{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T04:02:48Z","timestamp":1774411368453,"version":"3.50.1"},"reference-count":32,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2021,2,18]],"date-time":"2021-02-18T00:00:00Z","timestamp":1613606400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,2,18]],"date-time":"2021-02-18T00:00:00Z","timestamp":1613606400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"name":"Foxconn Brazil and Zerbini Foundation"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Digit Imaging"],"published-print":{"date-parts":[[2021,4]]},"DOI":"10.1007\/s10278-021-00421-w","type":"journal-article","created":{"date-parts":[[2021,2,19]],"date-time":"2021-02-19T18:13:46Z","timestamp":1613758426000},"page":"297-307","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Novel Chest Radiographic Biomarkers for COVID-19 Using Radiomic Features Associated with Diagnostics and Outcomes"],"prefix":"10.1007","volume":"34","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8202-588X","authenticated-orcid":false,"given":"Jos\u00e9 Raniery","family":"Ferreira Junior","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Diego Armando","family":"Cardona Cardenas","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ramon Alfredo","family":"Moreno","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Marina de F\u00e1tima","family":"de S\u00e1 Rebelo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jos\u00e9 Eduardo","family":"Krieger","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Marco Antonio","family":"Gutierrez","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,2,18]]},"reference":[{"issue":"3","key":"421_CR1","doi-asserted-by":"publisher","first-page":"e0230548","DOI":"10.1371\/journal.pone.0230548","volume":"15","author":"M Yuan","year":"2020","unstructured":"Yuan M, Yin W, Tao Z, Tan W, and Hu Y: Association of radiologic findings with mortality of patients infected with 2019 novel coronavirus in Wuhan, China. PloS One, 15(3):e0230548, 2020","journal-title":"China. PloS One"},{"issue":"5","key":"421_CR2","doi-asserted-by":"publisher","first-page":"603","DOI":"10.1016\/j.acra.2020.03.003","volume":"27","author":"M Li","year":"2020","unstructured":"Li M, Lei P, Zeng B, Li Z, Yu P, Fan B, Wang C, Li Z, Zhou J, Hu S, et\u00a0al: Coronavirus disease (COVID-19): spectrum of CT findings and temporal progression of the disease. Acad Radiol, 27(5):603\u2013608, 2020","journal-title":"Acad Radiol"},{"key":"421_CR3","doi-asserted-by":"crossref","unstructured":"Liu K, Xu P, Lv WF, Qiu XH, Yao JL, Jin-Feng G, et\u00a0al: CT manifestations of coronavirus disease-2019: a retrospective analysis of 73 cases by disease severity. Eur J Radiol, 108941, 2020","DOI":"10.1016\/j.ejrad.2020.108941"},{"issue":"10223","key":"421_CR4","doi-asserted-by":"publisher","first-page":"497","DOI":"10.1016\/S0140-6736(20)30183-5","volume":"395","author":"C Huang","year":"2020","unstructured":"Huang C, Wang Y, Li X, Ren L, Zhao J, Hu Y, Zhang L, Fan G, Xu J, Gu X, et\u00a0al: Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. The Lancet, 395(10223):497\u2013506, 2020","journal-title":"China. The Lancet"},{"key":"421_CR5","doi-asserted-by":"crossref","unstructured":"Fang Y, Zhang H, Xie J, Lin M, Ying L, Pang P, and Ji W: Sensitivity of chest CT for COVID-19: comparison to RT-PCR. Radiology, 200432, 2020","DOI":"10.1148\/radiol.2020200432"},{"key":"421_CR6","doi-asserted-by":"crossref","unstructured":"Bai HX, Hsieh B, Xiong Z, Halsey K, Choi JW, Tran TML, Pan I, Shi LB, Wang DC, Mei J, et\u00a0al: Performance of radiologists in differentiating COVID-19 from viral pneumonia on chest CT. Radiology, 200823, 2020","DOI":"10.1148\/radiol.2020200823"},{"issue":"5","key":"421_CR7","doi-asserted-by":"publisher","first-page":"609","DOI":"10.1016\/j.acra.2020.03.002","volume":"7","author":"CS Guan","year":"2020","unstructured":"Guan CS, Lv ZB, Yan S, Du YN, Chen H, Wei LG, Xie RM, and Chen BD: Imaging features of coronavirus disease 2019 (COVID-19): evaluation on thin-section CT. Acad Radiol, 7(5):609\u2013613, 2020","journal-title":"Acad Radiol"},{"key":"421_CR8","doi-asserted-by":"crossref","unstructured":"Ng MY, Lee EY, Yang J, Yang F, Li X, Wang H, Lui MMS, Lo CSY, Leung B, Khong PL, et\u00a0al: Imaging profile of the COVID-19 infection: radiologic findings and literature review. Radiology: Cardiothoracic Imaging, 2(1):e200034, 2020","DOI":"10.1148\/ryct.2020200034"},{"issue":"1","key":"421_CR9","doi-asserted-by":"publisher","first-page":"163","DOI":"10.1007\/s11548-019-02093-y","volume":"15","author":"JR Ferreira-Junior","year":"2020","unstructured":"Ferreira-Junior JR, Koenigkam-Santos M, Ten\u00f3rio APM, Faleiros MC, Cipriano FEG,\u00a0 Fabro AT, N\u00e4ppi J, Yoshida H, and de\u00a0Azevedo-Marques PM: CT-based radiomics for prediction of histologic subtype and metastatic disease in primary malignant lung neoplasms. Int J Comput Assist Radiol Surg, 15(1):163\u2013172, 2020","journal-title":"Int J Comput Assist Radiol Surg"},{"issue":"6","key":"421_CR10","doi-asserted-by":"publisher","first-page":"833","DOI":"10.1016\/j.acra.2018.11.006","volume":"26","author":"AJ Degnan","year":"2019","unstructured":"Degnan AJ, Ghobadi EH, Hardy P, Krupinski E, Scali EP, Stratchko L, Ulano A, Walker E, Wasnik AP, and Auffermann WF: Perceptual and interpretive error in diagnostic radiology causes and potential solutions. Acad Radiol, 26(6):833\u2013845, 2019","journal-title":"Acad Radiol"},{"key":"421_CR11","doi-asserted-by":"crossref","unstructured":"Santos MK, Ferreira\u00a0J\u00fanior JR, Wada DT, Ten\u00f3rio APM, Barbosa MNH, and Marques PMDA: Artificial intelligence, machine learning, computer-aided diagnosis, and radiomics: advances in imaging towards to precision medicine. Radiologia Brasileira, 52(6):387\u2013396, 2019","DOI":"10.1590\/0100-3984.2019.0049"},{"issue":"1","key":"421_CR12","first-page":"1","volume":"5","author":"HJ Aerts","year":"2014","unstructured":"Aerts HJ, Velazquez ER, Leijenaar RT, Parmar C, Grossmann P, Carvalho S, Bussink J, Monshouwer R, Haibe-Kains B, Rietveld D, et\u00a0al: Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun, 5(1):1\u20139, 2014","journal-title":"Nat Commun"},{"issue":"1","key":"421_CR13","doi-asserted-by":"publisher","first-page":"26","DOI":"10.1097\/RTI.0000000000000268","volume":"33","author":"M Kolossv\u00e1ry","year":"2018","unstructured":"Kolossv\u00e1ry M, Kellermayer M, Merkely B, and Maurovich-Horvat P: Cardiac computed tomography radiomics. J\u00a0Thorac Imaging,\u00a033(1):26\u201334, 2018","journal-title":"J Thorac Imaging"},{"issue":"2","key":"421_CR14","doi-asserted-by":"publisher","first-page":"563","DOI":"10.1148\/radiol.2015151169","volume":"278","author":"RJ Gillies","year":"2016","unstructured":"Gillies RJ, Kinahan PE, and Hricak H: Radiomics: images are more than pictures, they are data. Radiology, 278(2):563\u2013577, 2016","journal-title":"Radiology"},{"key":"421_CR15","doi-asserted-by":"crossref","unstructured":"Ferreira\u00a0Junior JR, Koenigkam-Santos M, Machado CVB, Faleiros MC, Correia NSC,\u00a0 Cipriano FEG, Fabro AT, and de\u00a0Azevedo-Marques PM: Radiomics analysis of lung cancer for patient prognosis and intratumor heterogeneity assessment. Radiologia Brasileira, Accepted for publication, 2020","DOI":"10.1590\/0100-3984.2019.0135"},{"key":"421_CR16","doi-asserted-by":"crossref","unstructured":"Van\u00a0Griethuysen JJ, Fedorov A, Parmar C, Hosny A, Aucoin N, Narayan V, Beets-Tan RG, Fillion-Robin JC, Pieper S, and Aerts HJ: Computational radiomics system to decode the radiographic phenotype. Cancer Res, 77(21):e104\u2013e107, 2017","DOI":"10.1158\/0008-5472.CAN-17-0339"},{"key":"421_CR17","unstructured":"Italian Society of Medical and Interventional Radiology. COVID-19 Database. Online; last acess on March 24, 2020. Available at www.sirm.org\/category\/senza-categoria\/covid-19\/, 2020"},{"key":"421_CR18","doi-asserted-by":"crossref","unstructured":"Bustos A, Pertusa A, Salinas JM, and de\u00a0la Iglesia-Vay\u00e1 M: Padchest: A large chest x-ray image dataset with multi-label annotated reports. arXiv preprint arXiv:1901.07441, 2019.","DOI":"10.1016\/j.media.2020.101797"},{"issue":"2","key":"421_CR19","doi-asserted-by":"publisher","first-page":"304","DOI":"10.1093\/jamia\/ocv080","volume":"23","author":"D Demner-Fushman","year":"2016","unstructured":"Demner-Fushman D, Kohli MD, Rosenman MB, Shooshan SE, Rodriguez L, Antani S, Thoma GR, and McDonald CJ: Preparing a collection of radiology examinations for distribution and retrieval. J Am Med Inform Assoc, 23(2):304\u2013310, 2016","journal-title":"J Am Med Inform Assoc"},{"key":"421_CR20","unstructured":"Cohen JP, Morrison P, and Dao L: COVID-19 image data collection. Online; last acess on March 24, 2020. Available at https:\/\/github.com\/ieee8023\/covid-chestxray-dataset, 2020"},{"key":"421_CR21","doi-asserted-by":"crossref","unstructured":"Ronneberger O, Fischer P, and Brox T: U-Net: Convolutional networks for biomedical image segmentation. In Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, 234\u201324, 2020","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"421_CR22","unstructured":"Pazhitnykh I, and Petsiuk V: Lung segmentation (2D). Online; last acess on March 24, 2020. Available at https:\/\/github.com\/imlab-uiip\/lung-segmentation-2d, 2017"},{"issue":"2","key":"421_CR23","doi-asserted-by":"publisher","first-page":"328","DOI":"10.1148\/radiol.2020191145","volume":"295","author":"A Zwanenburg","year":"2020","unstructured":"Zwanenburg A, Valli\u00e8res M, Abdalah MA, Aerts HJ, Andrearczyk V, Apte A, Ashrafinia S, Bakas S, Beukinga RJ, Boellaard R, et\u00a0al: The image biomarker standardization initiative: standardized quantitative radiomics for high-throughput image-based phenotyping. Radiology, 295(2):328\u2013338, 2020","journal-title":"Radiology"},{"key":"421_CR24","unstructured":"Li L, Qin L, Xu Z, Yin Y, Wang X, Kong B, Bai J, Lu Y, Fang Z, Song Q, et\u00a0al: Artificial intelligence distinguishes COVID-19 from community acquired pneumonia on chest CT. Radiology, 200905, 2020"},{"key":"421_CR25","unstructured":"Randomised Evaluation of COVid-19 thERapY (RECOVERY) Trial. Low-cost dexamethasone reduces death by up to one third in hospitalised patients with severe respiratory complications of covid-19. Online; last access on June 18, 2020. Available at https:\/\/www.recoverytrial.net\/news\/low-cost-dexamethasone-reduces-death-by-up-to-one-third-in-hospitalised-patients-with-severe-respiratory-complications-of-covid-19, 2020"},{"key":"421_CR26","doi-asserted-by":"crossref","unstructured":"Ai T, Yang Z, Hou H, Zhan C, Chen C, Lv W, Tao Q, Sun Z, and Xia L: Correlation of chest CT and RT-PCR testing in coronavirus disease 2019 (COVID-19) in China: a report of 1014 cases. Radiology, 200642, 2020","DOI":"10.1148\/radiol.2020200642"},{"key":"421_CR27","doi-asserted-by":"crossref","unstructured":"Chung M, Bernheim A, Mei X, Zhang N, Huang M, Zeng X, Cui J, Xu W, Yang Y, Fayad ZA, et\u00a0al: CT imaging features of 2019 novel coronavirus (2019-nCoV). Radiology, 295(1):202\u2013207, 2020","DOI":"10.1148\/radiol.2020200230"},{"key":"421_CR28","doi-asserted-by":"crossref","unstructured":"Moradi B, Ghanaati H, Kazemi MA, Gity M, Hashemi H, Davaritanha F, Chavoshi M, Rouzrokh P, and Kolahdouzan K: Implications of sex difference in CT scan findings and outcome of patients with COVID-19 pneumonia. Radiology: Cardiothoracic Imaging, 2(4):e200248, 2020","DOI":"10.1148\/ryct.2020200248"},{"key":"421_CR29","doi-asserted-by":"publisher","first-page":"2579","DOI":"10.1016\/j.procs.2013.05.444","volume":"18","author":"RT Sousa","year":"2013","unstructured":"Sousa RT, Marques O, Soares FAA, Sene\u00a0Jr II, de\u00a0Oliveira LL, and Spoto ES: Comparative performance analysis of machine learning classifiers in detection of childhood pneumonia using chest radiographs. Procedia Computer Science, 18:2579\u20132582, 2013","journal-title":"Procedia Computer Science"},{"key":"421_CR30","doi-asserted-by":"crossref","unstructured":"Chandra TB, and Verma K. Pneumonia detection on chest x-ray using machine learning paradigm. In Proceedings of 3rd International Conference on Computer Vision and Image Processing, Springer, 21-33, 2020","DOI":"10.1007\/978-981-32-9088-4_3"},{"issue":"5","key":"421_CR31","doi-asserted-by":"publisher","first-page":"1122","DOI":"10.1016\/j.cell.2018.02.010","volume":"172","author":"DS Kermany","year":"2018","unstructured":"Kermany DS, Goldbaum M, Cai W, Valentim CC, Liang H, Baxter SL, McKeown A, Yang G, Wu X, Yan F, et\u00a0al: Identifying medical diagnoses and treatable diseases by image-based deep learning. Cell, 172(5):1122\u20131131, 2018","journal-title":"Cell"},{"key":"421_CR32","doi-asserted-by":"crossref","unstructured":"Liang G, and Zheng L: A transfer learning method with deep residual network for pediatric pneumonia diagnosis. Comput Methods Programs Biomed, 104964, 2020","DOI":"10.1016\/j.cmpb.2019.06.023"}],"container-title":["Journal of Digital Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-021-00421-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10278-021-00421-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-021-00421-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,7,19]],"date-time":"2021-07-19T14:09:49Z","timestamp":1626703789000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10278-021-00421-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,2,18]]},"references-count":32,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2021,4]]}},"alternative-id":["421"],"URL":"https:\/\/doi.org\/10.1007\/s10278-021-00421-w","relation":{},"ISSN":["0897-1889","1618-727X"],"issn-type":[{"value":"0897-1889","type":"print"},{"value":"1618-727X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,2,18]]},"assertion":[{"value":"20 June 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 November 2020","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 January 2021","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 February 2021","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors received research grants from Foxconn Brazil and Zerbini Foundation.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflicts of Interest"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}