tetano
Editor, Senior Moderator
Eur J Med Res
. 2020 Oct 12;25(1):49.
doi: 10.1186/s40001-020-00450-1.
Development of a quantitative segmentation model to assess the effect of comorbidity on patients with COVID-19
Cui Zhang[SUP] 1 [/SUP], Guangzhao Yang[SUP] 1 [/SUP], Chunxian Cai[SUP] 2 [/SUP], Zhihua Xu[SUP] 1 [/SUP], Hai Wu[SUP] 3 [/SUP], Youmin Guo[SUP] 4 [/SUP], Zongyu Xie[SUP] 5 [/SUP], Hengfeng Shi[SUP] 6 [/SUP], Guohua Cheng[SUP] 7 [/SUP], Jian Wang[SUP] 8 [/SUP]
Affiliations
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.
Keywords: COVID-19; Comorbidity; Deep learning; X-ray computed tomography.
. 2020 Oct 12;25(1):49.
doi: 10.1186/s40001-020-00450-1.
Development of a quantitative segmentation model to assess the effect of comorbidity on patients with COVID-19
Cui Zhang[SUP] 1 [/SUP], Guangzhao Yang[SUP] 1 [/SUP], Chunxian Cai[SUP] 2 [/SUP], Zhihua Xu[SUP] 1 [/SUP], Hai Wu[SUP] 3 [/SUP], Youmin Guo[SUP] 4 [/SUP], Zongyu Xie[SUP] 5 [/SUP], Hengfeng Shi[SUP] 6 [/SUP], Guohua Cheng[SUP] 7 [/SUP], Jian Wang[SUP] 8 [/SUP]
Affiliations
- PMID: 33046116
- DOI: 10.1186/s40001-020-00450-1
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.
Keywords: COVID-19; Comorbidity; Deep learning; X-ray computed tomography.