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Study on the prognosis predictive model of COVID-19 patients based on CT radiomics
Making timely assessments of disease progression in patients with COVID-19 could help offer the best personalized treatment. The purpose of this study was to explore an effective model to predict the outcome of patients with COVID-19. We retrospectively included 188 patients (124 in the training set...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8172890/ https://www.ncbi.nlm.nih.gov/pubmed/34078950 http://dx.doi.org/10.1038/s41598-021-90991-0 |
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author | Wang, Dandan Huang, Chencui Bao, Siyu Fan, Tingting Sun, Zhongqi Wang, Yiqiao Jiang, Huijie Wang, Song |
author_facet | Wang, Dandan Huang, Chencui Bao, Siyu Fan, Tingting Sun, Zhongqi Wang, Yiqiao Jiang, Huijie Wang, Song |
author_sort | Wang, Dandan |
collection | PubMed |
description | Making timely assessments of disease progression in patients with COVID-19 could help offer the best personalized treatment. The purpose of this study was to explore an effective model to predict the outcome of patients with COVID-19. We retrospectively included 188 patients (124 in the training set and 64 in the test set) diagnosed with COVID-19. Patients were divided into aggravation and improvement groups according to the disease progression. Three kinds of models were established, including the radiomics, clinical, and combined model. Receiver operating characteristic curves, decision curves, and Delong’s test were used to evaluate and compare the models. Our analysis showed that all the established prediction models had good predictive performance in predicting the progress and outcome of COVID-19. |
format | Online Article Text |
id | pubmed-8172890 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-81728902021-06-04 Study on the prognosis predictive model of COVID-19 patients based on CT radiomics Wang, Dandan Huang, Chencui Bao, Siyu Fan, Tingting Sun, Zhongqi Wang, Yiqiao Jiang, Huijie Wang, Song Sci Rep Article Making timely assessments of disease progression in patients with COVID-19 could help offer the best personalized treatment. The purpose of this study was to explore an effective model to predict the outcome of patients with COVID-19. We retrospectively included 188 patients (124 in the training set and 64 in the test set) diagnosed with COVID-19. Patients were divided into aggravation and improvement groups according to the disease progression. Three kinds of models were established, including the radiomics, clinical, and combined model. Receiver operating characteristic curves, decision curves, and Delong’s test were used to evaluate and compare the models. Our analysis showed that all the established prediction models had good predictive performance in predicting the progress and outcome of COVID-19. Nature Publishing Group UK 2021-06-02 /pmc/articles/PMC8172890/ /pubmed/34078950 http://dx.doi.org/10.1038/s41598-021-90991-0 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Wang, Dandan Huang, Chencui Bao, Siyu Fan, Tingting Sun, Zhongqi Wang, Yiqiao Jiang, Huijie Wang, Song Study on the prognosis predictive model of COVID-19 patients based on CT radiomics |
title | Study on the prognosis predictive model of COVID-19 patients based on CT radiomics |
title_full | Study on the prognosis predictive model of COVID-19 patients based on CT radiomics |
title_fullStr | Study on the prognosis predictive model of COVID-19 patients based on CT radiomics |
title_full_unstemmed | Study on the prognosis predictive model of COVID-19 patients based on CT radiomics |
title_short | Study on the prognosis predictive model of COVID-19 patients based on CT radiomics |
title_sort | study on the prognosis predictive model of covid-19 patients based on ct radiomics |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8172890/ https://www.ncbi.nlm.nih.gov/pubmed/34078950 http://dx.doi.org/10.1038/s41598-021-90991-0 |
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