Visual classification of three computed tomography lung patterns to predict prognosis of COVID-19: a retrospective study

Abstract Background Quantitative evaluation of radiographic images has been developed and suggested for the diagnosis of coronavirus disease 2019 (COVID-19). However, there are limited opportunities to use these image-based diagnostic indices in clinical practice. Our aim in this study was to evaluate the utility of a novel visually-based classification of pulmonary findings from computed tomography (CT) images of COVID-19 patients with the following three patterns defined: peripheral, multifocal, and diffuse findings of pneumonia. We also evaluated the prognostic value of this classification to predict the severity of COVID-19. Methods This was a single-center retrospective cohort study of patients hospitalized with COVID-19 between January 1st and September 30th, 2020, who presented with suspicious findings on CT lung images at admission (n = 69). We compared the association between the three predefined patterns (peripheral, multifocal, and diffuse), admission to the intensive care unit, tracheal intubation, and death. We tested quantitative CT analysis as an outcome predictor for COVID-19. Quantitative CT analysis was performed using a semi-automated method (Thoracic Volume Computer-Assisted Reading software, GE Health care, United States). Lungs were divided by Hounsfield unit intervals. Compromised lung (%CL) volume was the sum of poorly and non-aerated volumes (− 500, 100 HU). We collected patient clinical data, including demographic and clinical variables at the time of admission. Results Patients with a diffuse pattern were intubated more frequently and for a longer duration than patients with a peripheral or multifocal pattern. The following clinical variables were significantly different between the diffuse pattern and peripheral and multifocal groups: body temperature (p = 0.04), lymphocyte count (p = 0.01), neutrophil count (p = 0.02), c-reactive protein (p < 0.01), lactate dehydrogenase (p < 0.01), Krebs von den Lungen-6 antigen (p < 0.01), D-dimer (p < 0.01), and steroid (p = 0.01) and favipiravir (p = 0.03) administration. Conclusions Our simple visual assessment of CT images can predict the severity of illness, a resulting decrease in respiratory function, and the need for supplemental respiratory ventilation among patients with COVID-19.

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Visual Classification of Three Computed Tomography Lung Patterns to Predict Prognosis of COVID-19: A Retrospective Study

10.21203/rs.3.rs-820588/v1 ◽

2021 ◽

Author(s):

Daisuke Yamada ◽

Sachiko Ohde ◽

Kengo Ikejima ◽

Masaki Matsusako ◽

Yasuyuki Kurihara

Keyword(s):

Computed Tomography ◽

Lactate Dehydrogenase ◽

Neutrophil Count ◽

Lymphocyte Count ◽

Ct Images ◽

C Reactive Protein ◽

Clinical Variables ◽

Reactive Protein ◽

D Dimer

Abstract Background: Quantitative evaluation of radiographic images has been developed and suggested for the diagnosis of coronavirus disease 2019 (COVID-19). However, there are limited opportunities to use these image-based diagnostic indices in clinical practice. Our aim in this study was to evaluate the utility of a visually-based classification of pulmonary findingsfrom computed tomography (CT) images among COVID-19 patients that we developed, with the following three patterns defined: peripheral, multifocal, and diffuse findings of pneumonia. We also evaluated the prognostic value of this classification to predict the severity of COVID-19. Methods: This was a single-center retrospective cohort study of patients hospitalized with COVID-19 between January and September 2020, who presented with suspicious findings on CT lung images (n=69). We compared the association between the three predefined patterns (peripheral, multifocal, and diffuse) and admission to the intensive care unit, tracheal intubation, and death. The following demographic and clinical variables were compared between the three groups: sex, age, respiratory rate, pulse rate, blood pressure, temperature, oxygen saturation, partial pressure of oxygen, white blood cell count, lymphocyte count, neutrophil count, c-reactive protein, lactate dehydrogenase, Krebs von den Lungen-6 antigen, D-dimer, platelet count, steroid administration, heparin administration, favipiravir administration, Acute Physiology and Chronic Health Evaluation-II score, and sequential organ failure assessment score.Results: Patients with a diffuse pattern were intubated more frequently and for a longer duration than patients with a peripheral or multifocal pattern. The following clinical variables were significantly different between the diffuse pattern and peripheral and multifocal groups: body temperature, lymphocyte count, neutrophil count, c-reactive protein, lactate dehydrogenase, Krebs von den Lungen-6 antigen, D-dimer, and steroid and Avigan administration.Conclusions: Our simple visual assessment of CT images can predict a systemic cytokine storm, a resulting decrease in respiratory function, and the need for supplemental respiratory ventilationamong patientswith COVID-19.

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Usefulness of quantitative computed tomography analysis using a commercially available software program through its simple and automatic procedures in evaluation of emphysema and pulmonary fibrosis

10.21203/rs.3.rs-15881/v1 ◽

2020 ◽

Author(s):

Yoshiaki Kitaguchi ◽

Keisaku Fujimoto ◽

Masanori Yasuo ◽

Yosuke Wada ◽

Fumika Ueno ◽

...

Keyword(s):

Computed Tomography ◽

Pulmonary Fibrosis ◽

Quantitative Computed Tomography ◽

Visual Assessment ◽

Quantitative Ct ◽

High Attenuation ◽

Software Program ◽

Ct Analysis ◽

Quantitative Ct Analysis ◽

Quantitative Assessments

Abstract BackgroundThe aim of this study was to investigate the usefulness of quantitative computed tomography (CT) analysis using a commercially available software program through its simple and automatic procedures. The software program, which was developed with the density mask technique using two thresholds, was used for simultaneously assessing both the low attenuation volume (LAV) and high attenuation volume (HAV) to detect emphysema and pulmonary fibrosis.MethodsIn this prospective cohort study of stable patients with chronic obstructive pulmonary disease (COPD) and idiopathic pulmonary fibrosis (IPF), we investigated the correlations between quantitative assessments performed using the software program and visual assessments, and between pulmonary function parameters and quantitative assessment parameters. We also investigated whether the utility of quantitative assessments could improve by assessing the destroyed lung volume (DLV), defined as the LAV+HAV.ResultsStrong significant correlations were detected between the percentage of LAV (LAV%) and the visual assessment of emphysema and between the percentage of HAV (HAV%) and the visual assessment of pulmonary fibrosis. A receiver operating characteristic curve analysis revealed 86.8% sensitivity and 84.2% specificity for the LAV% to detect emphysema, using 1.5% as the cut-off value, and 87.5% sensitivity and 96.1% specificity for the HAV% to detect pulmonary fibrosis, using 12% as the cut-off value. The percentage of DLV (DLV%) significantly correlated with the diffusion capacity of lung for carbon monoxide (DLco) and delta N2 in patients with COPD. Meanwhile, the DLV% significantly correlated with DLco and the composite physiologic index in patients with IPF. Moreover, the DLV% significantly also correlated with DLco in total patients with COPD and IPF.ConclusionsThe quantitative CT analysis using a commercially available software program could be useful in clinical practice by the advantage of the simple and automatic procedures. The utility of the quantitative CT analysis could improve by assessing the DLV%.

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MUC5B variant is associated with visually and quantitatively detected preclinical pulmonary fibrosis

Thorax ◽

10.1136/thoraxjnl-2018-212430 ◽

2019 ◽

Vol 74 (12) ◽

pp. 1131-1139 ◽

Cited By ~ 10

Author(s):

Susan K Mathai ◽

Stephen Humphries ◽

Jonathan A Kropski ◽

Timothy S Blackwell ◽

Julia Powers ◽

...

Keyword(s):

Deep Learning ◽

Pulmonary Fibrosis ◽

Learning Algorithm ◽

Quantitative Ct ◽

First Degree Relatives ◽

Deep Learning Algorithm ◽

Increased Risk ◽

Ct Evaluation ◽

Ct Analysis ◽

Quantitative Ct Analysis

BackgroundRelatives of patients with familial interstitial pneumonia (FIP) are at increased risk for pulmonary fibrosis. We assessed the prevalence and risk factors for preclinical pulmonary fibrosis (PrePF) in first-degree relatives of patients with FIP and determined the utility of deep learning in detecting PrePF on CT.MethodsFirst-degree relatives of patients with FIP over 40 years of age who believed themselves to be unaffected by pulmonary fibrosis underwent CT scans of the chest. Images were visually reviewed, and a deep learning algorithm was used to quantify lung fibrosis. Genotyping for common idiopathic pulmonary fibrosis risk variants in MUC5B and TERT was performed.FindingsIn 494 relatives of patients with FIP from 263 families of patients with FIP, the prevalence of PrePF on visual CT evaluation was 15.6% (95% CI 12.6 to 19.0). Compared with visual CT evaluation, deep learning quantitative CT analysis had 84% sensitivity (95% CI 0.72 to 0.89) and 86% sensitivity (95% CI 0.83 to 0.89) for discriminating subjects with visual PrePF diagnosis. Subjects with PrePF were older (65.9, SD 10.1 years) than subjects without fibrosis (55.8 SD 8.7 years), more likely to be male (49% vs 37%), more likely to have smoked (44% vs 27%) and more likely to have the MUC5B promoter variant rs35705950 (minor allele frequency 0.29 vs 0.21). MUC5B variant carriers had higher quantitative CT fibrosis scores (mean difference of 0.36%), a difference that remains significant when controlling for age and sex.InterpretationPrePF is common in relatives of patients with FIP. Its prevalence increases with age and the presence of a common MUC5B promoter variant. Quantitative CT analysis can detect these imaging abnormalities.

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