Centers for Disease Control and Prevention. CDC covid data tracker. https://covid.cdc.gov/covid-data-tracker/ (Accessed 13 June 2022) (2022).

Karim, S. S. A. & Karim, Q. A. Omicron sars-cov-2 variant: A new chapter in the covid-19 pandemic. Lancet 398(10317), 21262128 (2021).

Article CAS PubMed PubMed Central Google Scholar

Kupferschmidt, K. & Wadman, M. Delta variant triggers new phase in the pandemic. Science 372(6549), 13751376 (2021).

Article ADS CAS Google Scholar

McCue, C. et al. Long term outcomes of critically ill covid-19 pneumonia patients: Early learning. Intensive Care Med. 47(2), 240241 (2021).

Article CAS PubMed Google Scholar

Michelen, M. et al. Characterising long term covid-19: A living systematic review. BMJ Glob. Health 6(9), e005427 (2021).

Article PubMed Google Scholar

Jacobi, A. et al. Portable chest x-ray in coronavirus disease-19 (covid-19): A pictorial review. Clin. Imaging 64, 3542 (2020).

Article PubMed PubMed Central Google Scholar

Kim, H. W. et al. The role of initial chest x-ray in triaging patients with suspected covid-19 during the pandemic. Emerg. Radiol. 27(6), 617621 (2020).

Article PubMed PubMed Central Google Scholar

Akl, E. A. et al. Use of chest imaging in the diagnosis and management of covid-19: A who rapid advice guide. Radiology 298(2), E63E69 (2021).

Article PubMed Google Scholar

Borkowski, A. A. et al. Using artificial intelligence for covid-19 chest x-ray diagnosis. Fed. Pract. 37(9), 398404 (2020).

PubMed PubMed Central Google Scholar

Balbi, M. et al. Chest x-ray for predicting mortality and the need for ventilatory support in covid-19 patients presenting to the emergency department. Eur. Radiol. 31(4), 19992012 (2021).

Article CAS PubMed Google Scholar

Maroldi, R. et al. Which role for chest x-ray score in predicting the outcome in covid-19 pneumonia?. Eur. Radiol. 31(6), 40164022 (2021).

Article CAS PubMed Google Scholar

Monaco, C. G. et al. Chest x-ray severity score in covid-19 patients on emergency department admission: A two-centre study. Eur. Radiol. Exp. 4(1), 68 (2020).

Article PubMed PubMed Central Google Scholar

Hussain, L. et al. Machine-learning classification of texture features of portable chest x-ray accurately classifies covid-19 lung infection. Biomed. Eng. Online 19(1), 88 (2020).

Article PubMed PubMed Central Google Scholar

Ismael, A. M. & engr, A. Deep learning approaches for covid-19 detection based on chest x-ray images. Expert Syst. Appl. 164(114), 054 (2021).

Google Scholar

Salvatore, M. et al. A phenome-wide association study (phewas) of covid-19 outcomes by race using the electronic health records data in michigan medicine. J. Clin. Med. 10(7), 1351 (2021).

Article CAS PubMed PubMed Central Google Scholar

Spector-Bagdady, K. et al. Coronavirus disease 2019 (covid-19) clinical trial oversight at a major academic medical center: Approach of michigan medicine. Clin. Infect. Dis. 71(16), 21872190 (2020).

Article CAS PubMed Google Scholar

Nypaver, M. et al. The michigan emergency department improvement collaborative: A novel model for implementing large scale practice change in pediatric emergency care. Pediatrics 142(1 MeetingAbstract), 105 (2018).

Article Google Scholar

Abbas, A., Abdelsamea, M. M. & Gaber, M. M. Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network. Appl. Intell. 51, 854864 (2021).

Article Google Scholar

Gupta, A. et al. Association between antecedent statin use and decreased mortality in hospitalized patients with COVID-19. Nat. Commun. 12(1), 1325 (2021).

Article ADS CAS PubMed PubMed Central Google Scholar

Cox, D. R. Regression models and life tables (with discussion). J. R. Stat. Soc. B 34(2), 187220 (1972).

MATH Google Scholar

Therneau, T. M. & Grambsch, P. M. Modeling survival data: Extending the Cox model. In The Cox Model 3977 (Springer, 2000).

MATH Google Scholar

Plsterl, S., Navab, N. & Katouzian, A. An efficient training algorithm for kernel survival support vector machines. https://doi.org/10.48550/arXiv.1611.07054 (Preprint posted online November 21, 2016).

Ishwaran, H. et al. Random survival forests. Ann. Appl. Stat. 2(3), 841860 (2008).

Article MathSciNet MATH Google Scholar

Hothorn, T. et al. Survival ensembles. Biostatistics 7(3), 355373 (2006).

Article PubMed MATH Google Scholar

Zhou, Z. H. Ensemble Methods: Foundations and Algorithms (CRC Press, 2012).

Book Google Scholar

Zwanenburg, A. et al. Image biomarker standardisation initiative. https://doi.org/10.48550/arXiv.1612.07003 (Preprint posted online December 21, 2016)

Harrell, F. E. et al. Evaluating the yield of medical tests. JAMA 247(18), 25432546 (1982).

Article PubMed Google Scholar

Harrell, F. E. Jr., Lee, K. L. & Mark, D. B. Multivariable prognostic models: Issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Stat. Med. 15(4), 361387 (1996).

Article PubMed Google Scholar

Holste, G. et al. End-to-end learning of fused image and non-image features for improved breast cancer classification from mri. In Proceedings of the IEEE/CVF International Conference on Computer Vision, 32943303 (2021).

Zhou, H. et al. Diagnosis of distant metastasis of lung cancer: Based on clinical and radiomic features. Transl. Oncol. 11(1), 3136 (2018).

Article PubMed Google Scholar

Militello, C. et al. CT Radiomic Features and Clinical Biomarkers for Predicting Coronary Artery Disease. Cogn. Comput. 15(1), 238253 (2023).

Article Google Scholar

Huang, S. C. et al. Multimodal fusion with deep neural networks for leveraging CT imaging and electronic health record: A case-study in pulmonary embolism detection. Sci. Rep. 10(1), 19 (2020).

Article Google Scholar

Liu, Z. et al. Imaging genomics for accurate diagnosis and treatment of tumors: A cutting edge overview. Biomed. Pharmacother. 135, 111173 (2021).

Article CAS PubMed Google Scholar

Tomaszewski, M. R. & Gillies, R. J. The biological meaning of radiomic features. Radiology 298(3), 505516 (2021).

Article PubMed Google Scholar

Brouqui, P. et al. Asymptomatic hypoxia in COVID-19 is associated with poor outcome. Int. J. Infect. Dis. 102, 233238 (2021).

Article CAS PubMed Google Scholar

Struyf, T. et al. Cochrane COVID-19 Diagnostic Test Accuracy Group. Signs and symptoms to determine if a patient presenting in primary care or hospital outpatient settings has COVID19. Cochrane Database Syst. Rev. (5) (2022).

Garrafa, E. et al. Early prediction of in-hospital death of covid-19 patients: A machine-learning model based on age, blood analyses, and chest x-ray score. Elife 10, e70640 (2021).

Article CAS PubMed PubMed Central Google Scholar

Schalekamp, S. et al. Model-based prediction of critical illness in hospitalized patients with covid-19. Radiology 298(1), E46E54 (2021).

Article PubMed Google Scholar

Soda, P. et al. Aiforcovid: Predicting the clinical outcomes in patients with covid-19 applying ai to chest-x-rays. An Italian multicentre study. Med. Image Anal. 74, 102216 (2021).

Article PubMed PubMed Central Google Scholar

Shen, B. et al. Initial chest radiograph scores inform covid-19 status, intensive care unit admission and need for mechanical ventilation. Clin. Radiol. 76(6), 473.e1-473.e7 (2021).

Article CAS PubMed Google Scholar

Liu, Y. et al. Tumor heterogeneity assessed by texture analysis on contrast-enhanced CT in lung adenocarcinoma: Association with pathologic grade. Oncotarget 8(32), 5366453674 (2017).

Article PubMed PubMed Central Google Scholar

Krizhevsky, A., Sutskever, I. & Hinton, G. E. Imagenet classification with deep convolutional neural networks. Adv. Neural. Inf. Process. Syst. 25, 19 (2012).

Google Scholar

He, K. et al. Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 770778 (2016).

Chandra, T. B. et al. Coronavirus disease (covid19) detection in chest x-ray images using majority voting based classifier ensemble. Expert Syst. Appl. 165(113), 909 (2021).

Google Scholar

Johri, S. et al. A novel machine learning-based analytical framework for automatic detection of covid-19 using chest x-ray images. Int. J. Imaging Syst. Technol. 31(3), 11051119 (2021).

Article Google Scholar

Selvi, J. T., Subhashini, K. & Methini, M. Investigation of covid-19 chest x-ray images using texture featuresA comprehensive approach. Computational 1, 4558 (2021).

MATH Google Scholar

van Griethuysen, J. J. M. et al. Computational radiomics system to decode the radiographic phenotype. Cancer Res. 77(21), e104e107 (2017).

Article PubMed PubMed Central Google Scholar

Zhang, Q., Wu, Y. N. & Zhu, S. C. Interpretable convolutional neural networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 88278836 (2018).

Varghese, B. A. et al. Predicting clinical outcomes in covid-19 using radiomics on chest radiographs. Br. J. Radiol. 94(1126), 20210221 (2021).

Article PubMed Google Scholar

Iori, M. et al. Mortality prediction of COVID-19 patients using radiomic and neural network features extracted from a wide chest X-ray sample size: A robust approach for different medical imbalanced scenarios. Appl. Sci. 12(8), 3903 (2022).

Article CAS Google Scholar

Blain, M. et al. Determination of disease severity in covid-19 patients using deep learning in chest x-ray images. Diagn. Interv. Radiol. 27(1), 2027 (2021).

Article PubMed Google Scholar

Liu, X. et al. Temporal radiographic changes in covid-19 patients: Relationship to disease severity and viral clearance. Sci. Rep. 10(1), 10263 (2020).

Article ADS CAS PubMed PubMed Central Google Scholar

Yasin, R. & Gouda, W. Chest x-ray findings monitoring covid-19 disease course and severity. Egypt. J. Radiol. Nucl. Med. 51(1), 193 (2020).

Article Google Scholar

Castelli, G. et al. Brief communication: Chest radiography score in young covid-19 patients: Does one size fit all?. PLoS ONE 17(2), e0264172 (2022).

Original post:
Use of machine learning to assess the prognostic utility of radiomic ... - Nature.com