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European Journal of Medical Research
Published in: European Journal of Medical Research 1/2020

01-12-2020 | COVID-19 | Research

Development of a quantitative segmentation model to assess the effect of comorbidity on patients with COVID-19

Authors: Cui Zhang, Guangzhao Yang, Chunxian Cai, Zhihua Xu, Hai Wu, Youmin Guo, Zongyu Xie, Hengfeng Shi, Guohua Cheng, Jian Wang

Published in: European Journal of Medical Research | Issue 1/2020

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Abstract

Background

The coronavirus disease 2019 (COVID-19) has brought a global disaster. Quantitative lesions may provide the radiological evidence of the severity of pneumonia and further to assess the effect of comorbidity on patients with COVID-19.

Methods

294 patients with COVID-19 were enrolled from February, 24, 2020 to June, 1, 2020 from six centers. Multi-task Unet network was used to segment the whole lung and lesions from chest CT images. This deep learning method was pre-trained in 650 CT images (550 in primary dataset and 100 in test dataset) with COVID-19 or community-acquired pneumonia and Dice coefficients in test dataset were calculated. 50 CT scans of 50 patients (15 with comorbidity and 35 without comorbidity) were random selected to mark lesions manually. The results will be compared with the automatic segmentation model. Eight quantitative parameters were calculated based on the segmentation results to evaluate the effect of comorbidity on patients with COVID-19.

Results

Quantitative segmentation model was proved to be effective and accurate with all Dice coefficients more than 0.85 and all accuracies more than 0.95. Of the 294 patients, 52 (17.7%) patients were reported having at least one comorbidity; 14 (4.8%) having more than one comorbidity. Patients with any comorbidity were older (P < 0.001), had longer incubation period (P < 0.001), were more likely to have abnormal laboratory findings (P < 0.05), and be in severity status (P < 0.001). More lesions (including larger volume of lesion, consolidation, and ground-glass opacity) were shown in patients with any comorbidity than patients without comorbidity (all P < 0.001). More lesions were found on CT images in patients with more comorbidities. The median volumes of lesion, consolidation, and ground-glass opacity in diabetes mellitus group were largest among the groups with single comorbidity that had the incidence rate of top three.

Conclusions

Multi-task Unet network can make quantitative CT analysis of lesions to assess the effect of comorbidity on patients with COVID-19, further to provide the radiological evidence of the severity of pneumonia. More lesions (including GGO and consolidation) were found in CT images of cases with comorbidity. The more comorbidities patients have, the more lesions CT images show.
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Metadata
Title
Development of a quantitative segmentation model to assess the effect of comorbidity on patients with COVID-19
Authors
Cui Zhang
Guangzhao Yang
Chunxian Cai
Zhihua Xu
Hai Wu
Youmin Guo
Zongyu Xie
Hengfeng Shi
Guohua Cheng
Jian Wang
Publication date
01-12-2020
Publisher
BioMed Central
Keyword
COVID-19
Published in
European Journal of Medical Research / Issue 1/2020
Electronic ISSN: 2047-783X
DOI
https://doi.org/10.1186/s40001-020-00450-1

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