Study on the prognosis predictive model of COVID-19 patients based on CT radiomics

Abstract 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 tra...

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Autores principales:	Dandan Wang, Chencui Huang, Siyu Bao, Tingting Fan, Zhongqi Sun, Yiqiao Wang, Huijie Jiang, Song Wang
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/1f65b776d63e4385ae2988ad3bc74504
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spelling	oai:doaj.org-article:1f65b776d63e4385ae2988ad3bc745042021-12-02T18:25:04ZStudy on the prognosis predictive model of COVID-19 patients based on CT radiomics10.1038/s41598-021-90991-02045-2322https://doaj.org/article/1f65b776d63e4385ae2988ad3bc745042021-06-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-90991-0https://doaj.org/toc/2045-2322Abstract 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.Dandan WangChencui HuangSiyu BaoTingting FanZhongqi SunYiqiao WangHuijie JiangSong WangNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Dandan Wang Chencui Huang Siyu Bao Tingting Fan Zhongqi Sun Yiqiao Wang Huijie Jiang Song Wang Study on the prognosis predictive model of COVID-19 patients based on CT radiomics
description	Abstract 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	article
author	Dandan Wang Chencui Huang Siyu Bao Tingting Fan Zhongqi Sun Yiqiao Wang Huijie Jiang Song Wang
author_facet	Dandan Wang Chencui Huang Siyu Bao Tingting Fan Zhongqi Sun Yiqiao Wang Huijie Jiang Song Wang
author_sort	Dandan Wang
title	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_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_sort	study on the prognosis predictive model of covid-19 patients based on ct radiomics
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/1f65b776d63e4385ae2988ad3bc74504
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_version_	1718378026158260224

Study on the prognosis predictive model of COVID-19 patients based on CT radiomics

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