Acknowledgement
This study was supported by the Brown COVID-19 Research Seed Award, the Amazon Web Services Diagnostic Development Initiative, RSNA Research Scholar Grant, and National Institutes of Health/National Cancer Institute R03 grant (R03CA249554) to Dr. Harrison X. Bai, as well as NIH grants (CA223358, DK117297, MH120811, and EB022573) to Dr. Yong Fan.
References
- Yang X, Yu Y, Xu J, Shu H, Xia J, Liu H, et al. Clinical course and outcomes of critically ill patients with SARS-CoV-2 pneumonia in Wuhan, China: a single-centered, retrospective, observational study. Lancet Respir Med 2020;8:475-481 https://doi.org/10.1016/S2213-2600(20)30079-5
- Long B, Brady WJ, Koyfman A, Gottlieb M. Cardiovascular complications in COVID-19. Am J Emerg Med 2020;38:1504-1507 https://doi.org/10.1016/j.ajem.2020.04.048
- Sheraton M, Deo N, Kashyap R, Surani S. A review of neurological complications of COVID-19. Cureus 2020;12:e8192
- Lai CC, Ko WC, Lee PI, Jean SS, Hsueh PR. Extra-respiratory manifestations of COVID-19. Int J Antimicrob Agents 2020;56:106024
- Aziz S, Arabi YM, Alhazzani W, Evans L, Citerio G, Fischkoff K, et al. Managing ICU surge during the COVID-19 crisis: rapid guidelines. Intensive Care Med 2020;46:1303-1325 https://doi.org/10.1007/s00134-020-06092-5
- Sun Q, Qiu H, Huang M, Yang Y. Lower mortality of COVID-19 by early recognition and intervention: experience from Jiangsu Province. Ann Intensive Care 2020;10:33
- Zhai P, Ding Y, Wu X, Long J, Zhong Y, Li Y. The epidemiology, diagnosis and treatment of COVID-19. Int J Antimicrob Agents 2020;55:105955
- Wiersinga WJ, Rhodes A, Cheng AC, Peacock SJ, Prescott HC. Pathophysiology, transmission, diagnosis, and treatment of coronavirus disease 2019 (COVID-19): a review. JAMA 2020;324:782-793 https://doi.org/10.1001/jama.2020.12839
- Wang D, Hu B, Hu C, Zhu F, Liu X, Zhang J, et al. Clinical characteristics of 138 hospitalized patients with 2019 novel coronavirus-infected pneumonia in Wuhan, China. JAMA 2020;323:1061-1069 https://doi.org/10.1001/jama.2020.1585
- Li K, Wu J, Wu F, Guo D, Chen L, Fang Z, et al. The clinical and chest CT features associated with severe and critical COVID-19 pneumonia. Invest Radiol 2020;55:327-331 https://doi.org/10.1097/RLI.0000000000000672
- Bai HX, Hsieh B, Xiong Z, Halsey K, Choi JW, Tran TML, et al. Performance of radiologists in differentiating COVID-19 from non-COVID-19 viral pneumonia at chest CT. Radiology 2020;296:E46-E54 https://doi.org/10.1148/radiol.2020200823
- Yang R, Li X, Liu H, Zhen Y, Zhang X, Xiong Q, et al. Chest CT severity score: an imaging tool for assessing severe COVID-19. Radiology: Cardiothoracic Imaging 2020;2:e200047
- Zhao W, Zhong Z, Xie X, Yu Q, Liu J. Relation between chest CT findings and clinical conditions of coronavirus disease (COVID-19) pneumonia: a multicenter study. AJR Am J Roentgenol 2020;214:1072-1077 https://doi.org/10.2214/AJR.20.22976
- Bernheim A, Mei X, Huang M, Yang Y, Fayad ZA, Zhang N, et al. Chest CT findings in coronavirus disease-19 (COVID-19): relationship to duration of infection. Radiology 2020;295:200463
- Bai HX, Wang R, Xiong Z, Hsieh B, Chang K, Halsey K, et al. Artificial intelligence augmentation of radiologist performance in distinguishing COVID-19 from pneumonia of other origin at chest CT. Radiology 2020;296:E156-E165 https://doi.org/10.1148/radiol.2020201491
- Zhang K, Liu X, Shen J, Li Z, Sang Y, Wu X, et al. Clinically applicable AI system for accurate diagnosis, quantitative measurements, and prognosis of COVID-19 pneumonia using computed tomography. Cell 2020;181:1423-1433.e11 https://doi.org/10.1016/j.cell.2020.04.045
- Chassagnon G, Vakalopoulou M, Battistella E, Christodoulidis S, Hoang-Thi TN, Dangeard S, et al. AI-driven quantification, staging and outcome prediction of COVID-19 pneumonia. Med Image Anal 2021;67:101860
- Zheng Y, Xiao A, Yu X, Zhao Y, Lu Y, Li X, et al. Development and validation of a prognostic nomogram based on clinical and CT features for adverse outcome prediction in patients with COVID-19. Korean J Radiol 2020;21:1007-1017 https://doi.org/10.3348/kjr.2020.0485
- Park B, Park J, Lim JK, Shin KM, Lee J, Seo H, et al. Prognostic implication of volumetric quantitative ct analysis in patients with COVID-19: a multicenter study in Daegu, Korea. Korean J Radiol 2020;21:1256-1264 https://doi.org/10.3348/kjr.2020.0567
- Yin X, Min X, Nan Y, Feng Z, Li B, Cai W, et al. Assessment of the severity of coronavirus disease: quantitative computed tomography parameters versus semiquantitative visual score. Korean J Radiol 2020;21:998-1006 https://doi.org/10.3348/kjr.2020.0423
- Sun D, Li X, Guo D, Wu L, Chen T, Fang Z, et al. CT quantitative analysis and its relationship with clinical features for assessing the severity of patients with COVID-19. Korean J Radiol 2020;21:859-868 https://doi.org/10.3348/kjr.2020.0293
- He Y. Missing data analysis using multiple imputation: getting to the heart of the matter. Circ Cardiovasc Qual Outcomes 2010;3:98-105 https://doi.org/10.1161/CIRCOUTCOMES.109.875658
- Xiang Q, Dai X, Deng Y, He C, Wang J, Feng J, et al. Missing value imputation for microarray gene expression data using histone acetylation information. BMC Bioinformatics 2008;9:252
- Chang YC, Yu CJ, Chang SC, Galvin JR, Liu HM, Hsiao CH, et al. Pulmonary sequelae in convalescent patients after severe acute respiratory syndrome: evaluation with thin-section CT. Radiology 2005;236:1067-1075 https://doi.org/10.1148/radiol.2363040958
- Pan F, Ye T, Sun P, Gui S, Liang B, Li L, et al. Time course of lung changes at chest CT during recovery from coronavirus disease 2019 (COVID-19). Radiology 2020;295:715-721 https://doi.org/10.1148/radiol.2020200370
- Zhou S, Wang Y, Zhu T, Xia L. CT features of coronavirus disease 2019 (COVID-19) pneumonia in 62 patients in Wuhan, China. AJR Am J Roentgenol 2020;214:1287-1294 https://doi.org/10.2214/AJR.20.22975
- Zwanenburg A, Leger S, Vallieres M, Lock S. Image biomarker standardization initiative. arXiv preprint 2016;arXiv:1612.07003
- Vallieres M, Freeman CR, Skamene SR, El Naqa I. A radiomics model from joint FDG-PET and MRI texture features for the prediction of lung metastases in soft-tissue sarcomas of the extremities. Phys Med Biol 2015;60:5471-5496 https://doi.org/10.1088/0031-9155/60/14/5471
- Vallieres M, Kay-Rivest E, Perrin LJ, Liem X, Furstoss C, Aerts HJWL, et al. Radiomics strategies for risk assessment of tumour failure in head-and-neck cancer. Sci Rep 2017;7:10117
- Olson RS, Urbanowicz RJ, Andrews PC, Lavender NA, Kidd LC, Moore JH. Automating biomedical data science through treebased pipeline optimization. In: Squillero G, Burelli P, eds. Applications of evolutionary computation. EvoApplications 2016. Lecture notes in computer science. Cham: Springer, 2016:123-137
- Troyanskaya O, Cantor M, Sherlock G, Brown P, Hastie T, Tibshirani R, et al. Missing value estimation methods for DNA microarrays. Bioinformatics 2001;17:520-525 https://doi.org/10.1093/bioinformatics/17.6.520
- Ishwaran H, Kogalur UB, Blackstone EH, Lauer MS. Random survival forests. Ann Appl Stat 2008;2:841-860
- Agresti A, Coull BA. Approximate is better than "exact" for interval estimation of binomial proportions. Am Stat 1998;52:119-126
- Harrell FE Jr, Lee KL, Mark DB. Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Stat Med 1996;15:361-387 https://doi.org/10.1002/(SICI)1097-0258(19960229)15:4<361::AID-SIM168>3.0.CO;2-4
- Heagerty PJ, Zheng Y. Survival model predictive accuracy and ROC curves. Biometrics 2005;61:92-105 https://doi.org/10.1111/j.0006-341X.2005.030814.x
- Kang L, Chen W, Petrick NA, Gallas BD. Comparing two correlated C indices with right-censored survival outcome: a one-shot nonparametric approach. Stat Med 2015;34:685-703 https://doi.org/10.1002/sim.6370
- Blanche P, Dartigues JF, Jacqmin-Gadda H. Estimating and comparing time-dependent areas under receiver operating characteristic curves for censored event times with competing risks. Stat Med 2013;32:5381-5397 https://doi.org/10.1002/sim.5958
- Maves RC, Downar J, Dichter JR, Hick JL, Devereaux A, Geiling JA, et al. Triage of scarce critical care resources in COVID-19: an implementation guide for regional allocation: an expert panel report of the Task Force for Mass Critical Care and the American College of Chest Physicians. Chest 2020;158:212-225 https://doi.org/10.1016/j.chest.2020.03.063
- Million M, Lagier JC, Gautret P, Colson P, Fournier PE, Amrane S, et al. Early treatment of COVID-19 patients with hydroxychloroquine and azithromycin: a retrospective analysis of 1061 cases in Marseille, France. Travel Med Infect Dis 2020;35:101738
- Salazar E, Perez KK, Ashraf M, Chen J, Castillo B, Christensen PA, et al. Treatment of coronavirus disease 2019 (COVID-19) patients with convalescent plasma. Am J Pathol 2020;190:1680-1690 https://doi.org/10.1016/j.ajpath.2020.05.014
- Homayounieh F, Ebrahimian S, Babaei R, Karimi Mobin H, Zhang E, Bizzo BC, et al. CT radiomics, radiologists and clinical information in predicting outcome of patients with COVID-19 pneumonia. Radiology: Cardiothoracic Imaging 2020;2:e200322
- Wu Q, Wang S, Li L, Wu Q, Qian W, Hu Y, et al. Radiomics analysis of computed tomography helps predict poor prognostic outcome in COVID-19. Theranostics 2020;10:7231-7244 https://doi.org/10.7150/thno.46428
- Wei W, Hu XW, Cheng Q, Zhao YM, Ge YQ. Identification of common and severe COVID-19: the value of CT texture analysis and correlation with clinical characteristics. Eur Radiol 2020;30:6788-6796 https://doi.org/10.1007/s00330-020-07012-3
- Wang S, Zha Y, Li W, Wu Q, Li X, Niu M, et al. A fully automatic deep learning system for COVID-19 diagnostic and prognostic analysis. Eur Respir J 2020;56:2000775
- Liu F, Zhang Q, Huang C, Shi C, Wang L, Shi N, et al. CT quantification of pneumonia lesions in early days predicts progression to severe illness in a cohort of COVID-19 patients. Theranostics 2020;10:5613-5622 https://doi.org/10.7150/thno.45985
- Wu C, Chen X, Cai Y, Xia J, Zhou X, Xu S, et al. Risk factors associated with acute respiratory distress syndrome and death in patients with coronavirus disease 2019 pneumonia in Wuhan, China. JAMA Intern Med 2020;18:934-943
- Liang W, Liang H, Ou L, Chen B, Chen A, Li C, et al. Development and validation of a clinical risk score to predict the occurrence of critical illness in hospitalized patients with COVID-19. JAMA Intern Med 2020;180:1081-1089 https://doi.org/10.1001/jamainternmed.2020.2033