{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T22:30:31Z","timestamp":1790375431049,"version":"4.1.0"},"reference-count":34,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2021,1,7]],"date-time":"2021-01-07T00:00:00Z","timestamp":1609977600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2021,1,7]],"date-time":"2021-01-07T00:00:00Z","timestamp":1609977600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Special Research Program of Shanghai Municipal Commission of Heath and Family Planning on medical intelligence","award":["No.2018ZHYL0108"],"award-info":[{"award-number":["No.2018ZHYL0108"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No. 81901695"],"award-info":[{"award-number":["No. 81901695"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Shanghai Sailing Program","award":["No.19YF1433100"],"award-info":[{"award-number":["No.19YF1433100"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Transl Med"],"published-print":{"date-parts":[[2021,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Limited data was available for rapid and accurate detection of COVID-19 using CT-based machine learning model. This study aimed to investigate the value of chest CT radiomics for diagnosing COVID-19 pneumonia compared with clinical model and COVID-19 reporting and data system (CO-RADS), and develop an open-source diagnostic tool with the constructed radiomics model.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This study enrolled 115 laboratory-confirmed COVID-19 and 435 non-COVID-19 pneumonia patients (training dataset, n\u2009=\u2009379; validation dataset, n\u2009=\u2009131; testing dataset, n\u2009=\u200940). Key radiomics features extracted from chest CT images were selected to build a radiomics signature using least absolute shrinkage and selection operator (LASSO) regression. Clinical and clinico-radiomics combined models were constructed. The combined model was further validated in the viral pneumonia cohort, and compared with performance of two radiologists using CO-RADS. The diagnostic performance was assessed by receiver operating characteristics curve (ROC) analysis, calibration curve, and decision curve analysis (DCA).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Eight radiomics features and 5 clinical variables were selected to construct the combined radiomics model, which outperformed the clinical model in diagnosing COVID-19 pneumonia with an area under the ROC (AUC) of 0.98 and good calibration in the validation cohort. The combined model also performed better in distinguishing COVID-19 from other viral pneumonia with an AUC of 0.93 compared with 0.75 (\n                      <jats:italic>P<\/jats:italic>\n                      \u2009=\u20090.03) for clinical model, and 0.69 (\n                      <jats:italic>P<\/jats:italic>\n                      \u2009=\u20090.008) or 0.82 (\n                      <jats:italic>P<\/jats:italic>\n                      \u2009=\u20090.15) for two trained radiologists using CO-RADS. The sensitivity and specificity of the combined model can be achieved to 0.85 and 0.90. The DCA confirmed the clinical utility of the combined model. An easy-to-use open-source diagnostic tool was developed using the combined model.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The combined radiomics model outperformed clinical model and CO-RADS for diagnosing COVID-19 pneumonia, which can facilitate more rapid and accurate detection.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s12967-020-02692-3","type":"journal-article","created":{"date-parts":[[2021,1,7]],"date-time":"2021-01-07T13:04:01Z","timestamp":1610024641000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":44,"title":["CT radiomics facilitates more accurate diagnosis of COVID-19 pneumonia: compared with CO-RADS"],"prefix":"10.1186","volume":"19","author":[{"given":"Huanhuan","family":"Liu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hua","family":"Ren","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zengbin","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"He","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuhai","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinning","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liang","family":"Hou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Runmin","family":"Chi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hui","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanhong","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shaofeng","family":"Duan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huimin","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zongyu","family":"Xie","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9529-4870","authenticated-orcid":false,"given":"Dengbin","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,1,7]]},"reference":[{"key":"2692_CR1","unstructured":"World Health Organization. WHO Director-General's remarks at the media briefing on 2019-nCoV on 11 February 2020; 2020. https:\/\/www.who.int\/dg\/speeches\/detail\/who-director-general-s-remarks-at-the-media-briefing-on-2019-ncov-on-11-february-2020. Accessed 11 Feb 2020."},{"key":"2692_CR2","unstructured":"World Health Organization. WHO Director-General's opening remarks at the media briefing on COVID-19\u201411 March 2020. https:\/\/www.who.int\/dg\/speeches\/detail\/who-director-general-s-opening-remarks-at-the-media-briefing-on-covid-19-11-march-2020. Accessed 11 Mar 2020."},{"key":"2692_CR3","unstructured":"Johns Hopkinds University & Medicine, C.R.C.; 2020. https:\/\/coronavirus.jhu.edu. Accessed 5 Sept 2020."},{"key":"2692_CR4","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2020200642","author":"T Ai","year":"2020","unstructured":"Ai T, Yang Z, Hou H, et al. Correlation of chest CT and RT-PCR testing in coronavirus disease 2019 (COVID-19) in China: a report of 1014 cases. Radiology. 2020. https:\/\/doi.org\/10.1148\/radiol.2020200642.","journal-title":"Radiology"},{"key":"2692_CR5","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2020200432","author":"Y Fang","year":"2020","unstructured":"Fang Y, Zhang H, Xie J, et al. Sensitivity of chest CT for COVID-19: comparison to RT-PCR. Radiology. 2020. https:\/\/doi.org\/10.1148\/radiol.2020200432.","journal-title":"Radiology"},{"key":"2692_CR6","doi-asserted-by":"publisher","first-page":"210","DOI":"10.1148\/radiol.2020200274","volume":"295","author":"F Song","year":"2020","unstructured":"Song F, Shi N, Shan F, et al. Emerging coronavirus 2019-nCoV pneumonia. Radiology. 2020;295:210\u20137.","journal-title":"Radiology"},{"key":"2692_CR7","doi-asserted-by":"publisher","first-page":"425","DOI":"10.1016\/S1473-3099(20)30086-4","volume":"20","author":"H Shi","year":"2020","unstructured":"Shi H, Han X, Jiang N, et al. Radiological findings from 81 patients with COVID-19 pneumonia in Wuhan, China: a descriptive study. Lancet Infect Dis. 2020;20:425\u201334.","journal-title":"Lancet Infect Dis"},{"key":"2692_CR8","doi-asserted-by":"publisher","first-page":"715","DOI":"10.1148\/radiol.2020200370","volume":"295","author":"F Pan","year":"2020","unstructured":"Pan F, Ye T, Sun P, et al. Time course of lung changes on chest CT during recovery from 2019 novel coronavirus (COVID-19) pneumonia. Radiology. 2020;295:715\u201321.","journal-title":"Radiology"},{"key":"2692_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.chest.2020.04.003","author":"GD Rubin","year":"2020","unstructured":"Rubin GD, Ryerson CJ, Haramati LB, et al. The role of chest imaging in patient management during the COVID-19 pandemic: a multinational consensus statement from the Fleischner Society. Radiology. 2020. https:\/\/doi.org\/10.1016\/j.chest.2020.04.003.","journal-title":"Radiology"},{"key":"2692_CR10","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2020200823","author":"HX Bai","year":"2020","unstructured":"Bai HX, Hsieh B, Xiong Z, et al. Performance of radiologists in differentiating COVID-19 from viral pneumonia on chest CT. Radiology. 2020. https:\/\/doi.org\/10.1148\/radiol.2020200823.","journal-title":"Radiology"},{"key":"2692_CR11","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2020201473","author":"M Prokop","year":"2020","unstructured":"Prokop M, van Everdingen W, van Rees VT, et al. CO-RADS\u2014a categorical CT assessment scheme for patients with suspected COVID-19: definition and evaluation. Radiology. 2020. https:\/\/doi.org\/10.1148\/radiol.2020201473.","journal-title":"Radiology"},{"key":"2692_CR12","doi-asserted-by":"publisher","first-page":"4930","DOI":"10.1007\/s00330-020-06863-0","volume":"30","author":"S Salehi","year":"2020","unstructured":"Salehi S, Abedi A, Balakrishnan S, Gholamrezanezhad A. Coronavirus disease 2019 (COVID-19) imaging reporting and data system (COVID-RADS) and common lexicon: a proposal based on the imaging data of 37 studies. Eur Radiol. 2020;30:4930\u201342.","journal-title":"Eur Radiol"},{"key":"2692_CR13","doi-asserted-by":"publisher","unstructured":"Guiot J, Vaidyanathan A, Deprez L, et al. Development and validation of an automated radiomic CT signature for detecting COVID-19. medRxiv. 2020. https:\/\/doi.org\/10.1101\/2020.04.28.20082966","DOI":"10.1101\/2020.04.28.20082966"},{"key":"2692_CR14","doi-asserted-by":"publisher","first-page":"7253","DOI":"10.1158\/1078-0432.CCR-17-1038","volume":"23","author":"Z Liu","year":"2017","unstructured":"Liu Z, Zhang XY, Shi YJ, et al. Radiomics analysis for evaluation of pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer. Clin Cancer Res. 2017;23:7253\u201362.","journal-title":"Clin Cancer Res"},{"key":"2692_CR15","doi-asserted-by":"publisher","first-page":"593","DOI":"10.3389\/fonc.2020.00593","volume":"10","author":"BT Chen","year":"2020","unstructured":"Chen BT, Chen Z, Ye N, et al. Differentiating peripherally-located small cell lung cancer from non-small cell lung cancer using a CT radiomic approach. Front Oncol. 2020;10:593.","journal-title":"Front Oncol"},{"key":"2692_CR16","doi-asserted-by":"publisher","first-page":"4670","DOI":"10.1007\/s00330-019-06026-w","volume":"29","author":"Y Liu","year":"2019","unstructured":"Liu Y, Dong D, Zhang L, et al. Radiomics in multiple sclerosis and neuromyelitis optica spectrum disorder. Eur Radiol. 2019;29:4670\u20137.","journal-title":"Eur Radiol"},{"key":"2692_CR17","doi-asserted-by":"publisher","first-page":"1067","DOI":"10.1148\/radiol.2363040958","volume":"236","author":"YC Chang","year":"2005","unstructured":"Chang YC, Yu CJ, Chang SC, et al. Pulmonary sequelae in convalescent patients after severe acute respiratory syndrome: evaluation with thin-section CT. Radiology. 2005;236:1067\u201375.","journal-title":"Radiology"},{"key":"2692_CR18","doi-asserted-by":"publisher","first-page":"e104","DOI":"10.1158\/0008-5472.CAN-17-0339","volume":"77","author":"JJM van Griethuysen","year":"2017","unstructured":"van Griethuysen JJM, Fedorov A, Parmar C, et al. Computational radiomics system to decode the radiographic phenotype. Cancer Res. 2017;77:e104\u20137.","journal-title":"Cancer Res"},{"key":"2692_CR19","first-page":"1256","volume":"43","author":"X Chen","year":"2020","unstructured":"Chen X, Feng B, Li C, et al. A radiomics model to predict the invasiveness of thymic epithelial tumors based on contrast-enhanced computed tomography. Oncol Rep. 2020;43:1256\u201366.","journal-title":"Oncol Rep"},{"key":"2692_CR20","doi-asserted-by":"publisher","first-page":"837","DOI":"10.2307\/2531595","volume":"44","author":"ER DeLong","year":"1988","unstructured":"DeLong ER, DeLong DM, Clarke-Pearson DL. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics. 1988;44:837\u201345.","journal-title":"Biometrics"},{"key":"2692_CR21","doi-asserted-by":"publisher","first-page":"1061","DOI":"10.1001\/jama.2020.1585","volume":"323","author":"D Wang","year":"2020","unstructured":"Wang D, Hu B, Hu C, et al. Clinical characteristics of 138 hospitalized patients with 2019 novel coronavirus-infected pneumonia in Wuhan. China JAMA. 2020;323:1061\u20139.","journal-title":"China JAMA"},{"key":"2692_CR22","doi-asserted-by":"publisher","first-page":"507","DOI":"10.1016\/S0140-6736(20)30211-7","volume":"395","author":"N Chen","year":"2020","unstructured":"Chen N, Zhou M, Dong X, et al. Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study. Lancet. 2020;395:507\u201313.","journal-title":"Lancet"},{"key":"2692_CR23","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2020200230","author":"M Chung","year":"2020","unstructured":"Chung M, Bernheim A, Mei X, et al. CT imaging features of 2019 novel coronavirus (2019-nCoV). Radiology. 2020. https:\/\/doi.org\/10.1148\/radiol.2020200230.","journal-title":"Radiology"},{"key":"2692_CR24","doi-asserted-by":"publisher","first-page":"3306","DOI":"10.1007\/s00330-020-06731-x","volume":"30","author":"Y Pan","year":"2020","unstructured":"Pan Y, Guan H, Zhou S, et al. Initial CT findings and temporal changes in patients with the novel coronavirus pneumonia (2019-nCoV): a study of 63 patients in Wuhan China. Eur Radiol. 2020;30:3306\u20139.","journal-title":"Eur Radiol"},{"key":"2692_CR25","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2020201491","author":"HX Bai","year":"2020","unstructured":"Bai HX, Wang R, Xiong Z, et al. AI augmentation of radiologist performance in distinguishing COVID-19 from pneumonia of other etiology on chest CT. Radiology. 2020. https:\/\/doi.org\/10.1148\/radiol.2020201491.","journal-title":"Radiology"},{"key":"2692_CR26","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2020200905","author":"L Li","year":"2020","unstructured":"Li L, Qin L, Xu Z, et al. Artificial intelligence distinguishes COVID-19 from community acquired pneumonia on Chest CT. Radiology. 2020. https:\/\/doi.org\/10.1148\/radiol.2020200905.","journal-title":"Radiology"},{"key":"2692_CR27","doi-asserted-by":"publisher","DOI":"10.1101\/2020.02.14.20023028","author":"S Wang","year":"2020","unstructured":"Wang S, Kang B, Ma J, et al. A deep learning algorithm using CT images to screen for corona virus disease (COVID-19). medRxiv. 2020. https:\/\/doi.org\/10.1101\/2020.02.14.20023028.","journal-title":"medRxiv"},{"key":"2692_CR28","doi-asserted-by":"publisher","first-page":"106897","DOI":"10.1016\/j.asoc.2020.106897","volume":"98","author":"B Wang","year":"2021","unstructured":"Wang B, Jin S, Yan Q, et al. AI-assisted CT imaging analysis for COVID-19 screening: building and deploying a medical AI system. Appl Soft Comput. 2021;98:106897.","journal-title":"Appl Soft Comput"},{"key":"2692_CR29","unstructured":"Yan Q, Wang B, Gong D, et al. COVID-19 chest CT image segmentation\u2014a deep convolutional neural network solution. arXiv 2020; 2004.10987."},{"key":"2692_CR30","doi-asserted-by":"publisher","first-page":"1948","DOI":"10.1007\/s00330-019-06572-3","volume":"30","author":"Y Cui","year":"2020","unstructured":"Cui Y, Liu H, Ren J, et al. Development and validation of a MRI-based radiomics signature for prediction of KRAS mutation in rectal cancer. Eur Radiol. 2020;30:1948\u201358.","journal-title":"Eur Radiol"},{"key":"2692_CR31","doi-asserted-by":"publisher","first-page":"4418","DOI":"10.1007\/s00330-018-5802-7","volume":"29","author":"H Liu","year":"2019","unstructured":"Liu H, Zhang C, Wang L, et al. MRI radiomics analysis for predicting preoperative synchronous distant metastasis in patients with rectal cancer. Eur Radiol. 2019;29:4418\u201326.","journal-title":"Eur Radiol"},{"key":"2692_CR32","doi-asserted-by":"publisher","first-page":"719","DOI":"10.1148\/rg.2018170048","volume":"38","author":"HJ Koo","year":"2018","unstructured":"Koo HJ, Lim S, Choe J, Choi SH, Sung H, Do KH. Radiographic and CT features of viral pneumonia. Radiographics. 2018;38:719\u201339.","journal-title":"Radiographics"},{"key":"2692_CR33","doi-asserted-by":"publisher","first-page":"563","DOI":"10.1148\/radiol.2015151169","volume":"278","author":"RJ Gillies","year":"2016","unstructured":"Gillies RJ, Kinahan PE, Hricak H. Radiomics: Images are more than pictures, they are data. Radiology. 2016;278:563\u201377.","journal-title":"Radiology"},{"key":"2692_CR34","doi-asserted-by":"publisher","first-page":"749","DOI":"10.1038\/nrclinonc.2017.141","volume":"14","author":"P Lambin","year":"2017","unstructured":"Lambin P, Leijenaar RTH, Deist TM, et al. Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol. 2017;14:749\u201362.","journal-title":"Nat Rev Clin Oncol"}],"container-title":["Journal of Translational Medicine"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s12967-020-02692-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1186\/s12967-020-02692-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s12967-020-02692-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,1,7]],"date-time":"2021-01-07T14:14:12Z","timestamp":1610028852000},"score":1,"resource":{"primary":{"URL":"https:\/\/translational-medicine.biomedcentral.com\/articles\/10.1186\/s12967-020-02692-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,1,7]]},"references-count":34,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2021,12]]}},"alternative-id":["2692"],"URL":"https:\/\/doi.org\/10.1186\/s12967-020-02692-3","relation":{"has-preprint":[{"id-type":"doi","id":"10.21203\/rs.3.rs-76981\/v1","asserted-by":"object"}]},"ISSN":["1479-5876"],"issn-type":[{"value":"1479-5876","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,1,7]]},"assertion":[{"value":"12 September 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 December 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 January 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The Institutional Ethics Committee of Xinhua Hospital affiliated to Shanghai Jiao Tong University School of Medicine\u00a0approved this retrospective study and waived the informed consent requirement.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Consent for publication was obtained.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"One of the authors (S.F.D) is an employee of GE Healthcare. The remaining authors of this manuscript declare no relationships with any companies whose products or services may be related to the subject matter of the article.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"29"}}