{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T17:52:27Z","timestamp":1773251547772,"version":"3.50.1"},"reference-count":29,"publisher":"Public Library of Science (PLoS)","issue":"3","license":[{"start":{"date-parts":[[2021,3,10]],"date-time":"2021-03-10T00:00:00Z","timestamp":1615334400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS ONE"],"abstract":"<jats:sec id=\"sec001\">\n<jats:title>Purpose<\/jats:title>\n<jats:p>To evaluate the discrimination of parenchymal lesions between COVID-19 and other atypical pneumonia (AP) by using only radiomics features.<\/jats:p>\n<\/jats:sec>\n<jats:sec id=\"sec002\">\n<jats:title>Methods<\/jats:title>\n<jats:p>In this retrospective study, 301 pneumonic lesions (150 ground-glass opacity [GGO], 52 crazy paving [CP], 99 consolidation) obtained from nonenhanced thorax CT scans of 74 AP (46 male and 28 female; 48.25\u00b113.67 years) and 60 COVID-19 (39 male and 21 female; 48.01\u00b120.38 years) patients were segmented manually by two independent radiologists, and Location, Size, Shape, and First- and Second-order radiomics features were calculated.<\/jats:p>\n<\/jats:sec>\n<jats:sec id=\"sec003\">\n<jats:title>Results<\/jats:title>\n<jats:p>Multiple parameters showed significant differences between AP and COVID-19-related GGOs and consolidations, although only the Range parameter was significantly different for CPs. Models developed by using the Bayesian information criterion (BIC) for the whole group of GGO and consolidation lesions predicted COVID-19 consolidation and AP GGO lesions with low accuracy (46.1% and 60.8%, respectively). Thus, instead of subjective classification, lesions were reclassified according to their skewness into positive skewness group (PSG, 78 AP and 71 COVID-19 lesions) and negative skewness group (NSG, 56 AP and 44 COVID-19 lesions), and group-specific models were created. The best AUC, accuracy, sensitivity, and specificity were respectively 0.774, 75.8%, 74.6%, and 76.9% among the PSG models and 0.907, 83%, 79.5%, and 85.7% for the NSG models. The best PSG model was also better at predicting NSG lesions smaller than 3 mL. Using an algorithm, 80% of COVID-19 and 81.1% of AP patients were correctly predicted.<\/jats:p>\n<\/jats:sec>\n<jats:sec id=\"sec004\">\n<jats:title>Conclusion<\/jats:title>\n<jats:p>During periods of increasing AP, radiomics parameters may provide valuable data for the differential diagnosis of COVID-19.<\/jats:p>\n<\/jats:sec>","DOI":"10.1371\/journal.pone.0246582","type":"journal-article","created":{"date-parts":[[2021,3,10]],"date-time":"2021-03-10T18:45:47Z","timestamp":1615401947000},"page":"e0246582","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":16,"title":["A CT radiomics analysis of COVID-19-related ground-glass opacities and consolidation: Is it valuable in a differential diagnosis with other atypical pneumonias?"],"prefix":"10.1371","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5921-1652","authenticated-orcid":true,"given":"Mutlu","family":"G\u00fclbay","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bahad\u0131r Orkun","family":"\u00d6zbay","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6102-2188","authenticated-orcid":true,"given":"B\u00f6kebatur Ahmet Ra\u015fit","family":"Mendi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Aliye","family":"Ba\u015ftu\u011f","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"H\u00fcrrem","family":"Bodur","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"340","published-online":{"date-parts":[[2021,3,10]]},"reference":[{"key":"pone.0246582.ref001","unstructured":"WHO Coronavirus Disease (COVID-19) Dashboard. 2020; Geneva. Retrieved from: https:\/\/covid19.who.int\/ Page last reviewed: February 5, 2021."},{"key":"pone.0246582.ref002","doi-asserted-by":"crossref","unstructured":"Simpson S, Kay FU, Abbara S, et al. Radiological Society of North America Expert Consensus Statement on Reporting Chest CT Findings Related to COVID-19. Endorsed by the Society of Thoracic Radiology, the American College of Radiology, and RSNA. Radiology: Cardiothoracic Imaging. 2020 https:\/\/doi.org\/10.1148\/ryct.2020200152.","DOI":"10.1148\/ryct.2020200152"},{"key":"pone.0246582.ref003","doi-asserted-by":"crossref","first-page":"1178636119890302","DOI":"10.1177\/1178636119890302","article-title":"Clinical Characteristics of Influenza in Season 2017\/2018 in a German Emergency Department: A Retrospective Analysis","volume":"12","author":"S Mohammad","year":"2019","journal-title":"Microbiol Insights"},{"issue":"7","key":"pone.0246582.ref004","doi-asserted-by":"crossref","first-page":"683","DOI":"10.1086\/653202","article-title":"Failure of routine diagnostic methods to detect influenza in hospitalized older adults","volume":"31","author":"HK Talbot","year":"2010","journal-title":"Infection control and hospital epidemiology"},{"issue":"9","key":"pone.0246582.ref005","doi-asserted-by":"crossref","first-page":"2829","DOI":"10.1007\/s11606-020-05949-1","article-title":"Late Diagnosis of COVID-19 in Patients Admitted to the Hospital","volume":"35","author":"ER Pfoh","year":"2020","journal-title":"J Gen Intern Med"},{"key":"pone.0246582.ref006","doi-asserted-by":"crossref","first-page":"e38","DOI":"10.1056\/NEJMp2015897","article-title":"False Negative Tests for SARS-CoV-2 Infection\u2014Challenges and Implications","volume":"383","author":"S Woloshin","year":"2020","journal-title":"N Engl J Med"},{"issue":"2","key":"pone.0246582.ref007","doi-asserted-by":"crossref","first-page":"00324","DOI":"10.1183\/23120541.00324-2020","article-title":"False-negative RT-PCR in SARS-CoV-2 disease: experience from an Italian COVID-19 unit","volume":"6","author":"M Di Paolo","year":"2020","journal-title":"ERJ Open Res"},{"issue":"3","key":"pone.0246582.ref008","first-page":"270","article-title":"PCR versus serology for diagnosing Mycoplasma pneumoniae infection: A systematic review & meta-analysis","volume":"134","author":"L Zhang","year":"2011","journal-title":"Indian J Med Res"},{"key":"pone.0246582.ref009","unstructured":"Rapid Influenza Diagnostic Tests. CDC, Atlanta. Retrieved from: https:\/\/www.cdc.gov\/flu\/professionals\/diagnosis\/clinician_guidance_ridt.htm Page last reviewed: January 26, 2021."},{"issue":"7","key":"pone.0246582.ref010","doi-asserted-by":"crossref","first-page":"1848","DOI":"10.1148\/rg.2020200159","article-title":"Chest CT in COVID-19: What the Radiologist Needs to Know","volume":"40","author":"TC Kwee","year":"2020","journal-title":"RadioGraphics"},{"issue":"2","key":"pone.0246582.ref011","doi-asserted-by":"crossref","first-page":"E115","DOI":"10.1148\/radiol.2020200432","article-title":"Sensitivity of Chest CT for COVID-19: Comparison to RT-PCR","volume":"296","author":"Y Fang","year":"2020","journal-title":"Radiology"},{"issue":"2","key":"pone.0246582.ref012","doi-asserted-by":"crossref","first-page":"E32","DOI":"10.1148\/radiol.2020200642","article-title":"Correlation of Chest CT and RT-PCR Testing for Coronavirus Disease 2019 (COVID-19) in China: A Report of 1014 Cases","volume":"296","author":"T Ai","year":"2020","journal-title":"Radiology"},{"issue":"2","key":"pone.0246582.ref013","doi-asserted-by":"crossref","first-page":"E41","DOI":"10.1148\/radiol.2020200343","article-title":"Chest CT for Typical Coronavirus Disease 2019 (COVID-19) Pneumonia: Relationship to Negative RT-PCR Testing","volume":"296","author":"X Xie","year":"2020","journal-title":"Radiology"},{"issue":"2","key":"pone.0246582.ref014","doi-asserted-by":"crossref","first-page":"E65","DOI":"10.1148\/radiol.2020200905","article-title":"Using Artificial Intelligence to Detect COVID-19 and Community-acquired Pneumonia Based on Pulmonary CT: Evaluation of the Diagnostic Accuracy","volume":"296","author":"L Li","year":"2020","journal-title":"Radiology"},{"key":"pone.0246582.ref015","doi-asserted-by":"crossref","first-page":"520","DOI":"10.2214\/AJR.18.20624","article-title":"Texture Analysis of Imaging: What Radiologists Need to Know","volume":"212","author":"BA Varghese","year":"2019","journal-title":"AJR"},{"key":"pone.0246582.ref016","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1186\/s40644-019-0239-z","article-title":"Clinical impact of variability on CT radiomics and suggestions for suitable feature selection: a focus on lung cancer","volume":"19","author":"S Lee","year":"2019","journal-title":"Cancer Imaging"},{"issue":"1","key":"pone.0246582.ref017","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1016\/j.crad.2018.08.014","article-title":"Differentiation of focal organising pneumonia and peripheral adenocarcinoma in solid lung lesions using thin-section CT-based radiomics","volume":"74","author":"T Zhang","year":"2019","journal-title":"Clin Radiol"},{"issue":"2","key":"pone.0246582.ref018","doi-asserted-by":"crossref","first-page":"300","DOI":"10.1097\/RCT.0000000000000836","article-title":"Radiomics for Classifying Histological Subtypes of Lung Cancer Based on Multiphasic Contrast-Enhanced Computed Tomography","volume":"43","author":"E Linning","year":"2019","journal-title":"J Comput Assist Tomogr"},{"key":"pone.0246582.ref019","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1016\/j.ejrad.2019.01.021","article-title":"Development and validation of a radiomics nomogram for identifying invasiveness of pulmonary adenocarcinomas appearing as subcentimeter ground-glass opacity nodules","volume":"112","author":"W Zhao","year":"2019","journal-title":"Eur J Radiol"},{"key":"pone.0246582.ref020","doi-asserted-by":"crossref","DOI":"10.1007\/s00330-020-07012-3","article-title":"Identification of common and severe COVID-19: the value of CT texture analysis and correlation with clinical characteristics","author":"W Wei","year":"2020","journal-title":"Eur Radiol"},{"issue":"4","key":"pone.0246582.ref021","article-title":"CT Radiomics, Radiologists and Clinical Information in Predicting Outcome of Patients with COVID-19 Pneumonia","volume":"2","author":"F Homayounieh","year":"2020","journal-title":"Radiology: Cardiothoracic Imaging"},{"key":"pone.0246582.ref022","first-page":"1","article-title":"Radiomics nomogram for the prediction of 2019 novel coronavirus pneumonia caused by SARS-CoV-2","volume":"3","author":"X Fang","year":"2020","journal-title":"Eur Radiol"},{"key":"pone.0246582.ref023","unstructured":"Radiomic features. 2016; Pyradiomics community. Retrieved from: https:\/\/pyradiomics.readthedocs.io\/en\/latest\/features.html. Page last reviewed: January 26, 2021."},{"issue":"3","key":"pone.0246582.ref024","doi-asserted-by":"crossref","first-page":"719","DOI":"10.1148\/rg.2018170048","article-title":"Radiographic and CT Features of Viral Pneumonia","volume":"38","author":"HJ Koo","year":"2018","journal-title":"Radiographics"},{"key":"pone.0246582.ref025","doi-asserted-by":"crossref","first-page":"572","DOI":"10.1186\/s12879-017-2677-1","article-title":"Secondary organizing pneumonia following viral pneumonia caused by severe influenza B: a case report and literature reviews","volume":"17","author":"N Asai","year":"2017","journal-title":"BMC Infect Dis"},{"key":"pone.0246582.ref026","doi-asserted-by":"crossref","first-page":"301","DOI":"10.5152\/dir.2019.20176","article-title":"CT findings of COVID-19 in follow-up: comparison between progression and recovery","volume":"26","author":"CS Guan","year":"2020","journal-title":"Diagn Interv Radiol"},{"issue":"6","key":"pone.0246582.ref027","doi-asserted-by":"crossref","first-page":"1488","DOI":"10.2214\/AJR.09.3599","article-title":"Chest radiographic and CT findings in novel swine-origin influenza A (H1N1) virus (S-OIV) infection","volume":"193","author":"PP Agarwal","year":"2009","journal-title":"AJR Am J Roentgenol"},{"key":"pone.0246582.ref028","doi-asserted-by":"crossref","first-page":"S137","DOI":"10.1148\/radiographics.22.suppl_1.g02oc15s137","article-title":"Viral pneumonias in adults: radiologic and pathologic findings","volume":"22","author":"EA Kim","year":"2002","journal-title":"Radiographics"},{"key":"pone.0246582.ref029","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1148\/radiol.2019182023","article-title":"Validation of a Method to Compensate Multicenter Effects Affecting CT Radiomics","volume":"291","author":"F Orlhac","year":"2019","journal-title":"Radiology"}],"container-title":["PLOS ONE"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0246582","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,3,10]],"date-time":"2021-03-10T18:46:26Z","timestamp":1615401986000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0246582"}},"subtitle":[],"editor":[{"given":"Domokos","family":"M\u00e1th\u00e9","sequence":"first","affiliation":[],"role":[{"role":"editor","vocabulary":"crossref"}]}],"short-title":[],"issued":{"date-parts":[[2021,3,10]]},"references-count":29,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2021,3,10]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pone.0246582","relation":{},"ISSN":["1932-6203"],"issn-type":[{"value":"1932-6203","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,3,10]]}}}