L. C. Myers, S. M. Parodi, G. J. Escobar, and V. X. Liu, Characteristics of Hospitalized Adults With COVID-19 in an Integrated Health Care System in California, JAMA, 2020.

A. B. Docherty, Features of 20 133 UK patients in hospital with covid-19 using the ISARIC WHO Clinical Characterisation Protocol: prospective observational cohort study, BMJ, vol.369, p.1985, 2020.

S. Richardson, Presenting Characteristics, Comorbidities, and Outcomes Among 5700 Patients Hospitalized With COVID-19 in the New York City Area, JAMA, 2020.

C. Wu, Risk Factors Associated With Acute Respiratory Distress Syndrome and Death in Patients With Coronavirus Disease, JAMA Intern. Med, 2019.

J. Phua, Intensive care management of coronavirus disease 2019 (COVID-19): challenges and recommendations, Lancet Respir Med, vol.8, pp.506-517, 2020.

W. Liang, 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.

L. Yan, An interpretable mortality prediction model for COVID-19 patients, Nature Machine Intelligence, vol.2, pp.283-288, 2020.

T. J. Levy, Development and Validation of a Survival Calculator for Hospitalized Patients with

D. Ji, Prediction for Progression Risk in Patients with COVID-19 Pneumonia: the CALL Score, Clin. Infect. Dis, 2020.

J. M. Mejia-vilet, A Risk Score to Predict Admission to Intensive Care Unit in Patients With COVID-19: The ABC-GOALS Score, medRxiv, 2020.

L. Wynants, Prediction models for diagnosis and prognosis of covid-19 infection: systematic review and critical appraisal, BMJ, vol.369, p.1328, 2020.

J. Xie, Association Between Hypoxemia and Mortality in Patients With COVID-19

, Mayo Clin. Proc, vol.95, pp.1138-1147, 2020.

F. Zhou, Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study, Lancet, vol.395, pp.1054-1062, 2020.

G. Lippi and M. Plebani, Laboratory abnormalities in patients with COVID-2019 infection, Clin. Chem. Lab. Med, 2020.

D. Colombi, Well-aerated Lung on Admitting Chest CT to Predict Adverse Outcome in COVID-19 Pneumonia, Radiology, 2020.

K. Zhang, Clinically Applicable AI System for Accurate Diagnosis, Quantitative Measurements and Prognosis of COVID-19 Pneumonia Using Computed Tomography

, Cell, 2020.

W. Zhao, Z. Zhong, X. Xie, Q. Yu, and J. Liu, Relation Between Chest CT Findings and Clinical Conditions of Coronavirus Disease (COVID-19) Pneumonia: A Multicenter Study, AJR Am. J. Roentgenol, vol.214, pp.1072-1077, 2020.

E. Taieb, Prognostic value of visual quantification of lesion severity at initial chest CT in confirmed Covid-19 infection: a retrospective analysis on 216 patients, 2020.

J. Wu, Chest CT Findings in Patients With Coronavirus Disease 2019 and Its Relationship With Clinical Features, Invest. Radiol, vol.55, pp.257-261, 2020.

, (ncov)-infection-is-suspected

O. Ronneberger, P. Fischer, T. Brox, and . U-net, Convolutional Networks for Biomedical Image Segmentation. in Medical Image Computing and Computer-Assisted Intervention -MICCAI, 2015.

K. Hara, H. Kataoka, and Y. Satoh, Learning spatio-temporal features with 3D residual networks for action recognition, Proc. IEEE, 2017.

P. Courtiol, Deep learning-based classification of mesothelioma improves prediction of patient outcome, Nat. Med, vol.25, pp.1519-1525, 2019.

E. Williamson, OpenSAFELY: factors associated with COVID-19-related hospital death in the linked electronic health records of 17 million adult NHS patients, MedRxiv, 2020.

W. Liang, Cancer patients in SARS-CoV-2 infection: a nationwide analysis in China, Lancet Oncol, vol.21, pp.335-337, 2020.

H. Miyashita, Do patients with cancer have a poorer prognosis of COVID-19? An experience in, Ann. Oncol, 2020.

M. Dai, Patients with cancer appear more vulnerable to SARS-COV-2: a multi-center study during the COVID-19 outbreak, Cancer Discov, 2020.

B. Wang, R. Li, Z. Lu, and Y. Huang, Does comorbidity increase the risk of patients with COVID-19: evidence from meta-analysis, Aging, vol.12, pp.6049-6057, 2020.

R. Gupta, A. Ghosh, A. K. Singh, and A. Misra, Clinical considerations for patients with diabetes in times of COVID-19 epidemic, Diabetes Metab. Syndr, vol.14, p.211, 2020.

B. M. Henry and G. Lippi, Chronic kidney disease is associated with severe coronavirus disease 2019 (COVID-19) infection, Int. Urol. Nephrol, vol.1, issue.2, 2020.

G. Lippi, J. Wong, and B. M. Henry, Hypertension and its severity or mortality in Coronavirus Disease 2019 (COVID-19): a pooled analysis, Pol Arch Intern Med, vol.130, pp.304-309, 2020.

X. Wang, Comorbid Chronic Diseases and Acute Organ Injuries Are Strongly Correlated with Disease Severity and Mortality among COVID-19 Patients: A Systemic Review and Meta-Analysis, Research, vol.2020, p.2402961, 2020.

C. L. Sprung, Adult ICU Triage During the Coronavirus Disease 2019 Pandemic: Who Will Live and Who Will Die? Recommendations to Improve Survival, Crit. Care Med, 2020.

K. Li, The Clinical and Chest CT Features Associated With Severe and Critical COVID-19 Pneumonia, Invest. Radiol, vol.55, pp.327-331, 2020.

R. Du, Predictors of mortality for patients with COVID-19 pneumonia caused by SARS-CoV-2: a prospective cohort study, Eur. Respir. J, vol.55, 2020.

X. Li, Risk factors for severity and mortality in adult COVID-19 inpatients in Wuhan, J. Allergy Clin. Immunol, 2020.

Q. Ruan, K. Yang, W. Wang, L. Jiang, and J. Song, Clinical predictors of mortality due to COVID-19 based on an analysis of data of 150 patients from Wuhan, China, Intensive Care Med, vol.46, pp.846-848, 2020.

M. Yuan, W. Yin, Z. Tao, W. Tan, and Y. Hu, Association of radiologic findings with mortality of patients infected with 2019 novel coronavirus in Wuhan, China, PLoS One, vol.15, p.230548, 2020.

R. Zhang, CT features of SARS-CoV-2 pneumonia according to clinical presentation: a retrospective analysis of 120 consecutive patients from Wuhan city, Eur. Radiol, 2020.

K. Li, CT image visual quantitative evaluation and clinical classification of coronavirus disease (COVID-19), Eur. Radiol, 2020.

Y. Li, Z. Yang, T. Ai, S. Wu, and L. Xia, Association of 'initial CT' findings with mortality in older patients with coronavirus disease 2019 (COVID-19), Eur. Radiol, 2020.

K. Liu, CT manifestations of coronavirus disease-2019: A retrospective analysis of 73 cases by disease severity, Eur. J. Radiol, vol.126, p.108941, 2020.

Z. Ye, Y. Zhang, Y. Wang, Z. Huang, and B. Song, Chest CT manifestations of new coronavirus disease 2019 (COVID-19): a pictorial review, Eur. Radiol, 2020.

Z. Xu, Pathological findings of COVID-19 associated with acute respiratory distress syndrome, Lancet Respir Med, vol.8, pp.420-422, 2020.

S. Tian, Pulmonary Pathology of Early-Phase 2019 Novel Coronavirus (COVID-19) Pneumonia in Two Patients With Lung Cancer, J. Thorac. Oncol, vol.15, pp.700-704, 2020.

A. Hagiwara, S. Fujita, Y. Ohno, and S. Aoki, Variability and Standardization of Quantitative Imaging: Monoparametric to Multiparametric Quantification, Radiomics, and Artificial Intelligence, Invest. Radiol, 2020.

K. Wang, Imaging manifestations and diagnostic value of chest CT of coronavirus disease 2019 (COVID-19) in the Xiaogan area, Clin. Radiol, vol.75, pp.341-347, 2020.

Y. Xiong, Clinical and high-resolution CT features of the COVID-19 infection: comparison of the initial and follow-up changes, Invest. Radiol, 2020.

R. Yang, Chest CT Severity Score: An Imaging Tool for Assessing Severe COVID-19, Radiology: Cardiothoracic Imaging, vol.2, p.200047, 2020.

S. Simpson, 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, vol.2, p.200152, 2020.

D. M. Hansell, Fleischner Society: glossary of terms for thoracic imaging, Radiology, vol.246, pp.697-722, 2008.

. La, Imagerie Thoracique propose un compte-rendu structuré de scanner thoracique pour les patients suspects de COVID-19, 2020.

E. R. Delong, D. M. Delong, and D. L. Clarke-pearson, Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach, Biometrics, vol.44, pp.837-845, 1988.

K. Hara, H. Kataoka, and Y. Satoh, Learning Spatio-Temporal Features with 3D Residual Networks for Action Recognition, 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp.3154-3160, 2017.

O. Ronneberger, P. Fischer, T. Brox, and . U-net, Convolutional Networks for Biomedical Image Segmentation. in Medical Image Computing and Computer-Assisted Intervention -MICCAI, 2015.

S. Chen, K. Ma, Y. Zheng, and . Med3d, Transfer Learning for 3D Medical Image Analysis, 2019.

J. Yang, Autosegmentation for thoracic radiation treatment planning: A grand challenge at AAPM 2017, Med. Phys, vol.45, pp.4568-4581, 2018.

M. Baldeon-calisto and S. K. Lai-yuen, AdaEn-Net: An ensemble of adaptive 2D-3D Fully Convolutional Networks for medical image segmentation, Neural Netw, vol.126, pp.76-94, 2020.

M. Tan, Q. V. Le, and . Efficientnet, Rethinking Model Scaling for Convolutional Neural Networks, 2019.

K. He, X. Zhang, S. Ren, and J. Sun, Deep Residual Learning for Image Recognition, 2015.

X. Chen, H. Fan, R. Girshick, and K. He, Baselines with Momentum Contrastive Learning, 2020.

K. Yan, X. Wang, L. Lu, and R. M. Summers, DeepLesion: automated mining of large-scale lesion annotations and universal lesion detection with deep learning, J Med Imaging (Bellingham), vol.5, p.36501, 2018.

, LIDC-IDRI -The Cancer Imaging Archive (TCIA) Public Access -Cancer Imaging Archive Wiki

J. Hofmanninger, Automatic lung segmentation in routine imaging is a data diversity problem, not a methodology problem, 2020.

F. Pedregosa, Scikit-learn: Machine learning in Python. the, Journal of machine Learning research, vol.12, pp.2825-2830, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00650905

W. S. Lim, Defining community acquired pneumonia severity on presentation to hospital: an international derivation and validation study, Thorax, vol.58, pp.377-382, 2003.