Literature DB >> 33767213

Generalized chest CT and lab curves throughout the course of COVID-19.

Michael T Kassin1,2, Nicole Varble1,3, Maxime Blain1, Sheng Xu1, Evrim B Turkbey2, Stephanie Harmon4,5,6, Dong Yang7, Ziyue Xu7, Holger Roth7, Daguang Xu7, Mona Flores8, Amel Amalou1, Kaiyun Sun9, Sameer Kadri10, Francesca Patella11, Maurizio Cariati11, Alice Scarabelli12, Elvira Stellato12, Anna Maria Ierardi13, Gianpaolo Carrafiello13, Peng An14, Baris Turkbey4,6, Bradford J Wood15,16,17,18.   

Abstract

A better understanding of temporal relationships between chest CT and labs may provide a reference for disease severity over the disease course. Generalized curves of lung opacity volume and density over time can be used as standardized references from well before symptoms develop to over a month after recovery, when residual lung opacities remain. 739 patients with COVID-19 underwent CT and RT-PCR in an outbreak setting between January 21st and April 12th, 2020. 29 of 739 patients had serial exams (121 CTs and 279 laboratory measurements) over 50 ± 16 days, with an average of 4.2 sequential CTs each. Sequential volumes of total lung, overall opacity and opacity subtypes (ground glass opacity [GGO] and consolidation) were extracted using deep learning and manual segmentation. Generalized temporal curves of CT and laboratory measurements were correlated. Lung opacities appeared 3.4 ± 2.2 days prior to symptom onset. Opacity peaked 1 day after symptom onset. GGO onset was earlier and resolved later than consolidation. Lactate dehydrogenase, and C-reactive protein peaked earlier than procalcitonin and leukopenia. The temporal relationships of quantitative CT features and clinical labs have distinctive patterns and peaks in relation to symptom onset, which may inform early clinical course in patients with mild COVID-19 pneumonia, or may shed light upon chronic lung effects or mechanisms of medical countermeasures in clinical trials.

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Year:  2021        PMID: 33767213      PMCID: PMC7994835          DOI: 10.1038/s41598-021-85694-5

Source DB:  PubMed          Journal:  Sci Rep        ISSN: 2045-2322            Impact factor:   4.996


  31 in total

1.  Software performance in segmenting ground-glass and solid components of subsolid nodules in pulmonary adenocarcinomas.

Authors:  Julien G Cohen; Jin Mo Goo; Roh-Eul Yoo; Chang Min Park; Chang Hyun Lee; Bram van Ginneken; Doo Hyun Chung; Young Tae Kim
Journal:  Eur Radiol       Date:  2016-04-05       Impact factor: 5.315

2.  Automatic detection and quantification of ground-glass opacities on high-resolution CT using multiple neural networks: comparison with a density mask.

Authors:  H U Kauczor; K Heitmann; C P Heussel; D Marwede; T Uthmann; M Thelen
Journal:  AJR Am J Roentgenol       Date:  2000-11       Impact factor: 3.959

3.  Lung Adenocarcinoma: Correlation of Quantitative CT Findings with Pathologic Findings.

Authors:  Jane P Ko; James Suh; Opeyemi Ibidapo; Joanna G Escalon; Jinyu Li; Harvey Pass; David P Naidich; Bernard Crawford; Emily B Tsai; Chi Wan Koo; Artem Mikheev; Henry Rusinek
Journal:  Radiology       Date:  2016-04-20       Impact factor: 11.105

4.  Chest CT Findings in Coronavirus Disease-19 (COVID-19): Relationship to Duration of Infection.

Authors:  Adam Bernheim; Xueyan Mei; Mingqian Huang; Yang Yang; Zahi A Fayad; Ning Zhang; Kaiyue Diao; Bin Lin; Xiqi Zhu; Kunwei Li; Shaolin Li; Hong Shan; Adam Jacobi; Michael Chung
Journal:  Radiology       Date:  2020-02-20       Impact factor: 11.105

5.  CT Quantitative Analysis and Its Relationship with Clinical Features for Assessing the Severity of Patients with COVID-19.

Authors:  Dong Sun; Xiang Li; Dajing Guo; Lan Wu; Ting Chen; Zheng Fang; Linli Chen; Wenbing Zeng; Ran Yang
Journal:  Korean J Radiol       Date:  2020-07       Impact factor: 3.500

6.  Dynamic evolution of COVID-19 on chest computed tomography: experience from Jiangsu Province of China.

Authors:  Yuan-Cheng Wang; Huanyuan Luo; Songqiao Liu; Shan Huang; Zhen Zhou; Qian Yu; Shijun Zhang; Zhen Zhao; Yizhou Yu; Yi Yang; Duolao Wang; Shenghong Ju
Journal:  Eur Radiol       Date:  2020-06-10       Impact factor: 5.315

7.  High versus low attenuation thresholds to determine the solid component of ground-glass opacity nodules.

Authors:  Jae Ho Lee; Tae Hoon Kim; Sungsoo Lee; Kyunghwa Han; Min Kwang Byun; Yoon Soo Chang; Hyung Jung Kim; Geun Dong Lee; Chul Hwan Park
Journal:  PLoS One       Date:  2018-10-18       Impact factor: 3.240

8.  CT quantification of pneumonia lesions in early days predicts progression to severe illness in a cohort of COVID-19 patients.

Authors:  Fengjun Liu; Qi Zhang; Chao Huang; Chunzi Shi; Lin Wang; Nannan Shi; Cong Fang; Fei Shan; Xue Mei; Jing Shi; Fengxiang Song; Zhongcheng Yang; Zezhen Ding; Xiaoming Su; Hongzhou Lu; Tongyu Zhu; Zhiyong Zhang; Lei Shi; Yuxin Shi
Journal:  Theranostics       Date:  2020-04-27       Impact factor: 11.556

9.  Multicenter cohort study demonstrates more consolidation in upper lungs on initial CT increases the risk of adverse clinical outcome in COVID-19 patients.

Authors:  Qian Yu; Yuancheng Wang; Shan Huang; Songqiao Liu; Zhen Zhou; Shijun Zhang; Zhen Zhao; Yizhou Yu; Yi Yang; Shenghong Ju
Journal:  Theranostics       Date:  2020-04-27       Impact factor: 11.556

10.  CT image visual quantitative evaluation and clinical classification of coronavirus disease (COVID-19).

Authors:  Kunwei Li; Yijie Fang; Wenjuan Li; Cunxue Pan; Peixin Qin; Yinghua Zhong; Xueguo Liu; Mingqian Huang; Yuting Liao; Shaolin Li
Journal:  Eur Radiol       Date:  2020-03-25       Impact factor: 5.315

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  3 in total

1.  COVID-MTL: Multitask learning with Shift3D and random-weighted loss for COVID-19 diagnosis and severity assessment.

Authors:  Guoqing Bao; Huai Chen; Tongliang Liu; Guanzhong Gong; Yong Yin; Lisheng Wang; Xiuying Wang
Journal:  Pattern Recognit       Date:  2021-12-12       Impact factor: 7.740

2.  Towards a multi-scale computer modeling workflow for simulation of pulmonary ventilation in advanced COVID-19.

Authors:  Shea Middleton; Elizabeth Dimbath; Anup Pant; Stephanie M George; Veeranna Maddipati; M Sean Peach; Kaida Yang; Andrew W Ju; Ali Vahdati
Journal:  Comput Biol Med       Date:  2022-04-12       Impact factor: 6.698

3.  Spatially distributed infection increases viral load in a computational model of SARS-CoV-2 lung infection.

Authors:  Melanie E Moses; Steven Hofmeyr; Judy L Cannon; Akil Andrews; Rebekah Gridley; Monica Hinga; Kirtus Leyba; Abigail Pribisova; Vanessa Surjadidjaja; Humayra Tasnim; Stephanie Forrest
Journal:  PLoS Comput Biol       Date:  2021-12-23       Impact factor: 4.475

  3 in total

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