Quantitative computed tomography analysis for stratifying the severity of Coronavirus Disease 2019

Shen, Cong; Ye, Nan; Cai, Shubo; Zhou, Jie; Sheng, Jiexin; Liu, Kang; Zhou, Heping; Guo, Youmin; Niu, Gang

doi:10.1016/j.jpha.2020.03.004

Cited by 154 publications

(149 citation statements)

References 20 publications

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“…These ndings indicated that the progression of the crazy-paving pattern might represent further in ltration of the lung parenchyma and lung interstitium [24,28]. The previous studies [26,29,30] have reported that in the progression or peak period of pneumonia (1-3 weeks), the progression of crazy-paving pattern, septal thickening and consolidation can be observed. However, COVID-19 pneumonia has different CT manifestations at different stages, which are mainly related to pathogenesis.…”

Section: Discussionmentioning

confidence: 69%

Dynamic Changes of Chest CT Follow-up In Coronavirus Disease-19 (COVID-19) Pneumonia: Relationship To Clinical Typing

Liu

Yang

et al. 2020

Preprint

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Background: To investigate the CT changes of different clinical types of COVID-19 pneumonia. Methods: This retrospective study included 50 patients with COVID-19 from 16 January 2020 to 25 February 2020. We analyzed the clinical characteristics, CT characteristics and the pneumonia involvement of the patients between the moderate group and the severe and critical group, and the dynamic changes of severity with the CT follow-up time. Results: There were differences in the CT severity score of the right lung in the initial CT, and total CT severity score in the initial and follow-up CT between the moderate group and the severe and critical group (all p <0.05). There was a quadratic relationship between total CT severity score and CT follow-up time in the severe and critical group (r2=0.137, p=0.008), the total CT severity score peaked at the second follow-up CT. There was no correlation between total CT severity score and CT follow-up time in the moderate group (p >0.05). There were no differences in the occurrence rate of CT characteristics in the initial CT between the two groups (all p >0.05). There were differences in the occurrence rate of ground-glass opacity and crazy-paving pattern in the second follow-up CT, and pleural thickening or adhesion in the third follow-up CT between the two groups (all p <0.05). Conclusions: The CT changes of COVID-19 pneumonia with different severity were different, and the extent of pneumonia involvement by CT can help to assess the severity of COVID-19 pneumonia rather than the initial CT characteristics.

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Section: Discussionmentioning

confidence: 69%

Dynamic Changes of Chest CT Follow-up In Coronavirus Disease-19 (COVID-19) Pneumonia: Relationship To Clinical Typing

Liu

Yang

et al. 2020

Preprint

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“…Quantification of lung involvements with advanced CT post-processing software or AI algorithms may be more accurate and reproducible. 5 Moreover, although CT score was suggested an independent predictor for liver injury in COVID-19 patients, it remains unclear that how many variables were included in the logistic regression and whether the CT score was the only significant predictor.…”

Section: Liver Injury In Covid-19: Diagnosis and Associated Factorsmentioning

confidence: 99%

Liver injury in COVID‐19: Diagnosis and associated factors

Zheng

Song

2020

Liver International

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We read with interest the study by Xie et al about the liver injury in non-ICU hospitalized COVID-19 patients 1 ; the authors found liver injury was prevalent in COVID-19 patients and might associate with CT scores. However, we believe some concerns should be aroused regarding this conclusion.Liver function abnormalities were frequent in COVID-19 patients, especially the severe cases. 2 However, as a new contagious disease, there is no standardized diagnostic criteria of COVID-19associated liver injury at present. Some researchers defined liver injury as any liver function parameter above the upper limit of normal (ULN), 3 but others defined it as liver enzymes higher than two or three times of ULN, and even further classified different liver injury patterns. 4 This study defined elevated levels of alanine transaminase (ALT), aspartate aminotransferase (AST) or bilirubin as liver injury without specifying the ULN of laboratory reference, which was ambiguous and made it difficult to replicate their results. Furthermore, the time point of diagnosing liver injury was vague. Although we can infer this diagnosis of liver injury was made on the initial laboratory tests on the admission throughout the paper, the authors did not directly mention it. Similarly, the time point of post-treatment was unclear. The authors described post-treatment ALT and AST levels, however, the exact day (i.e. the 1st, 3rd or 7th day after treatment) was unknown, and the time interval may affect the level of liver enzymes. Also, it was unclear whether the post-treatment data came from a single test or from the average of multiple post-treatment tests. Efforts should be made to establish a standardized definition and diagnostic time point of liver injury in COVID-19 patients.Another important finding in this study was that severe lung lesions on CT (i.e. high CT score) might be related to higher incidence of liver injury. However, the CT scores were assigned on the basis of the percentage of involved lung area, which was semi-quantitative and subjective. Quantification of lung involvements with advanced CT post-processing software or AI algorithms may be more accurate and reproducible. 5 Moreover, although CT score was suggested an independent predictor for liver injury in COVID-19 patients, it remains unclear that how many variables were included in the logistic regression and whether the CT score was the only significant predictor.In summary, this study provided interesting but preliminary findings. Large-sample multicentre studies are needed to validate these results and further explore COVID-19-associated liver injury.

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“…It should be noted that the segmentation methods in COVID-19 applications can be mainly divided into two categories, namely, the lung-region-oriented and the lung-lesion-oriented methods. The lung-region-oriented methods aim to separate lung regions, namely, whole lung and lung lobes, from other (background) regions in CT or X-ray, which is considered as a pre-requisite step in COVID-19 applications [29][30][31][32][33][34][35][36][37][38] . The popular segmentation networks for COVID-19 include classic U-Net [29][30][31][32][33][34] , UNet++ 34,35 , VB-Net 36 .…”

Section: /19 2 Covid19 Related Applicationsmentioning

confidence: 99%

COVID-19 Lesion Segmentation using Lung CT Scan Images: Comparative Study based on Active Contour Models

Akbari

Hassen

Al‐Maadeed

et al. 2020

Preprint

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Abstract Pneumonia is a lung infection threaten that threats all age groups. In this paper, using CT scans images, we used active contour models to evaluate and determine pneumonia infection caused by the Coronavirus disease (COVID-19). A background of active contour models (ACM) including contour representation and object boundary description methods is presented. The focus of this paper is on the conducted works based on the external forces. These methods include edge-based and region-based methods. Furthermore, the explanations of these methods, as well as the advantages and disadvantages of each method are presented. Finally, a comparison between the performances of the conducted works has been done based on a database of Lung CT Scan Images. The present review helps readers identify research starting points in active contour models on COVID19 research, which is a high priority topic to guide researchers and practitioners. In addition, when there are not enough images to use machine learning techniques, such as deep learning methods, the experimental results indicate that active contour methods obtain promising results.

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Quantitative computed tomography analysis for stratifying the severity of Coronavirus Disease 2019

Cited by 154 publications

References 20 publications

Dynamic Changes of Chest CT Follow-up In Coronavirus Disease-19 (COVID-19) Pneumonia: Relationship To Clinical Typing

Dynamic Changes of Chest CT Follow-up In Coronavirus Disease-19 (COVID-19) Pneumonia: Relationship To Clinical Typing

Liver injury in COVID‐19: Diagnosis and associated factors

COVID-19 Lesion Segmentation using Lung CT Scan Images: Comparative Study based on Active Contour Models

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