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Low-dose chest CT for diagnosing and assessing the extent of lung involvement of SARS-CoV-2 pneumonia using a semi quantitative score.

ABSTRACT: OBJECTIVES:The purpose is to assess the ability of low-dose CT (LDCT) to determine lung involvement in SARS-CoV-2 pneumonia and to describe a COVID19-LDCT severity score. MATERIALS AND METHODS:Patients with SARS-CoV-2 infection confirmed by RT-PCR were retrospectively analysed. Clinical data, the National Early Warning Score (NEWS) and imaging features were recorded. Lung features included ground-glass opacities (GGO), areas of consolidation and crazy paving patterns. The COVID19-LDCT score was calculated by summing the score of each segment from 0 (no involvement) to 10 (severe impairment). Univariate analysis was performed to explore predictive factor of high COVID19-LDCT score. The nonparametric Mann-Whitney test was used to compare groups and a Spearman correlation used with p<0.05 for significance. RESULTS:Eighty patients with positive RT-PCR were analysed. The mean age was 55 years ± 16, with 42 males (53%). The most frequent symptoms were fever (60/80, 75%) and cough (59/80, 74%), the mean NEWS was 1.7±2.3. All LDCT could be analysed and 23/80 (28%) were normal. The major imaging finding was GGOs in 56 cases (67%). The COVID19-LDCT score (mean value = 19±29) was correlated with NEWS (r = 0.48, p<0.0001). No symptoms were risk factor to have pulmonary involvement. Univariate analysis shown that dyspnea, high respiratory rate, hypertension and diabetes are associated to a COVID19-LDCT score superior to 50. CONCLUSIONS:COVID19-LDCT score did correlate with NEWS. It was significantly different in the clinical low-risk and high-risk groups. Further work is needed to validate the COVID19-LDCT score against patient prognosis.

SUBMITTER: Leger T

PROVIDER: S-EPMC7608883 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Low-dose chest CT for diagnosing and assessing the extent of lung involvement of SARS-CoV-2 pneumonia using a semi quantitative score.

Leger Thomas T Jacquier Alexis A Barral Pierre-Antoine PA Castelli Maxime M Finance Julie J Lagier Jean-Christophe JC Million Matthieu M Parola Philippe P Brouqui Philippe P Raoult Didier D Bartoli Axel A Gaubert Jean-Yves JY Habert Paul P

PloS one 20201103 11

<h4>Objectives</h4>The purpose is to assess the ability of low-dose CT (LDCT) to determine lung involvement in SARS-CoV-2 pneumonia and to describe a COVID19-LDCT severity score.<h4>Materials and methods</h4>Patients with SARS-CoV-2 infection confirmed by RT-PCR were retrospectively analysed. Clinical data, the National Early Warning Score (NEWS) and imaging features were recorded. Lung features included ground-glass opacities (GGO), areas of consolidation and crazy paving patterns. The COVID19- ...[more]

PMID: 33141845

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Project description:Background The role and performance of chest CT in the diagnosis of the coronavirus disease 2019 (COVID-19) pandemic remains under active investigation. Purpose To evaluate the French national experience using Chest CT for COVID-19, results of chest CT and RT-PCR were compared together and with the final discharge diagnosis used as reference standard. Materials and Methods A structured CT scan survey (NCT04339686) was sent to 26 hospital radiology departments in France between March 2 and April 24 2020. These dates correspond to the peak of the national COVID-19 epidemic. Radiology departments were selected to reflect the estimated geographical prevalence heterogeneities of the epidemic. All symptomatic patients suspected of having a COVID-19 pneumonia who underwent within 48 hours both initial chest CT and at least one RT-PCR testing were included. The final discharge diagnosis, based on multiparametric items, was recorded. Data for each center were prospectively collected and gathered each week. Test efficacy was determined by using Mann-Whitney Test, Student's t-test, Chi-square test and Pearson's correlation. A p value <.05 determined statistical significance. Results Twenty-six of 26 hospital radiology departments responded to the survey with 7500 patients entered; 2652 did not have RT-PCR results or had unknown or excess delay between RT-PCR and CT. After exclusions, 4824 patients (mean age 64, ± 19 yrs, 2669 males) were included. Using final diagnosis as the reference, 2564 of the 4824 patients were positive for COVID-19 (53%). Sensitivity, specificity, NPV and PPV of chest CT for diagnosing COVID-19 were 2319/2564 (90%, 95% confidence interval [CI]: 89, 91), 2056/2260 (91%, 95%CI: 91, 92%), 2056/2300 (89%, 95%CI; 87, 90%) and 2319/2524 (92%, 95%CI 91, 93%) respectively. There was no significant difference for chest CT efficacy among the 26 geographically separate sites, each with varying amounts of disease prevalence. Conclusion Use of chest CT for the initial diagnosis and triage of suspected COVID-19 patients was successful.

Project description:Current methods for organ and effective dose estimations in pediatric CT are largely patient generic. Physical phantoms and computer models have only been developed for standard/limited patient sizes at discrete ages (e.g., 0, 1, 5, 10, 15 years old) and do not reflect the variability of patient anatomy and body habitus within the same size/age group. In this investigation, full-body computer models of seven pediatric patients in the same size/protocol group (weight: 11.9-18.2 kg) were created based on the patients' actual multi-detector array CT (MDCT) data. Organs and structures in the scan coverage were individually segmented. Other organs and structures were created by morphing existing adult models (developed from visible human data) to match the framework defined by the segmented organs, referencing the organ volume and anthropometry data in ICRP Publication 89. Organ and effective dose of these patients from a chest MDCT scan protocol (64 slice LightSpeed VCT scanner, 120 kVp, 70 or 75 mA, 0.4 s gantry rotation period, pitch of 1.375, 20 mm beam collimation, and small body scan field-of-view) was calculated using a Monte Carlo program previously developed and validated to simulate radiation transport in the same CT system. The seven patients had normalized effective dose of 3.7-5.3 mSv/100 mAs (coefficient of variation: 10.8%). Normalized lung dose and heart dose were 10.4-12.6 mGy/100 mAs and 11.2-13.3 mGy/100 mAs, respectively. Organ dose variations across the patients were generally small for large organs in the scan coverage (<7%), but large for small organs in the scan coverage (9%-18%) and for partially or indirectly exposed organs (11%-77%). Normalized effective dose correlated weakly with body weight (correlation coefficient: r=-0.80). Normalized lung dose and heart dose correlated strongly with mid-chest equivalent diameter (lung: r=-0.99, heart: r=-0.93); these strong correlation relationships can be used to estimate patient-specific organ dose for any other patient in the same size/protocol group who undergoes the chest scan. In summary, this work reported the first assessment of dose variations across pediatric CT patients in the same size/protocol group due to the variability of patient anatomy and body habitus and provided a previously unavailable method for patient-specific organ dose estimation, which will help in assessing patient risk and optimizing dose reduction strategies, including the development of scan protocols.

Project description:ObjectiveFew studies have explored the clinical feasibility of using deep-learning reconstruction to reduce the radiation dose of CT. We aimed to compare the image quality and lung nodule detectability between chest CT using a quarter of the low dose (QLD) reconstructed with vendor-agnostic deep-learning image reconstruction (DLIR) and conventional low-dose (LD) CT reconstructed with iterative reconstruction (IR).Materials and methodsWe retrospectively collected 100 patients (median age, 61 years [IQR, 53-70 years]) who received LDCT using a dual-source scanner, where total radiation was split into a 1:3 ratio. QLD CT was generated using a quarter dose and reconstructed with DLIR (QLD-DLIR), while LDCT images were generated using a full dose and reconstructed with IR (LD-IR). Three thoracic radiologists reviewed subjective noise, spatial resolution, and overall image quality, and image noise was measured in five areas. The radiologists were also asked to detect all Lung-RADS category 3 or 4 nodules, and their performance was evaluated using area under the jackknife free-response receiver operating characteristic curve (AUFROC).ResultsThe median effective dose was 0.16 (IQR, 0.14-0.18) mSv for QLD CT and 0.65 (IQR, 0.57-0.71) mSv for LDCT. The radiologists' evaluations showed no significant differences in subjective noise (QLD-DLIR vs. LD-IR, lung-window setting; 3.23 ± 0.19 vs. 3.27 ± 0.22; P = .11), spatial resolution (3.14 ± 0.28 vs. 3.16 ± 0.27; P = .12), and overall image quality (3.14 ± 0.21 vs. 3.17 ± 0.17; P = .15). QLD-DLIR demonstrated lower measured noise than LD-IR in most areas (P < .001 for all). No significant difference was found between QLD-DLIR and LD-IR for the sensitivity (76.4% vs. 72.2%; P = .35) or the AUFROCs (0.77 vs. 0.78; P = .68) in detecting Lung-RADS category 3 or 4 nodules. Under a noninferiority limit of -0.1, QLD-DLIR showed noninferior detection performance (95% CI for AUFROC difference, -0.04 to 0.06).ConclusionQLD-DLIR images showed comparable image quality and noninferior nodule detectability relative to LD-IR images.

Project description:ObjectivesGround-glass opacity and consolidation are recognized typical features of Coronavirus disease-19 (COVID-19) pneumonia on Chest CT, yet ancillary findings have not been fully described. We aimed to describe ancillary findings of COVID-19 pneumonia on CT, to define their prevalence, and investigate their association with clinical data.MethodsWe retrospectively reviewed our CT chest cases with coupled reverse transcriptase polymerase chain reaction (rt-PCR). Patients with negative rt-PCR or without admission chest CT were excluded. Ancillary findings included: vessel enlargement, subpleural curvilinear lines, dependent subpleural atelectasis, centrilobular solid nodules, pleural and/or pericardial effusions, enlarged mediastinal lymph nodes. Continuous data were expressed as median and 95% confidence interval (95% CI) and tested by Mann-Whitney U test.ResultsAncillary findings were represented by 106/252 (42.1%, 36.1 to 48.2) vessel enlargement, 50/252 (19.8%, 15.4 to 25.2) subpleural curvilinear lines, 26/252 (10.1%, 7.1 to 14.7) dependent subpleural atelectasis, 15/252 (5.9%, 3.6 to 9.6) pleural effusion, 15/252 (5.9%, 3.6 to 9.6) mediastinal lymph nodes enlargement, 13/252 (5.2%, 3 to 8.6) centrilobular solid nodules, and 6/252 (2.4%, 1.1 to 5.1) pericardial effusion. Air space disease was more extensive in patients with vessel enlargement or centrilobular solid nodules (p < 0.001). Vessel enlargement was associated with longer history of fever (p = 0.035) and lower admission oxygen saturation (p = 0.014); dependent subpleural atelectasis with lower oxygen saturation (p < 0.001) and higher respiratory rate (p < 0.001); mediastinal lymph nodes with shorter history of cough (p = 0.046); centrilobular solid nodules with lower prevalence of cough (p = 0.023), lower oxygen saturation (p < 0.001), and higher respiratory rate (p = 0.032), and pericardial effusion with shorter history of cough (p = 0.015). Ancillary findings associated with longer hospital stay were subpleural curvilinear lines (p = 0.02), whereas centrilobular solid nodules were associated with higher rate of intensive care unit admission (p = 0.01).ConclusionTypical high-resolution CT findings of COVID-19 pneumonia are frequently associated with ancillary findings that variably associate with disease extent, clinical parameters, and disease severity.Advances in knowledgeAncillary findings might reflect the broad range of heterogeneous mechanisms in severe acute respiratory syndrome from viral pneumonia, and potentially help disease phenotyping.

Dataset Information

Low-dose chest CT for diagnosing and assessing the extent of lung involvement of SARS-CoV-2 pneumonia using a semi quantitative score.

Publications

Low-dose chest CT for diagnosing and assessing the extent of lung involvement of SARS-CoV-2 pneumonia using a semi quantitative score.

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