A Novel Machine Learning-derived Radiomic Signature of the Whole Lung Differentiates Stable From Progressive COVID-19 Infection: A Retrospective Cohort Study.

Journal: Journal of thoracic imaging

Published Date: Nov 1, 2020

Abstract

OBJECTIVE: This study aimed to use the radiomics signatures of a machine learning-based tool to evaluate the prognosis of patients with coronavirus disease 2019 (COVID-19) infection.

Authors

Liping Fu

Department of Nuclear Medicine, The Chinese PLA General Hospital, Beijing, China.
Yongchou Li

Department of Radiology, The Third Affiliated Hospital of Wenzhou Medical University, Ruian, Zhejiang Province.
Aiping Cheng

Departments of Nuclear Medicine.
PeiPei Pang

GE Healthcare China, Shanghai, China.
Zhenyu Shu

Radiology, Zhejiang Provincial People's Hospital, People's Hospital of Hangzhou Medical College, Hangzhou.

Keywords

COVID-19 Diagnosis, Differential Disease Progression Female Humans Lung Machine Learning Male Middle Aged Radiographic Image Interpretation, Computer-Assisted Reproducibility of Results Retrospective Studies SARS-CoV-2 Sensitivity and Specificity Severity of Illness Index Tomography, X-Ray Computed

External Resources

View on PubMed Access via DOI PubMed (32555006)

A Novel Machine Learning-derived Radiomic Signature of the Whole Lung Differentiates Stable From Progressive COVID-19 Infection: A Retrospective Cohort Study.

Abstract

Authors

Keywords

External Resources

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