Comparison of Radiographic Appearances of Covid-19 Pneumonia and Influenza Pneumonia

Comparison of Radiographic Appearances of Covid-19 Pneumonia and Influenza Pneumonia | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Comparison of Radiographic Appearances of Covid-19 Pneumonia and Influenza Pneumonia Samuel Nguku Gitau, Edward C Nganga, Paul Kareithi, Jasmit Shah, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1649535/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background The novel coronavirus (COVID-19) first discovered in December 2019 has infected over 400 million people worldwide with over 5 million deaths as of February 2022. Differentiation of the imaging appearance of COVID-19 pneumonia and the endemic seasonal influenza pneumonia is clinically important as it may help give direction on patient care. The aim of this study was to evaluate for differences in radiographic appearances of COVID-19 pneumonia and influenza pneumonia. Methods This was a cross sectional descriptive study comparing patterns of pneumonia on radiographs and CT examinations of patients diagnosed with COVID-19 pneumonia and influenza pneumonia. The comparisons included the predominant radiographic pattern of pneumonia (alveolar, interstitial, ground glass or nodular) and the extent of pneumonia (lobar, multi-lobar or diffuse involvement). The radiographic severity of disease was classified as mild, moderate or severe based on extent of lung involvement. Results A total of 99 examinations were reviewed, 52 chest x-rays and 47 CT chest examinations. Ground glass opacification had the highest sensitivity (89.5%) and negative predictive value (72.7%) in identification of COVID-19 pneumonia while reticulonodular opacities had the highest sensitivity (53.3%) and negative predictive value (68.2%) for identification of influenza pneumonia. The majority (90.9%) of Influenza pneumonia cases had lower CORADS scores of 2 and 3. Conclusion The predominant radiographic pattern in COVID-19 pneumonia was ground glass opacities while that for influenza pneumonia was reticulonodular opacities. This may help differentiate between the two pneumonias which can have similar clinical presentation but often variable severity and outcome. COVID-19 pneumonia Influenza pneumonia radiographic ground glass reticulonodular Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Since the outbreak of the coronavirus disease (COVID-19) in December 2019, the number of people diagnosed with the disease worldwide is over 400 million with over 5 million deaths as of February 2022 ( 1 ). The rapid spread of COVID-19 has caused a great health concern worldwide. The diagnosis of COVID-19 pneumonia is through real-time reverse-transcription polymerase chain reaction (RT-PCR) test predominantly performed from a nasopharyngeal and/or oropharyngeal swab ( 2 ). Although this is the accepted standard test, it has a few limitations including relatively long turnaround times, low availability and variable sensitivities. Radiological investigations (chest radiographs and computed tomography) play a supportive role in the diagnosis of COVID-19 ( 3 , 4 ). Imaging is also useful in stratification of disease severity and assessment of response to treatment. Influenza causes seasonal lower respiratory infections as well as periodic pandemics ( 5 ). Of all the respiratory viruses, influenza causes the greatest morbidity and mortality. Differentiation of the imaging appearance of pneumonia caused by SARS-CoV-2 and Influenza is clinically important as it helps give direction on patient care, in the administration of early disease-specific therapy while awaiting confirmation of the diagnosis. A review of CT chest findings of 1014 patients in Wuhan, China with confirmed COVID-19 pneumonia found that 97% of cases had CT findings of pneumonia ( 4 ). This may however be true for a large proportion of cases of pneumonia and therefore, and although it implies a high sensitivity, those observations do not address the discrimination of COVID-19 pneumonia from that caused by other viruses. The most common radiographic and CT appearances of COVID-19 pneumonia are bilateral and subpleural ground-glass opacities in multiple lobes but mainly affecting lower lobes progressing to “crazy-paving” patterns and consolidation ( 4 , 5 ). CT signs gradually improve beginning approximately 14 days post-symptom onset. On the other hand, the predominant imaging features of influenza pneumonia are bilateral reticulonodular opacities with or without focal areas of consolidation, usually in the lower lobes. Although there is potential overlap in imaging appearances of COVID-19 pneumonia and influenza pneumonia, the presence of reticulonodular opacities in influenza pneumonia may help in differentiating the two especially on CT scans. There is however paucity of literature on whether chest radiographs would perform similarly ( 6 , 7 ). Chest radiographs are the first line imaging modality for most patients presenting with pneumonia and are more widely available than CT scans. An additional advantage of chest radiographs is that they can be performed as portable at the point of care, with less logistical requirements therefore reducing chances of inadvertent exposure to staff and other patients. Despite this, there is paucity of literature on radiographic appearances of COVID-19 pneumonia and how this compares with influenza pneumonia with most publications having focused on CT findings. It is important to see how imaging appearances of pneumonia caused by these two viruses compares on chest radiographs in addition to findings on CT scan. This study therefore aims to evaluate differences in imaging appearances between COVID-19 pneumonia and influenza pneumonia. Methods The influenza cases were identified from a prospective study of community acquired pneumonia in which the enrolled patients had a respiratory specimen evaluated by PCR for all the major viral causes of lower respiratory infection ( 8 ). The study started in May 2019 and enrollment was discontinued in March 2020, the time the first case of COVID-19 pneumonia was identified in Kenya. At the onset of the COVID-19 pandemic, all patients admitted to the hospital were tested by reverse transcriptase polymerase chain reaction (RT-PCR) for COVID-19 pneumonia. Of those with positive results, patients who had imaging (radiograph or CT Chest) performed within 48 hours of diagnosis were included. We included patients between March 2019 and July 2019 with a diagnosis of COVID-19 who met the inclusion criteria. Patients with pulmonary infection within six weeks prior to presentation or chronic pulmonary disease that was likely to compromise assessment of the diagnosis or outcome of pneumonia including chronic pulmonary obstructive disease and interstitial lung disease were excluded. A total of 77 patients with COVID-19 pneumonia met our inclusion criteria. The imaging (chest radiographs and CT chest examinations) for the two arms were anonymised and provided for review. Two radiologists (with 9 and 8 years’ experience respectively), blinded to the final diagnosis, reviewed the radiographs and CT examinations and provided a score of the predominant pattern of disease, pattern distribution, the extent of pneumonia and likely diagnosis. The outcome categorical variables were the predominant radiographic and CT pattern of pneumonia (alveolar, interstitial, ground glass or nodular), the pattern of distribution (lobar, multi-lobar or diffuse involvement), and the radiographic extent/severity of pneumonia (mild, moderate or severe). The extent/severity index was based on qualitative evaluation of the percentage of lung parenchyma involved (mild 50%). The presence of other findings including fibrosis, pleural effusion and lymphadenopathy was also recorded. Approval for this study was obtained from the Institutional Ethics and Review Committee at the Aga Khan University, Nairobi. Data analysis Categorical data was presented as frequencies and percentages. Comparison of groups was evaluated using Fishers Exact test. Diagnostic tests such as sensitivity and specificity were presented as percentages. Interrater reliability was examined based in the Kappa statistic. Data analysis was performed using SPSS statistical software V.20.0 (IBM). The significance level was set at α = 0.05, and all tests were two tailed. Results A total of 99 examinations were reviewed, 52 chest x-rays and 47 CT chest examinations. The predominant radiographic pattern on both imaging modalities was ground glass opacification (68/99; 69%) and majority (61.6%) were peripherally distributed and in the lower lobes (74.7%). A total of 12 examinations; (9 chest x-rays and 3 CT examinations) did not have features of pneumonia. Pleural effusion and lymphadenopathy was present in less than 10% of patients. The majority of patients (39.4%) had mild severity of pneumonia as assessed on radiographs and CT scan. These baseline characteristics are summarized in Table 1 . Table 1 Baseline characteristics of the chest x-ray and CT findings and radiological impression Total Chest Radiograph CT Chest (N = 99) (N = 52) (N = 47) Patterns of Pneumonia Consolidation 25 25.3% 11 21.2% 14 29.8% Ground glass 68 68.7% 29 55.8% 39 83.0% No Opacities 12 12.1% 9 17.3% 3 6.4% Nodular 16 16.2% 10 19.2% 6 12.8% Reticular 28 28.3% 9 17.3% 19 40.4% Reticulonodular 22 22.2% 17 32.7% 5 10.6% Predominant Pattern Consolidation 8 8.1% 5 9.6% 3 6.4% Ground glass 57 57.6% 23 44.2% 34 72.3% Nodular 3 3.0% 3 5.8% 0 0.0% Reticular 4 4.0% 2 3.8% 2 4.3% Reticulonodular 15 15.2% 10 19.2% 5 10.6% No Opacities 12 12.1% 9 17.3% 3 6.4% Distribution of Opacities Central 36 36.4% 21 40.4% 15 31.9% Diffuse 32 32.3% 17 32.7% 15 31.9% Peripheral 61 61.6% 26 50.0% 35 74.5% Predominant Distribution of Opacities Central 17 17.2% 12 23.1% 5 10.6% Diffuse 21 21.2% 13 25.0% 8 17.0% Peripheral 49 49.5% 18 34.6% 31 66.0% No Opacities 12 12.1% 9 17.3% 3 6.4% Location of Opacities Left Lower Lung 64 64.6% 24 46.2% 40 85.1% Left Upper Lung 58 58.6% 19 36.5% 39 83.0% Right Lower Lung 79 79.8% 36 69.2% 43 91.5% Right Middle Lung 59 59.6% 20 38.5% 39 83.0% Right Upper Lung 61 61.6% 20 38.5% 41 87.2% Predominant Location of Opacities Lower lung 74 74.7% 39 75.0% 35 74.5% Upper lung 13 13.1% 4 7.7% 9 19.1% No Opacities 12 12.1% 9 17.3% 3 6.4% Fibrosis No 81 81.8% 49 94.2% 32 68.1% Yes 18 18.2% 3 5.8% 15 31.9% Septal Thickening No 62 62.6% 43 82.7% 19 40.4% Yes 37 37.4% 9 17.3% 28 59.6% Pleural Effusion No 90 90.9% 46 88.5% 44 93.6% Yes 9 9.1% 6 11.5% 3 6.4% Location of Pleural Effusion Left 1 11.1% 1 16.7% 0 0.0% Right 6 66.7% 5 83.3% 1 33.3% Bilateral 2 22.2% 0 0.0% 2 66.7% Lymphadenopathy No 93 93.9% 52 100.0% 41 87.2% Yes 6 6.1% 0 0.0% 6 12.8% Locations of Lymphadenopathy Hilar 3 50.0% 0 0.0% 3 50.0% Mediastinal 4 66.7% 0 0.0% 4 66.7% Predominant Location of Lymphadenopathy Hilar 2 33.3% 0 0.0% 2 33.3% Mediastinal 4 66.7% 0 0.0% 4 66.7% Severity of Pneumonia No pneumonia 14 14.1% 10 19.2% 4 8.5% Mild 39 39.4% 22 42.3% 17 36.2% Moderate 22 22.2% 7 13.5% 15 31.9% Severe 24 24.2% 13 25.0% 11 23.4% Radiologist Diagnosis COVID 19 49 49.5% 15 28.8% 34 72.3% Influenza 29 29.3% 20 38.5% 9 19.1% No pneumonia 14 14.1% 10 19.2% 4 8.5% Other pneumonia 7 7.0% 7 13.5% 0 0.0% CORADS Score 1 12 12.1% 9 17.3% 3 6.4% 2 31 31.3% 22 42.3% 9 19.1% 3 9 9.1% 7 13.5% 2 4.3% 4 8 8.1% 7 13.5% 1 2.1% 5 39 39.4% 7 13.5% 32 68.1% Laboratory diagnosis COVID-19 77 77.8% 37 71.2% 40 85.1% Influenza 22 22.2% 15 28.8% 7 14.9% The radiographic pattern of pneumonia was associated with the etiology of pneumonia (COVID-19 or influenza pneumonia) p-value <0.001 (Table 2). The ground glass was more common in COVID-19 diagnosis whereas the reticulonodular was more common in the Influenza diagnosis. Table 2 Association of radiographic patterns and the type of pneumonia shows most patients with COVID-19 pneumonia had ground glass opacification compared to reticulonodular opacities in influenza pneumonia. Laboratory diagnosis P Value COVID-19 Influenza Predominant Pattern Consolidation 2 (2.6%) 6 (27.3%) <0.001 Ground Glass 51 (66.2%) 6 (27.3%) Nodular 2 (2.6%) 1 (4.5%) Reticular 3 (3.9%) 1 (4.5%) Reticulonodular 8 (10.4%) 7 (31.8%) Normal 11 (14.3%) 1 (4.5%) The lower lobes were the predominant location of radiographic opacities for both COVID-19 (74.0%) and influenza pneumonia (77.3%). 14.3% of patients with COVID 19 had normal imaging compared to 4.5% of influenza pneumonia patients. Patients with reticulonodular opacities with background ground glass opacification had a 91% reduction in the odds of having COVID-19 pneumonia (CI: 0.02-0.037; p value=0.001). Table 3 Association of radiographic location and the type of pneumonia shows predominant lower lobe predilection in both COVID 19 and influenza pneumonia. Laboratory diagnosis P Value COVID-19 Influenza Predominant Location Lower Lung 57 (74.0%) 17 (77.3%) 0.384 Upper Lung 9 (11.7%) 4 (18.2%) Normal 11 (14.3%) 1 (4.5%) Ground glass opacification had the highest sensitivity (89.5%) and negative predictive value (72.7%) in identification of COVID-19 pneumonia. Reticulonodular opacities on the other hand had the highest sensitivity (53.3%) and negative predictive value (68.2%) for identification of influenza pneumonia (Table 4). Table 4 Diagnostic accuracy of the two predominant radiographic patterns showed high sensitivity and negative predictive value (NPV) of ground glass opacities for diagnosing COVID-19 pneumonia and moderate sensitivity and NPV of reticulonodular opacities for diagnosing influenza pneumonia. Pneumonia Type Overall Pattern Sensitivity Specificity PPV NPV COVID-19 Vs Influenza Ground Glass 89.47% 38.10% 66.23% 72.73% Reticulonodular 53.33% 17.86% 10.39% 68.18% Radiographic CORADS scores of 4 and 5 were associated a diagnosis of COVID-19 pneumonia with 59.8% of COVID-19 cases having a score of 4 or 5. The majority (90.9%) of Influenza pneumonia cases had a CORADS score of 2 and 3 on a radiographic assessment (table 5 and figure 1). Table 5 Comparison of CORADS score and laboratory diagnosis of pneumonia shows higher CORADS scores (4 and 5) for COVID-19 pneumonia and lower score (2) for influenza pneumonia. Laboratory diagnosis P Value COVID-19 Influenza CORADS Score 1 11(14.3%) 1 (4.5%) < 0.001 2 14(18.2%) 17 (77.3%) 3 6 (7.8%) 3 (13.6%) 4 8 (10.4%) 0 (0.0%) 5 38(49.4%) 1 (4.5%) The inter-reader agreement between the two radiologists comparing the radiological impression and the final laboratory diagnosis was almost perfect with kappa score of 0.947 (p value <0.001). Discussion The radiographic pattern of pneumonia predicted the viral etiology (COVID-19 or influenza pneumonia) with ground glass opacification having the highest positive predictive value for COVID-19 pneumonia compared to reticulonodular opacities for influenza pneumonia. The presence of reticulonodular opacities on imaging significantly reduces the likelihood of COVID-19 pneumonia. These findings can help differentiate the two etiologies of pneumonia which may have similar clinical presentation. Our findings are similar to the few published studies that have compared the two. A study by Liu M. et al which compared CT findings between COVID-19 pneumonia and influenza found that peripherally distributed rounded opacities and interlobular septal thickening and absence of nodules may help differentiate COVID-19 from influenza pneumonia ( 9 ). Similar findings were observed by Bai et al where radiologists had high accuracy (over 80%) in differentiating COVID-19 pneumonia from other viral pneumonia on CT chest ( 7 ). Both studies included chest CT findings only. Our study included both chest radiographs and CT examinations and both demonstrated similar findings with the pattern of lung infiltrates predicting the etiology of pneumonia. To the best of our knowledge, this is the first study demonstrating the value of chest radiographs in differentiation of COVID-19 from influenza pneumonia. Chest x-rays are the first line of imaging of evaluation in patients with pneumonia and widely available including at most primary care centers. Chest x-rays can therefore be integrated in the initial determination of the etiology of pneumonia where either COVID-19 or influenza pneumonia are suspected. The few studies comparing the performance of chest x-rays have had conflicting results. A case control study by Kim et al comparing clinical and chest imaging findings using CT and chest x-rays found that although CT performed exceptionally in differentiating the two, chest x-rays underestimated lung involvement ( 10 ). This is because there remains overlap of radiographic findings on CT between COVID-19 pneumonia and other pneumonias (viral and non-viral) which may make differentiation based solely on imaging findings difficult and clinical picture would help narrow the differential ( 11 ). The majority of patients (38.6%) in our study had mild severity of pneumonia as assessed on radiographs and CT scan. The extent or severity of COVID-19 pneumonia on imaging can be used to identify patients with severe pneumonia ( 12 ). The percentage of parenchyma involvement subjectively evaluated on imaging can therefore help categorize pneumonia severity as mild or severe and potentially guide treatment. The CORADS score has been widely used and validated as a scoring system for the probability of COVID-19 pneumonia on CT chest imaging with pooled frequency of COVID-19 in CORADS 4 and 5 at 61.9% and 89.7% respectively in a meta-analysis ( 13 ). The combined frequency of COVID-19 CORADS scores 4 and 5 in our study was 59.8% while majority of patients with influenza pneumonia had a CORADS score of 3 (indeterminate). This underscores the significant overlap of radiographic findings of COVID 19 pneumonia and other pneumonias. The relatively lower performance is likely attributable to the use of Chest x-rays in approximately half of our patients which perform less accurately than CT scans ( 14 ). There was excellent inter-reader agreement between the two radiologists, which is better than the moderate agreement observed in the study by Bai et al ( 7 ). Their study used CT scan only in the evaluation while our study had both CT and chest x-rays. The excellent inter-rater agreement in our study may be secondary to response bias as the two readers were expected to provide an impression of either COVID-19 pneumonia or Influenza pneumonia and may not mirror actual clinical practice. The other limitation of this study was selection bias due to the retrospective nature of the study. However, the radiographic patterns of pneumonia that were the main study variables in this study are less subject to the bias. Conclusion In conclusion, ground glass opacification on chest x-rays and CT chest imaging can help differentiate COVID-19 pneumonia from influenza pneumonia while the presence of reticulonodular opacities is a strong predictor of influenza pneumonia. These findings can help differentiate the two etiologies of pneumonia which may have similar clinical presentation. Declarations Competing interests: None Funding: No funding was available for this study Authors Contribution: SNG, EN and RA contributed in conception and design of the study. SNG, EN and PK contributed in data collection. JS performed data processing and analysis. The first draft was written by SNG. All authors read and approved the final manuscript. Acknowledgement: Evelyne Khamali who assisted with data entry. References Coronavirus disease (COVID-19) – World Health Organization [Internet]. [cited 2022 Feb 25]. Available from: https://www.who.int/emergencies/diseases/novel-coronavirus-2019 Laboratory testing for 2019 novel coronavirus (2019-nCoV) in suspected human cases [Internet]. [cited 2022 Feb 25]. Available from: https://www.who.int/publications-detail-redirect/10665-331501 Li Y, Xia L. Coronavirus Disease 2019 (COVID-19): Role of Chest CT in Diagnosis and Management. AJR Am J Roentgenol. 2020 Jun;214(6):1280–6. Ai T, Yang Z, Hou H, Zhan C, Chen C, Lv W, et al. Correlation of Chest CT and RT-PCR Testing for Coronavirus Disease 2019 (COVID-19) in China: A Report of 1014 Cases. Radiology. 2020 Aug;296(2):E32–40. Kanne JP, Little BP, Chung JH, Elicker BM, Ketai LH. Essentials for Radiologists on COVID-19: An Update-Radiology Scientific Expert Panel. Radiology. 2020 Aug;296(2):E113–4. Koo HJ, Lim S, Choe J, Choi SH, Sung H, Do KH. Radiographic and CT Features of Viral Pneumonia. Radiographics. 2018 Jun;38(3):719–39. Bai HX, Hsieh B, Xiong Z, Halsey K, Choi JW, Tran TML, et al. Performance of Radiologists in Differentiating COVID-19 from Non-COVID-19 Viral Pneumonia at Chest CT. Radiology. 2020 Aug;296(2):E46–54. Nambafu J, Achakolong M, Mwendwa F, Bwika J, Riunga F, Gitau S, et al. A prospective observational study of community acquired pneumonia in Kenya: the role of viral pathogens. BMC Infect Dis. 2021 Jul 23;21(1):703. Liu M, Zeng W, Wen Y, Zheng Y, Lv F, Xiao K. COVID-19 pneumonia: CT findings of 122 patients and differentiation from influenza pneumonia. Eur Radiol. 2020 Oct;30(10):5463–9. Kim SH, Wi YM, Lim S, Han KT, Bae IG. Differences in Clinical Characteristics and Chest Images between Coronavirus Disease 2019 and Influenza-Associated Pneumonia. Diagnostics (Basel). 2021 Feb 8;11(2):261. Duzgun SA, Durhan G, Demirkazik FB, Akpinar MG, Ariyurek OM. COVID-19 pneumonia: the great radiological mimicker. Insights into Imaging. 2020 Nov 23;11(1):118. Chest CT Severity Score: An Imaging Tool for Assessing Severe COVID-19 | Radiology: Cardiothoracic Imaging [Internet]. [cited 2022 Feb 25]. Available from: https://pubs.rsna.org/doi/full/ 10.1148/ryct.2020200047 Kwee RM, Adams HJA, Kwee TC. Diagnostic Performance of CO-RADS and the RSNA Classification System in Evaluating COVID-19 at Chest CT: A Meta-Analysis. Radiology: Cardiothoracic Imaging. 2021 Feb;3(1):e200510. Borakati A, Perera A, Johnson J, Sood T. Diagnostic accuracy of X-ray versus CT in COVID-19: a propensity-matched database study. BMJ Open. 2020 Nov 6;10(11):e042946. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-1649535","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":114090214,"identity":"9846367e-ff42-43ee-bdef-5b746f678680","order_by":0,"name":"Samuel Nguku Gitau","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5UlEQVRIiWNgGAWjYNCCAgYGNhD9gSEBRBkQoQWohg2oh3EGSVpA1jDzEKNFvv10msQHAwa7PvnmZ9I2NWmJDezN2yTwmn8md5vkDAOG5DY2NjPpnGM5iQ08x8rwa2HI3SbNA9QC9AtQC1tFYoNEjhleLfL9b7dJ/wFrYf8mbfEPqEX+DX4tDDeAtgDtsmNj4zGTZmwDOkyCB78WgxtvN1v2GEgksLHlFFv29qUZt/GkFVvgd1juxhs/Kmzs5ZuPAxnfkmX72Q9vvIHXYRAgkdgAY7IRoRwM7IlVOApGwSgYBSMQAAAIAD8L3/NubQAAAABJRU5ErkJggg==","orcid":"","institution":"Aga Khan University Nairobi","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Samuel","middleName":"Nguku","lastName":"Gitau","suffix":""},{"id":114090215,"identity":"49f323f6-647b-4132-a3b3-7a0c4502a4eb","order_by":1,"name":"Edward C Nganga","email":"","orcid":"","institution":"Aga Khan University Nairobi","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Edward","middleName":"C","lastName":"Nganga","suffix":""},{"id":114090216,"identity":"a2ec7fb1-09a7-4131-b643-cc8f8e2c1fcb","order_by":2,"name":"Paul Kareithi","email":"","orcid":"","institution":"Aga Khan University Nairobi","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Paul","middleName":"","lastName":"Kareithi","suffix":""},{"id":114090217,"identity":"32d4d025-7db5-472f-94f4-39b77e4275c1","order_by":3,"name":"Jasmit Shah","email":"","orcid":"","institution":"Aga Khan University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jasmit","middleName":"","lastName":"Shah","suffix":""},{"id":114090218,"identity":"0f9e9731-e4f9-4818-a4c9-7ca515ca068b","order_by":4,"name":"Rodney Adam","email":"","orcid":"","institution":"Aga Khan University Nairobi","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Rodney","middleName":"","lastName":"Adam","suffix":""}],"badges":[],"createdAt":"2022-05-12 12:29:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1649535/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1649535/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":22797890,"identity":"e35a0467-41b8-4c6f-8c98-44ee7cde4b82","added_by":"auto","created_at":"2022-06-17 22:26:20","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1211589,"visible":true,"origin":"","legend":"\u003cp\u003eChest radiograph of a 64 year old with cough and chest pain for 3 days shows centrally located reticulonodular opacities suggestive of pneumonia.\u0026nbsp;Influenza pneumonia was confirmed with PCR testing.\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-1649535/v1/5509a1b149338652aad98327.png"},{"id":22797938,"identity":"79911b04-efdc-4e91-a079-05ac9dd5316a","added_by":"auto","created_at":"2022-06-17 22:31:20","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":830321,"visible":true,"origin":"","legend":"\u003cp\u003eChest radiograph of a 72 year with cough and shortness of breath for 2 days shows peripherally located ground glass opacities. COVID-19 pneumonia was confirmed on PCR.\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-1649535/v1/f974241b003e58813bca39cf.png"},{"id":22797889,"identity":"6a4e1d7a-e42b-45ec-912f-5f75f66c0862","added_by":"auto","created_at":"2022-06-17 22:26:20","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":153285,"visible":true,"origin":"","legend":"\u003cp\u003eAxial CT images of the chest of a 56 year old with 3 days history of cough and fever shows reticulonodular opacities in both lungs best demonstrated on the maximum intensity projection (MIP) images on the left. Influenza pneumonia was confirmed on PCR.\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-1649535/v1/b0b521e5c37b07f01e357683.png"},{"id":22797937,"identity":"af2d0f13-1469-4a5d-a7f3-ac19172a5496","added_by":"auto","created_at":"2022-06-17 22:31:20","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":170405,"visible":true,"origin":"","legend":"\u003cp\u003eAxial CT images of the chest of a 45 year old with 4 days history of cough, fever and shortness of breath show peripherally located ground glass opacities in both lungs characteristic of COVID-19 pneumonia. COVID-19 was confirmed on PCR.\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-1649535/v1/420d15eac27545c7d90af543.png"},{"id":22797887,"identity":"f5f3e820-65bf-49b5-a149-04c456baf1bb","added_by":"auto","created_at":"2022-06-17 22:26:20","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":22450,"visible":true,"origin":"","legend":"\u003cp\u003eBar graph comparing CORADS score and final pneumonia diagnosis shows higher CORADS scores in COVID-19 pneumonia compared to influenza pneumonia.\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-1649535/v1/54478155e0f12142d4f8241b.jpeg"},{"id":24026151,"identity":"747ae38f-7813-48a8-8ac1-1602bb353157","added_by":"auto","created_at":"2022-07-19 09:29:21","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2487864,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1649535/v1/9163f767-f7cd-4c06-96ef-f0b7f8e712d5.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eComparison of Radiographic Appearances of Covid-19 Pneumonia and Influenza Pneumonia\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSince the outbreak of the coronavirus disease (COVID-19) in December 2019, the number of people diagnosed with the disease worldwide is over 400\u0026nbsp;million with over 5\u0026nbsp;million deaths as of February 2022 (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). The rapid spread of COVID-19 has caused a great health concern worldwide. The diagnosis of COVID-19 pneumonia is through real-time reverse-transcription polymerase chain reaction (RT-PCR) test predominantly performed from a nasopharyngeal and/or oropharyngeal swab (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Although this is the accepted standard test, it has a few limitations including relatively long turnaround times, low availability and variable sensitivities. Radiological investigations (chest radiographs and computed tomography) play a supportive role in the diagnosis of COVID-19 (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Imaging is also useful in stratification of disease severity and assessment of response to treatment. Influenza causes seasonal lower respiratory infections as well as periodic pandemics (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Of all the respiratory viruses, influenza causes the greatest morbidity and mortality. Differentiation of the imaging appearance of pneumonia caused by SARS-CoV-2 and Influenza is clinically important as it helps give direction on patient care, in the administration of early disease-specific therapy while awaiting confirmation of the diagnosis.\u003c/p\u003e \u003cp\u003eA review of CT chest findings of 1014 patients in Wuhan, China with confirmed COVID-19 pneumonia found that 97% of cases had CT findings of pneumonia (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). This may however be true for a large proportion of cases of pneumonia and therefore, and although it implies a high sensitivity, those observations do not address the discrimination of COVID-19 pneumonia from that caused by other viruses.\u003c/p\u003e \u003cp\u003eThe most common radiographic and CT appearances of COVID-19 pneumonia are bilateral and subpleural ground-glass opacities in multiple lobes but mainly affecting lower lobes progressing to \u0026ldquo;crazy-paving\u0026rdquo; patterns and consolidation (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). CT signs gradually improve beginning approximately 14 days post-symptom onset. On the other hand, the predominant imaging features of influenza pneumonia are bilateral reticulonodular opacities with or without focal areas of consolidation, usually in the lower lobes. Although there is potential overlap in imaging appearances of COVID-19 pneumonia and influenza pneumonia, the presence of reticulonodular opacities in influenza pneumonia may help in differentiating the two especially on CT scans. There is however paucity of literature on whether chest radiographs would perform similarly (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eChest radiographs are the first line imaging modality for most patients presenting with pneumonia and are more widely available than CT scans. An additional advantage of chest radiographs is that they can be performed as portable at the point of care, with less logistical requirements therefore reducing chances of inadvertent exposure to staff and other patients. Despite this, there is paucity of literature on radiographic appearances of COVID-19 pneumonia and how this compares with influenza pneumonia with most publications having focused on CT findings. It is important to see how imaging appearances of pneumonia caused by these two viruses compares on chest radiographs in addition to findings on CT scan. This study therefore aims to evaluate differences in imaging appearances between COVID-19 pneumonia and influenza pneumonia.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eThe influenza cases were identified from a prospective study of community acquired pneumonia in which the enrolled patients had a respiratory specimen evaluated by PCR for all the major viral causes of lower respiratory infection (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). The study started in May 2019 and enrollment was discontinued in March 2020, the time the first case of COVID-19 pneumonia was identified in Kenya.\u003c/p\u003e \u003cp\u003eAt the onset of the COVID-19 pandemic, all patients admitted to the hospital were tested by reverse transcriptase polymerase chain reaction (RT-PCR) for COVID-19 pneumonia. Of those with positive results, patients who had imaging (radiograph or CT Chest) performed within 48 hours of diagnosis were included. We included patients between March 2019 and July 2019 with a diagnosis of COVID-19 who met the inclusion criteria. Patients with pulmonary infection within six weeks prior to presentation or chronic pulmonary disease that was likely to compromise assessment of the diagnosis or outcome of pneumonia including chronic pulmonary obstructive disease and interstitial lung disease were excluded. A total of 77 patients with COVID-19 pneumonia met our inclusion criteria.\u003c/p\u003e \u003cp\u003eThe imaging (chest radiographs and CT chest examinations) for the two arms were anonymised and provided for review. Two radiologists (with 9 and 8 years\u0026rsquo; experience respectively), blinded to the final diagnosis, reviewed the radiographs and CT examinations and provided a score of the predominant pattern of disease, pattern distribution, the extent of pneumonia and likely diagnosis.\u003c/p\u003e \u003cp\u003eThe outcome categorical variables were the predominant radiographic and CT pattern of pneumonia (alveolar, interstitial, ground glass or nodular), the pattern of distribution (lobar, multi-lobar or diffuse involvement), and the radiographic extent/severity of pneumonia (mild, moderate or severe). The extent/severity index was based on qualitative evaluation of the percentage of lung parenchyma involved (mild\u0026thinsp;\u0026lt;\u0026thinsp;25%, moderate 25\u0026ndash;50% and severe\u0026thinsp;\u0026gt;\u0026thinsp;50%). The presence of other findings including fibrosis, pleural effusion and lymphadenopathy was also recorded.\u003c/p\u003e \u003cp\u003e Approval for this study was obtained from the Institutional Ethics and Review Committee at the Aga Khan University, Nairobi.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData analysis\u003c/h2\u003e \u003cp\u003eCategorical data was presented as frequencies and percentages. Comparison of groups was evaluated using Fishers Exact test. Diagnostic tests such as sensitivity and specificity were presented as percentages. Interrater reliability was examined based in the Kappa statistic. Data analysis was performed using SPSS statistical software V.20.0 (IBM). The significance level was set at α\u0026thinsp;=\u0026thinsp;0.05, and all tests were two tailed.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eA total of 99 examinations were reviewed, 52 chest x-rays and 47 CT chest examinations. The predominant radiographic pattern on both imaging modalities was ground glass opacification (68/99; 69%) and majority (61.6%) were peripherally distributed and in the lower lobes (74.7%). A total of 12 examinations; (9 chest x-rays and 3 CT examinations) did not have features of pneumonia. Pleural effusion and lymphadenopathy was present in less than 10% of patients. The majority of patients (39.4%) had mild severity of pneumonia as assessed on radiographs and CT scan. These baseline characteristics are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics of the chest x-ray and CT findings and radiological impression\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eChest Radiograph\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eCT Chest\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e\u003cb\u003e(N\u0026thinsp;=\u0026thinsp;99)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e\u003cb\u003e(N\u0026thinsp;=\u0026thinsp;52)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e\u003cb\u003e(N\u0026thinsp;=\u0026thinsp;47)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003ePatterns of Pneumonia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConsolidation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e21.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e29.8%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGround glass\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e68.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e55.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e83.0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo Opacities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e17.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6.4%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNodular\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e12.8%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReticular\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e17.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e40.4%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReticulonodular\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e32.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e10.6%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003ePredominant Pattern\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConsolidation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6.4%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGround glass\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e57.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e44.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e72.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNodular\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReticular\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReticulonodular\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e10.6%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo Opacities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e17.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6.4%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eDistribution of Opacities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCentral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e40.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e31.9%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDiffuse\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e32.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e31.9%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePeripheral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e61.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e50.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e74.5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003ePredominant Distribution of Opacities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCentral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e23.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e10.6%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDiffuse\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e25.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e17.0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePeripheral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e34.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e66.0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo Opacities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e17.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6.4%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eLocation of Opacities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLeft Lower Lung\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e64.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e46.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e85.1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLeft Upper Lung\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e58.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e36.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e83.0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRight Lower Lung\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e79.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e69.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e91.5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRight Middle Lung\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e59.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e38.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e83.0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRight Upper Lung\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e61.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e38.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e87.2%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003ePredominant Location of Opacities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLower lung\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e74.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e75.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e74.5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUpper lung\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e19.1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo Opacities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e17.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6.4%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFibrosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e81.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e94.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e68.1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e31.9%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSeptal Thickening\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e62.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e82.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e40.4%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e17.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e59.6%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePleural Effusion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e90.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e88.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e93.6%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6.4%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eLocation of Pleural Effusion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLeft\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e66.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e83.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e33.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBilateral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e66.7%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLymphadenopathy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e93.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e100.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e87.2%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e12.8%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLocations of Lymphadenopathy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHilar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e50.0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMediastinal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e66.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e66.7%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePredominant Location of Lymphadenopathy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHilar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e33.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMediastinal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e66.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e66.7%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eSeverity of Pneumonia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo pneumonia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e8.5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMild\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e42.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e36.2%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e31.9%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSevere\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e25.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e23.4%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eRadiologist Diagnosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCOVID 19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e28.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e72.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInfluenza\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e38.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e19.1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo pneumonia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e8.5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOther pneumonia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eCORADS Score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e17.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6.4%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e42.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e19.1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e68.1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLaboratory diagnosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCOVID-19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e77.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e71.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e85.1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInfluenza\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e28.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e14.9%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003cp\u003eThe radiographic pattern of pneumonia was associated with the etiology of pneumonia (COVID-19 or influenza pneumonia) p-value \u0026lt;0.001 (Table 2). The ground glass was more common in COVID-19 diagnosis whereas the reticulonodular was more common in the Influenza diagnosis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 2 Association of radiographic patterns and the type of pneumonia shows most patients with COVID-19 pneumonia had ground glass opacification compared to reticulonodular opacities in influenza pneumonia.\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"19.17562724014337%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.14695340501792%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"36.02150537634409%\"\u003e\n \u003cp\u003e\u003cstrong\u003eLaboratory diagnosis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" width=\"23.655913978494624%\"\u003e\n \u003cp\u003eP Value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"25.11737089201878%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"27.699530516431924%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"24.64788732394366%\"\u003e\n \u003cp\u003eCOVID-19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"22.535211267605632%\"\u003e\n \u003cp\u003eInfluenza\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"6\" valign=\"top\" width=\"19.17562724014337%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePredominant Pattern\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.14695340501792%\"\u003e\n \u003cp\u003eConsolidation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"18.817204301075268%\"\u003e\n \u003cp\u003e2 (2.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.204301075268816%\"\u003e\n \u003cp\u003e6 (27.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"6\" valign=\"top\" width=\"23.655913978494624%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"36.990595611285265%\"\u003e\n \u003cp\u003eGround Glass\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"32.9153605015674%\"\u003e\n \u003cp\u003e51 (66.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"30.094043887147336%\"\u003e\n \u003cp\u003e6 (27.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"36.990595611285265%\"\u003e\n \u003cp\u003eNodular\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"32.9153605015674%\"\u003e\n \u003cp\u003e2 (2.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"30.094043887147336%\"\u003e\n \u003cp\u003e1 (4.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"36.990595611285265%\"\u003e\n \u003cp\u003eReticular\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"32.9153605015674%\"\u003e\n \u003cp\u003e3 (3.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"30.094043887147336%\"\u003e\n \u003cp\u003e1 (4.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"36.990595611285265%\"\u003e\n \u003cp\u003eReticulonodular\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"32.9153605015674%\"\u003e\n \u003cp\u003e8 (10.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"30.094043887147336%\"\u003e\n \u003cp\u003e7 (31.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"36.990595611285265%\"\u003e\n \u003cp\u003eNormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"32.9153605015674%\"\u003e\n \u003cp\u003e11 (14.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"30.094043887147336%\"\u003e\n \u003cp\u003e1 (4.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe lower lobes were the predominant location of radiographic opacities for both COVID-19 (74.0%) and influenza pneumonia (77.3%). 14.3% of patients with COVID 19 had normal imaging compared to 4.5% of influenza pneumonia patients.\u003c/p\u003e\n\u003cp\u003ePatients with reticulonodular opacities with background ground glass opacification had a 91% reduction in the odds of having COVID-19 pneumonia (CI: 0.02-0.037; p value=0.001).\u003c/p\u003e\n\u003cp\u003eTable 3 Association of radiographic location and the type of pneumonia shows predominant lower lobe predilection in both COVID 19 and influenza pneumonia.\u003c/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\" cellspacing=\"0\" width=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"19.384057971014492%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"19.384057971014492%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" width=\"38.405797101449274%\"\u003e\n \u003cp\u003e\u003cstrong\u003eLaboratory diagnosis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"bottom\" width=\"22.82608695652174%\"\u003e\n \u003cp\u003eP Value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"25.11737089201878%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"25.11737089201878%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"24.413145539906104%\"\u003e\n \u003cp\u003eCOVID-19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"25.35211267605634%\"\u003e\n \u003cp\u003eInfluenza\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" width=\"19.384057971014492%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePredominant Location\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"19.384057971014492%\"\u003e\n \u003cp\u003eLower Lung\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"18.840579710144926%\"\u003e\n \u003cp\u003e57 (74.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"19.565217391304348%\"\u003e\n \u003cp\u003e17 (77.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" width=\"22.82608695652174%\"\u003e\n \u003cp\u003e0.384\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"33.542319749216304%\"\u003e\n \u003cp\u003eUpper Lung\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"32.60188087774295%\"\u003e\n \u003cp\u003e9 (11.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"33.85579937304075%\"\u003e\n \u003cp\u003e4 (18.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"33.542319749216304%\"\u003e\n \u003cp\u003eNormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"32.60188087774295%\"\u003e\n \u003cp\u003e11 (14.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"33.85579937304075%\"\u003e\n \u003cp\u003e1 (4.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eGround glass opacification had the highest sensitivity (89.5%) and negative predictive value (72.7%) in identification of COVID-19 pneumonia. Reticulonodular opacities on the other hand had the highest sensitivity (53.3%) and negative predictive value (68.2%) for identification of influenza pneumonia (Table 4).\u003c/p\u003e\n\u003cp\u003eTable 4 Diagnostic accuracy of the two predominant radiographic patterns showed high sensitivity and negative predictive value (NPV) of ground glass opacities for diagnosing COVID-19 pneumonia and moderate sensitivity and NPV of reticulonodular opacities for diagnosing influenza pneumonia.\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"22.69503546099291%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ePneumonia Type\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.099290780141843%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eOverall Pattern\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.843971631205672%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eSensitivity\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.602836879432624%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eSpecificity\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.588652482269504%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ePPV\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.170212765957446%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eNPV\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" width=\"22.69503546099291%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eCOVID-19 Vs Influenza\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.099290780141843%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eGround Glass\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.843971631205672%\"\u003e\n \u003cp\u003e89.47%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.602836879432624%\"\u003e\n \u003cp\u003e38.10%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.588652482269504%\"\u003e\n \u003cp\u003e66.23%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.170212765957446%\"\u003e\n \u003cp\u003e72.73%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"27.293577981651374%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eReticulonodular\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.788990825688074%\"\u003e\n \u003cp\u003e53.33%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.18348623853211%\"\u003e\n \u003cp\u003e17.86%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.28440366972477%\"\u003e\n \u003cp\u003e10.39%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.44954128440367%\"\u003e\n \u003cp\u003e68.18%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eRadiographic CORADS scores of 4 and 5 were associated a diagnosis of COVID-19 pneumonia with 59.8% of COVID-19 cases having a score of 4 or 5. The majority (90.9%) of Influenza pneumonia cases had a CORADS score of 2 and 3 on a radiographic assessment (table 5 and figure 1).\u003c/p\u003e\n\u003cp\u003eTable 5 Comparison of CORADS score and laboratory diagnosis of pneumonia shows higher CORADS scores (4 and 5) for COVID-19 pneumonia and lower score (2) for influenza pneumonia.\u003c/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\" cellspacing=\"0\" width=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"17.97153024911032%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"19.03914590747331%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"50%\"\u003e\n \u003cp\u003e\u003cstrong\u003eLaboratory diagnosis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" width=\"12.98932384341637%\"\u003e\n \u003cp\u003e\u003cstrong\u003eP Value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"20.69672131147541%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"21.92622950819672%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.778688524590164%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCOVID-19\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.59836065573771%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Influenza\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" width=\"18.003565062388592%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCORADS Score\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"19.073083778966133%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.72549019607843%\"\u003e\n \u003cp\u003e11(14.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.185383244206776%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 1 (4.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"5\" width=\"13.01247771836007%\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"27.64857881136951%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.896640826873384%\"\u003e\n \u003cp\u003e14(18.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"52.45478036175711%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 17 (77.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"27.64857881136951%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.896640826873384%\"\u003e\n \u003cp\u003e6 (7.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"52.45478036175711%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;3 (13.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"27.64857881136951%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.896640826873384%\"\u003e\n \u003cp\u003e8 (10.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"52.45478036175711%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;0 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"27.64857881136951%\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.896640826873384%\"\u003e\n \u003cp\u003e38(49.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"52.45478036175711%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;1 (4.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe inter-reader agreement between the two radiologists comparing the radiological impression and the final laboratory diagnosis was almost perfect with kappa score of 0.947 (p value \u0026lt;0.001).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe radiographic pattern of pneumonia predicted the viral etiology (COVID-19 or influenza pneumonia) with ground glass opacification having the highest positive predictive value for COVID-19 pneumonia compared to reticulonodular opacities for influenza pneumonia. The presence of reticulonodular opacities on imaging significantly reduces the likelihood of COVID-19 pneumonia. These findings can help differentiate the two etiologies of pneumonia which may have similar clinical presentation. Our findings are similar to the few published studies that have compared the two. A study by Liu M. et al which compared CT findings between COVID-19 pneumonia and influenza found that peripherally distributed rounded opacities and interlobular septal thickening and absence of nodules may help differentiate COVID-19 from influenza pneumonia (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Similar findings were observed by Bai et al where radiologists had high accuracy (over 80%) in differentiating COVID-19 pneumonia from other viral pneumonia on CT chest (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Both studies included chest CT findings only.\u003c/p\u003e \u003cp\u003eOur study included both chest radiographs and CT examinations and both demonstrated similar findings with the pattern of lung infiltrates predicting the etiology of pneumonia. To the best of our knowledge, this is the first study demonstrating the value of chest radiographs in differentiation of COVID-19 from influenza pneumonia. Chest x-rays are the first line of imaging of evaluation in patients with pneumonia and widely available including at most primary care centers. Chest x-rays can therefore be integrated in the initial determination of the etiology of pneumonia where either COVID-19 or influenza pneumonia are suspected. The few studies comparing the performance of chest x-rays have had conflicting results. A case control study by Kim et al comparing clinical and chest imaging findings using CT and chest x-rays found that although CT performed exceptionally in differentiating the two, chest x-rays underestimated lung involvement (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). This is because there remains overlap of radiographic findings on CT between COVID-19 pneumonia and other pneumonias (viral and non-viral) which may make differentiation based solely on imaging findings difficult and clinical picture would help narrow the differential (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe majority of patients (38.6%) in our study had mild severity of pneumonia as assessed on radiographs and CT scan. The extent or severity of COVID-19 pneumonia on imaging can be used to identify patients with severe pneumonia (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). The percentage of parenchyma involvement subjectively evaluated on imaging can therefore help categorize pneumonia severity as mild or severe and potentially guide treatment.\u003c/p\u003e \u003cp\u003eThe CORADS score has been widely used and validated as a scoring system for the probability of COVID-19 pneumonia on CT chest imaging with pooled frequency of COVID-19 in CORADS 4 and 5 at 61.9% and 89.7% respectively in a meta-analysis (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). The combined frequency of COVID-19 CORADS scores 4 and 5 in our study was 59.8% while majority of patients with influenza pneumonia had a CORADS score of 3 (indeterminate). This underscores the significant overlap of radiographic findings of COVID 19 pneumonia and other pneumonias. The relatively lower performance is likely attributable to the use of Chest x-rays in approximately half of our patients which perform less accurately than CT scans (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThere was excellent inter-reader agreement between the two radiologists, which is better than the moderate agreement observed in the study by Bai et al (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Their study used CT scan only in the evaluation while our study had both CT and chest x-rays. The excellent inter-rater agreement in our study may be secondary to response bias as the two readers were expected to provide an impression of either COVID-19 pneumonia or Influenza pneumonia and may not mirror actual clinical practice.\u003c/p\u003e \u003cp\u003eThe other limitation of this study was selection bias due to the retrospective nature of the study. However, the radiographic patterns of pneumonia that were the main study variables in this study are less subject to the bias.\u003c/p\u003e "},{"header":"Conclusion","content":"\u003cdiv id=\"Sec6\" type=\"Conclusion\" class=\"Section2\"\u003e \u003cp\u003eIn conclusion, ground glass opacification on chest x-rays and CT chest imaging can help differentiate COVID-19 pneumonia from influenza pneumonia while the presence of reticulonodular opacities is a strong predictor of influenza pneumonia. These findings can help differentiate the two etiologies of pneumonia which may have similar clinical presentation.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003eCompeting interests: None\u003c/p\u003e\n\u003cp\u003eFunding: No funding was available for this study\u003c/p\u003e\n\u003cp\u003eAuthors Contribution: SNG, EN and RA contributed in conception and design of the study. SNG, EN and PK contributed in data collection. JS performed data processing and analysis. The first draft was written by SNG. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003eAcknowledgement: Evelyne Khamali who assisted with data entry.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eCoronavirus disease (COVID-19) \u0026ndash; World Health Organization [Internet]. [cited 2022 Feb 25]. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.who.int/emergencies/diseases/novel-coronavirus-2019\u003c/span\u003e\u003cspan address=\"https://www.who.int/emergencies/diseases/novel-coronavirus-2019\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLaboratory testing for 2019 novel coronavirus (2019-nCoV) in suspected human cases [Internet]. [cited 2022 Feb 25]. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.who.int/publications-detail-redirect/10665-331501\u003c/span\u003e\u003cspan address=\"https://www.who.int/publications-detail-redirect/10665-331501\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi Y, Xia L. Coronavirus Disease 2019 (COVID-19): Role of Chest CT in Diagnosis and Management. AJR Am J Roentgenol. 2020 Jun;214(6):1280\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAi T, Yang Z, Hou H, Zhan C, Chen C, Lv W, et al. Correlation of Chest CT and RT-PCR Testing for Coronavirus Disease 2019 (COVID-19) in China: A Report of 1014 Cases. Radiology. 2020 Aug;296(2):E32\u0026ndash;40.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKanne JP, Little BP, Chung JH, Elicker BM, Ketai LH. Essentials for Radiologists on COVID-19: An Update-Radiology Scientific Expert Panel. Radiology. 2020 Aug;296(2):E113\u0026ndash;4.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKoo HJ, Lim S, Choe J, Choi SH, Sung H, Do KH. Radiographic and CT Features of Viral Pneumonia. Radiographics. 2018 Jun;38(3):719\u0026ndash;39.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBai HX, Hsieh B, Xiong Z, Halsey K, Choi JW, Tran TML, et al. Performance of Radiologists in Differentiating COVID-19 from Non-COVID-19 Viral Pneumonia at Chest CT. Radiology. 2020 Aug;296(2):E46\u0026ndash;54.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNambafu J, Achakolong M, Mwendwa F, Bwika J, Riunga F, Gitau S, et al. A prospective observational study of community acquired pneumonia in Kenya: the role of viral pathogens. BMC Infect Dis. 2021 Jul 23;21(1):703.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu M, Zeng W, Wen Y, Zheng Y, Lv F, Xiao K. COVID-19 pneumonia: CT findings of 122 patients and differentiation from influenza pneumonia. Eur Radiol. 2020 Oct;30(10):5463\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim SH, Wi YM, Lim S, Han KT, Bae IG. Differences in Clinical Characteristics and Chest Images between Coronavirus Disease 2019 and Influenza-Associated Pneumonia. Diagnostics (Basel). 2021 Feb 8;11(2):261.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDuzgun SA, Durhan G, Demirkazik FB, Akpinar MG, Ariyurek OM. COVID-19 pneumonia: the great radiological mimicker. Insights into Imaging. 2020 Nov 23;11(1):118.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChest CT Severity Score: An Imaging Tool for Assessing Severe COVID-19 | Radiology: Cardiothoracic Imaging [Internet]. [cited 2022 Feb 25]. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubs.rsna.org/doi/full/\u003c/span\u003e\u003cspan address=\"https://pubs.rsna.org/doi/full/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1148/ryct.2020200047\u003c/span\u003e\u003cspan address=\"10.1148/ryct.2020200047\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKwee RM, Adams HJA, Kwee TC. Diagnostic Performance of CO-RADS and the RSNA Classification System in Evaluating COVID-19 at Chest CT: A Meta-Analysis. Radiology: Cardiothoracic Imaging. 2021 Feb;3(1):e200510.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBorakati A, Perera A, Johnson J, Sood T. Diagnostic accuracy of X-ray versus CT in COVID-19: a propensity-matched database study. BMJ Open. 2020 Nov 6;10(11):e042946.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"COVID-19 pneumonia, Influenza pneumonia, radiographic, ground glass, reticulonodular","lastPublishedDoi":"10.21203/rs.3.rs-1649535/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1649535/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe novel coronavirus (COVID-19) first discovered in December 2019 has infected over 400\u0026nbsp;million people worldwide with over 5\u0026nbsp;million deaths as of February 2022. Differentiation of the imaging appearance of COVID-19 pneumonia and the endemic seasonal influenza pneumonia is clinically important as it may help give direction on patient care. The aim of this study was to evaluate for differences in radiographic appearances of COVID-19 pneumonia and influenza pneumonia.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis was a cross sectional descriptive study comparing patterns of pneumonia on radiographs and CT examinations of patients diagnosed with COVID-19 pneumonia and influenza pneumonia. The comparisons included the predominant radiographic pattern of pneumonia (alveolar, interstitial, ground glass or nodular) and the extent of pneumonia (lobar, multi-lobar or diffuse involvement). The radiographic severity of disease was classified as mild, moderate or severe based on extent of lung involvement.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 99 examinations were reviewed, 52 chest x-rays and 47 CT chest examinations. Ground glass opacification had the highest sensitivity (89.5%) and negative predictive value (72.7%) in identification of COVID-19 pneumonia while reticulonodular opacities had the highest sensitivity (53.3%) and negative predictive value (68.2%) for identification of influenza pneumonia. The majority (90.9%) of Influenza pneumonia cases had lower CORADS scores of 2 and 3.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe predominant radiographic pattern in COVID-19 pneumonia was ground glass opacities while that for influenza pneumonia was reticulonodular opacities. This may help differentiate between the two pneumonias which can have similar clinical presentation but often variable severity and outcome.\u003c/p\u003e","manuscriptTitle":"Comparison of Radiographic Appearances of Covid-19 Pneumonia and Influenza Pneumonia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-06-17 22:26:18","doi":"10.21203/rs.3.rs-1649535/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f50224bf-b90c-463a-876b-6829dc6c44c3","owner":[],"postedDate":"June 17th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2022-07-19T09:29:12+00:00","versionOfRecord":[],"versionCreatedAt":"2022-06-17 22:26:18","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-1649535","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1649535","identity":"rs-1649535","version":["v1"]},"buildId":"cBFmMYwuxLRRLfASyISRj","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}