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Materials and methods: we retrospectively evaluated 341 CT examinations of 140 patients (68 years of median age) infected with COVID-19 (confirmed by real-time reverse transcriptase polymerase chain reaction (RT-PCR)), who were hospitalized, and who received clinical and CT examinations. All CTs were evaluated by two expert radiologists, in consensus, at the same reading session, using a computer-aided tool for quantification of the pulmonary disease. The parameters obtained using the computer tool included the healthy residual parenchyma, ground glass opacity, consolidation, and total lung volume. Results: statistically significant differences (p value ≤ 0.05) were found among quantified volumes of healthy residual parenchyma, ground glass opacity (GGO), consolidation, and total lung volume, considering different clinical conditions (stable, improved, and worsened). Statistically significant differences were found among quantified volumes for healthy residual parenchyma, GGO, and consolidation (p value ≤ 0.05) between dead patients and discharged patients. CT was not performed on cadavers; the death was an outcome, which was retrospectively included to differentiate findings of patients who survived vs. patients who died during hospitalization. Among discharged patients, complete disease resolutions on CT scans were observed in 62/129 patients with lung disease involvement ≤5%; lung disease involvement from 5% to 15% was found in 40/129 patients, while 27/129 patients had lung disease involvement between 16 and 30%. Moreover, 8–21 days (after hospital admission) was an “advanced period” with the most severe lung disease involvement. After the extent of involvement started to decrease—particularly after 21 days—the absorption was more obvious. Conclusions: a complete disease resolution on chest CT scans was observed in 48.1% of discharged patients using a computer-aided tool to quantify the GGO and consolidation volumes; after 16 days of hospital admission, the abnormalities identified by chest CT began to improve; in particular, the absorption was more obvious after 21 days.</abstract><cop>Basel</cop><pub>MDPI AG</pub><pmid>34357108</pmid><doi>10.3390/jpm11070641</doi><orcidid>https://orcid.org/0000-0002-0469-9969</orcidid><orcidid>https://orcid.org/0000-0001-7028-9047</orcidid><orcidid>https://orcid.org/0000-0002-7848-1567</orcidid><orcidid>https://orcid.org/0000-0003-0566-3199</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Cadavers Chest Computed tomography Coronaviruses COVID-19 Lung diseases Medical equipment Parenchyma Patients Pneumonia Polymerase chain reaction Precision medicine Quantitative analysis RNA-directed DNA polymerase Software Statistical analysis |
title | Evolution of CT Findings and Lung Residue in Patients with COVID-19 Pneumonia: Quantitative Analysis of the Disease with a Computer Automatic Tool |
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