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Chen X, Long Z, Lei Y, Liang S, Sima Y, Lin R, Ding Y, Lin Q, Ma T, Deng Y. CT Differentiation and Prognostic Modeling in COVID-19 and Influenza A Pneumonia. Acad Radiol 2025:S1076-6332(25)00106-0. [PMID: 40037939 DOI: 10.1016/j.acra.2025.02.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2024] [Revised: 01/26/2025] [Accepted: 02/03/2025] [Indexed: 03/06/2025]
Abstract
RATIONALE AND OBJECTIVES This study aimed to compare CT features of COVID-19 and Influenza A pneumonia, develop a diagnostic differential model, and explore a prognostic model for lesion resolution. MATERIALS AND METHODS A total of 446 patients diagnosed with COVID-19 and 80 with Influenza A pneumonitis underwent baseline chest CT evaluation. Logistic regression analysis was conducted after multivariate analysis and the results were presented as nomograms. Machine learning models were also evaluated for their diagnostic performance. Prognostic factors for lesion resolution were analyzed using Cox regression after excluding patients who were lost to follow-up, with a nomogram being created. RESULTS COVID-19 patients showed more features such as thickening of bronchovascular bundles, crazy paving sign and traction bronchiectasis. Influenza A patients exhibited more features such as consolidation, coarse banding and pleural effusion (P < 0.05). The logistic regression model achieved AUC values of 0.937 (training) and 0.931 (validation). Machine learning models exhibited area under the curve values ranging from 0.8486 to 0.9017. COVID-19 patients showed better lesion resolution. Independent prognostic factors for resolution at baseline included age, sex, lesion distribution, morphology, coarse banding, and widening of the main pulmonary artery. CONCLUSION Distinct imaging features can differentiate COVID-19 from Influenza A pneumonia. The logistic discriminative model and each machine - learning network model constructed in this study demonstrated efficacy. The nomogram for the logistic discriminative model exhibited high utility. Patients with COVID-19 may exhibit a better resolution of lesions. Certain baseline characteristics may act as independent prognostic factors for complete resolution of lesions.
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Affiliation(s)
- Xilai Chen
- Department of Radiology, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Zhenchu Long
- Department of Radiology, Fuyong People's Hospital, Shenzhen, China
| | - Yongxia Lei
- Department of Radiology, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Shaohua Liang
- Department of Radiology, the Second Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Yizou Sima
- Department of Radiology, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Ran Lin
- Department of Radiology, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Yajun Ding
- Department of Radiology, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Qiuxi Lin
- Department of Radiology, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Ting Ma
- Department of Radiology, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Yu Deng
- Department of Radiology, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
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Yang N, Ou Z, Sun Q, Pan J, Wu J, Xue C. Chlamydia psittaci pneumonia - evolutionary aspects on chest CT. BMC Infect Dis 2025; 25:11. [PMID: 39748281 PMCID: PMC11697637 DOI: 10.1186/s12879-024-10374-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2024] [Accepted: 12/18/2024] [Indexed: 01/04/2025] Open
Abstract
PURPOSE To present the different findings of Chlamydia psittaci (C. psittaci) pneumonia on computed tomography (CT) according to the progression of the disease, to improve diagnostic accuracy, guide early clinical diagnosis, evaluate treatment efficacy, and reduce the mortality associated with the disease. METHODS In total, 80 cases of C. psittaci pneumonia diagnosed through next-generation sequencing from January 2019 to December 2023 in multiple hospitals in China were collected according to the inclusion criteria and analyzed. The study discussed important CT findings and their dynamic changes. RESULTS The most common manifestations of C. psittaci pneumonia are lobar pneumonia and spherical pneumonia types with interstitial changes. The most common signs are the intralobular lines, air bronchogram sign, and reverse halo sign. In addition, necrosis, cavitation, and the tree-in-bud sign are rare but often associated with pleural effusion and splenomegaly. In the ultra-early stage, vascular inflammation changes were observed on imaging, often manifesting as ground-glass opacities around small core vessels or thickening of pulmonary hilar vessels. In the early stage, secondary lobules showed high-density shadows, which rapidly fused into large areas in the progressive stage, easily forming lobar pneumonia. The repair and absorption period tended to show the formation of the reverse halo sign centrally, and the dissipation period might have led to the formation of fibrous bands. CONCLUSION Combining clinical manifestations, laboratory tests, contact history, and imaging findings contribute to the diagnosis of C. psittaci pneumonia.
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Affiliation(s)
- Na Yang
- Department of Radiology, Chengdu Fifth People's Hospital, Chengdu, 611130, China
| | - Zhengqiu Ou
- Department of Radiology, People's Hospital of Ningxiang, Changsha, 410600, China
| | - Qian Sun
- Department of Medical Image Center, Qihe County People's Hospital, Dezhou, 251100, China
| | - Junping Pan
- Department of Imaging, Centre for Tuberculosis Control of Guangdong Province, Guangzhou, 510630, China
| | - Jing Wu
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, No. 68, Changle Road, Nanjing, 210008, China.
| | - Chen Xue
- Department of Radiology, The Affiliated Brain Hospital of Nanjing Medical University, No.264, Guangzhou Road, Gulou District, Nanjing, 210029, Jiangsu, China.
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Hibi A, Cusimano MD, Bilbily A, Krishnan RG, Tyrrell PN. Automated screening of computed tomography using weakly supervised anomaly detection. Int J Comput Assist Radiol Surg 2023; 18:2001-2012. [PMID: 37247113 PMCID: PMC10226438 DOI: 10.1007/s11548-023-02965-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Accepted: 05/16/2023] [Indexed: 05/30/2023]
Abstract
BACKGROUND Current artificial intelligence studies for supporting CT screening tasks depend on either supervised learning or detecting anomalies. However, the former involves a heavy annotation workload owing to requiring many slice-wise annotations (ground truth labels); the latter is promising, but while it reduces the annotation workload, it often suffers from lower performance. This study presents a novel weakly supervised anomaly detection (WSAD) algorithm trained based on scan-wise normal and anomalous annotations to provide better performance than conventional methods while reducing annotation workload. METHODS Based on surveillance video anomaly detection methodology, feature vectors representing each CT slice were trained on an AR-Net-based convolutional network using a dynamic multiple-instance learning loss and a center loss function. The following two publicly available CT datasets were retrospectively analyzed: the RSNA brain hemorrhage dataset (normal scans: 12,862; scans with intracranial hematoma: 8882) and COVID-CT set (normal scans: 282; scans with COVID-19: 95). RESULTS Anomaly scores of each slice were successfully predicted despite inaccessibility to any slice-wise annotations. Slice-level area under the curve (AUC), sensitivity, specificity, and accuracy from the brain CT dataset were 0.89, 0.85, 0.78, and 0.79, respectively. The proposed method reduced the number of annotations in the brain dataset by 97.1% compared to an ordinary slice-level supervised learning method. CONCLUSION This study demonstrated a significant annotation reduction in identifying anomalous CT slices compared to a supervised learning approach. The effectiveness of the proposed WSAD algorithm was verified through higher AUC than existing anomaly detection techniques.
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Affiliation(s)
- Atsuhiro Hibi
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada
- Department of Medical Imaging, University of Toronto, 263 McCaul Street, 4th Floor rm 409, Toronto, ON, M5T 1W7, Canada
| | - Michael D Cusimano
- Division of Neurosurgery, St Michael's Hospital, University of Toronto, Toronto, ON, Canada
| | - Alexander Bilbily
- Department of Medical Imaging, University of Toronto, 263 McCaul Street, 4th Floor rm 409, Toronto, ON, M5T 1W7, Canada
- Sunnybrook Health Sciences Centre, Toronto, ON, Canada
| | - Rahul G Krishnan
- Department of Computer Science, University of Toronto, Toronto, ON, Canada
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada
| | - Pascal N Tyrrell
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada.
- Department of Medical Imaging, University of Toronto, 263 McCaul Street, 4th Floor rm 409, Toronto, ON, M5T 1W7, Canada.
- Department of Statistical Sciences, University of Toronto, Toronto, ON, Canada.
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Johnston J, Dorrian D, Linden D, Stanel SC, Rivera-Ortega P, Chaudhuri N. Pulmonary Sequelae of COVID-19: Focus on Interstitial Lung Disease. Cells 2023; 12:2238. [PMID: 37759460 PMCID: PMC10527752 DOI: 10.3390/cells12182238] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Revised: 08/28/2023] [Accepted: 09/06/2023] [Indexed: 09/29/2023] Open
Abstract
As the world transitions from the acute phase of the COVID-19 pandemic, a novel concern has arisen-interstitial lung disease (ILD) as a consequence of SARS-CoV-2 infection. This review discusses what we have learned about its epidemiology, radiological, and pulmonary function findings, risk factors, and possible management strategies. Notably, the prevailing radiological pattern observed is organising pneumonia, with ground-glass opacities and reticulation frequently reported. Longitudinal studies reveal a complex trajectory, with some demonstrating improvement in lung function and radiographic abnormalities over time, whereas others show more static fibrotic changes. Age, disease severity, and male sex are emerging as risk factors for residual lung abnormalities. The intricate relationship between post-COVID ILD and idiopathic pulmonary fibrosis (IPF) genetics underscores the need for further research and elucidation of shared pathways. As this new disease entity unfolds, continued research is vital to guide clinical decision making and improve outcomes for patients with post-COVID ILD.
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Affiliation(s)
- Janet Johnston
- Interstitial Lung Diseases Unit, North West Lung Centre, Wythenshawe Hospital, Manchester University NHS Foundation Trust, Manchester M23 9LT, UK (P.R.-O.)
| | - Delia Dorrian
- Wellcome-Wolfson Institute for Experimental Medicine, School of Medicine, Dentistry and Biomedical Sciences, Queen’s University Belfast, Belfast BT9 7BL, UK
| | - Dermot Linden
- Wellcome-Wolfson Institute for Experimental Medicine, School of Medicine, Dentistry and Biomedical Sciences, Queen’s University Belfast, Belfast BT9 7BL, UK
- Mater Hospital, Belfast Health and Social Care Trust, Belfast BT14 6AB, UK
| | - Stefan Cristian Stanel
- Interstitial Lung Diseases Unit, North West Lung Centre, Wythenshawe Hospital, Manchester University NHS Foundation Trust, Manchester M23 9LT, UK (P.R.-O.)
- Faculty of Biology, Medicine and Health, University of Manchester, Manchester M13 9PL, UK
| | - Pilar Rivera-Ortega
- Interstitial Lung Diseases Unit, North West Lung Centre, Wythenshawe Hospital, Manchester University NHS Foundation Trust, Manchester M23 9LT, UK (P.R.-O.)
| | - Nazia Chaudhuri
- School of Medicine, Magee Campus, University of Ulster, Northlands Road, Londonderry BT48 7JL, UK;
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Duan L, Zhang L, Lu G, Guo L, Duan S, Zhou C. A CT-Based Radiomics Model for Prediction of Prognosis in Patients with Novel Coronavirus Disease (COVID-19) Pneumonia: A Preliminary Study. Diagnostics (Basel) 2023; 13:1479. [PMID: 37189580 PMCID: PMC10137710 DOI: 10.3390/diagnostics13081479] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2023] [Revised: 03/16/2023] [Accepted: 03/28/2023] [Indexed: 05/17/2023] Open
Abstract
This study aimed to develop a computed tomography (CT)-based radiomics model to predict the outcome of COVID-19 pneumonia. In total of 44 patients with confirmed diagnosis of COVID-19 were retrospectively enrolled in this study. The radiomics model and subtracted radiomics model were developed to assess the prognosis of COVID-19 and compare differences between the aggravate and relief groups. Each radiomic signature consisted of 10 selected features and showed good performance in differentiating between the aggravate and relief groups. The sensitivity, specificity, and accuracy of the first model were 98.1%, 97.3%, and 97.6%, respectively (AUC = 0.99). The sensitivity, specificity, and accuracy of the second model were 100%, 97.3%, and 98.4%, respectively (AUC = 1.00). There was no significant difference between the models. The radiomics models revealed good performance for predicting the outcome of COVID-19 in the early stage. The CT-based radiomic signature can provide valuable information to identify potential severe COVID-19 patients and aid clinical decisions.
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Affiliation(s)
- Lizhen Duan
- Department of Medical Imaging, The Affiliated Huai’an No.1 People’s Hospital of Nanjing Medical University, Huai’an 223300, China
| | - Longjiang Zhang
- Department of Medical Imaging, Jinling Hospital, School of Medicine, Nanjing University, Nanjing 210002, China
| | - Guangming Lu
- Department of Medical Imaging, Jinling Hospital, School of Medicine, Nanjing University, Nanjing 210002, China
| | - Lili Guo
- Department of Medical Imaging, The Affiliated Huai’an No.1 People’s Hospital of Nanjing Medical University, Huai’an 223300, China
- Department of Medical Imaging, Jinling Hospital, School of Medicine, Nanjing University, Nanjing 210002, China
| | | | - Changsheng Zhou
- Department of Medical Imaging, Jinling Hospital, School of Medicine, Nanjing University, Nanjing 210002, China
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Lee JH, Koh J, Jeon YK, Goo JM, Yoon SH. An Integrated Radiologic-Pathologic Understanding of COVID-19 Pneumonia. Radiology 2023; 306:e222600. [PMID: 36648343 PMCID: PMC9868683 DOI: 10.1148/radiol.222600] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Revised: 12/07/2022] [Accepted: 12/09/2022] [Indexed: 01/18/2023]
Abstract
This article reviews the radiologic and pathologic findings of the epithelial and endothelial injuries in COVID-19 pneumonia to help radiologists understand the fundamental nature of the disease. The radiologic and pathologic manifestations of COVID-19 pneumonia result from epithelial and endothelial injuries based on viral toxicity and immunopathologic effects. The pathologic features of mild and reversible COVID-19 pneumonia involve nonspecific pneumonia or an organizing pneumonia pattern, while the pathologic features of potentially fatal and irreversible COVID-19 pneumonia are characterized by diffuse alveolar damage followed by fibrosis or acute fibrinous organizing pneumonia. These pathologic responses of epithelial injuries observed in COVID-19 pneumonia are not specific to SARS-CoV-2 but rather constitute universal responses to viral pneumonia. Endothelial injury in COVID-19 pneumonia is a prominent feature compared with other types of viral pneumonia and encompasses various vascular abnormalities at different levels, including pulmonary thromboembolism, vascular engorgement, peripheral vascular reduction, a vascular tree-in-bud pattern, and lung perfusion abnormality. Chest CT with different imaging techniques (eg, CT quantification, dual-energy CT perfusion) can fully capture the various manifestations of epithelial and endothelial injuries. CT can thus aid in establishing prognosis and identifying patients at risk for deterioration.
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Affiliation(s)
- Jong Hyuk Lee
- From the Departments of Radiology (J.H.L., J.M.G., S.H.Y.) and
Pathology (J.K., Y.K.J.), Seoul National University Hospital, Seoul National
University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul 03080, Korea;
Department of Radiology, Seoul National University College of Medicine, Seoul,
Korea (J.M.G.); Institute of Radiation Medicine, Seoul National University
Medical Research Center, Seoul, Korea (J.M.G.); and Cancer Research Institute,
Seoul National University, Seoul, Korea (J.M.G.)
| | - Jaemoon Koh
- From the Departments of Radiology (J.H.L., J.M.G., S.H.Y.) and
Pathology (J.K., Y.K.J.), Seoul National University Hospital, Seoul National
University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul 03080, Korea;
Department of Radiology, Seoul National University College of Medicine, Seoul,
Korea (J.M.G.); Institute of Radiation Medicine, Seoul National University
Medical Research Center, Seoul, Korea (J.M.G.); and Cancer Research Institute,
Seoul National University, Seoul, Korea (J.M.G.)
| | - Yoon Kyung Jeon
- From the Departments of Radiology (J.H.L., J.M.G., S.H.Y.) and
Pathology (J.K., Y.K.J.), Seoul National University Hospital, Seoul National
University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul 03080, Korea;
Department of Radiology, Seoul National University College of Medicine, Seoul,
Korea (J.M.G.); Institute of Radiation Medicine, Seoul National University
Medical Research Center, Seoul, Korea (J.M.G.); and Cancer Research Institute,
Seoul National University, Seoul, Korea (J.M.G.)
| | - Jin Mo Goo
- From the Departments of Radiology (J.H.L., J.M.G., S.H.Y.) and
Pathology (J.K., Y.K.J.), Seoul National University Hospital, Seoul National
University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul 03080, Korea;
Department of Radiology, Seoul National University College of Medicine, Seoul,
Korea (J.M.G.); Institute of Radiation Medicine, Seoul National University
Medical Research Center, Seoul, Korea (J.M.G.); and Cancer Research Institute,
Seoul National University, Seoul, Korea (J.M.G.)
| | - Soon Ho Yoon
- From the Departments of Radiology (J.H.L., J.M.G., S.H.Y.) and
Pathology (J.K., Y.K.J.), Seoul National University Hospital, Seoul National
University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul 03080, Korea;
Department of Radiology, Seoul National University College of Medicine, Seoul,
Korea (J.M.G.); Institute of Radiation Medicine, Seoul National University
Medical Research Center, Seoul, Korea (J.M.G.); and Cancer Research Institute,
Seoul National University, Seoul, Korea (J.M.G.)
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Li B, Jiang L, Lin D, Dong J. Registered Clinical Trials for Artificial Intelligence in Lung Disease: A Scoping Review on ClinicalTrials.gov. Diagnostics (Basel) 2022; 12:3046. [PMID: 36553052 PMCID: PMC9777443 DOI: 10.3390/diagnostics12123046] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Revised: 11/06/2022] [Accepted: 11/14/2022] [Indexed: 12/12/2022] Open
Abstract
Clinical trials are the most effective tools to evaluate the advantages of various diagnostic and treatment modalities. AI used in medical issues, including screening, diagnosis, and treatment decisions, improves health outcomes and patient experiences. This study's objective was to investigate the traits of registered trials on artificial intelligence for lung disease. Clinical studies on AI for lung disease that were present in the ClinicalTrials.gov database were searched, and fifty-three registered trials were included. Forty-six (72.1%) were observational trials, compared to seven (27.9%) that were interventional trials. Only eight trials (15.4%) were completed. Thirty (56.6%) trials were accepting applicants. Clinical studies often included a large number of cases; for example, 24 (32.0%) trials included samples of 100-1000 cases, while 14 (17.5%) trials included samples of 1000-2000 cases. Of the interventional trials, twenty (15.7%) were retrospective studies and twenty (65.7%) were prospective studies.
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Affiliation(s)
- Bingjie Li
- Department of Thoracic Oncology and State Key Laboratory of Biotherapy, Cancer Center, West China Hospital, Sichuan University, Chengdu 610017, China
| | - Lisha Jiang
- Department of Nuclear Medicine, West China Hospital, Sichuan University, Chengdu 610017, China
| | - Dan Lin
- Lung Cancer Center, West China Hospital, Sichuan University, Chengdu 610017, China
| | - Jingsi Dong
- Lung Cancer Center, West China Hospital, Sichuan University, Chengdu 610017, China
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Hefeda MM, Elsharawy DE, Dawoud TM. Correlation between the initial CT chest findings and short-term prognosis in Egyptian patients with COVID-19 pneumonia. EGYPTIAN JOURNAL OF RADIOLOGY AND NUCLEAR MEDICINE 2022. [PMCID: PMC8727045 DOI: 10.1186/s43055-021-00685-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
Background The recent pandemic of COVID‐19 has thrown the world into chaos due to its high rate of transmissions. This study aimed to highlight the encountered CT findings in 910 patients with COVID-19 pneumonia in Egypt including the mean severity score and also correlation between the initial CT finding and the short-term prognosis in 320 patients. Results All patients had confirmed COVID-19 infection. Non-contrast CT chest was performed for all cases; in addition, the correlation between each CT finding and disease severity or the short-term prognosis was reported. The mean age was higher for patients with unfavorable prognosis (P < 0.01). The patchy pattern was the most common, found in 532/910 patients (58.4%), the nodular pattern was the least common 123/910 (13.5%). The diffuse pattern was reported in 124 (13.6%). The ground glass density was the most common reported density in the study 512/910 (56.2%). The crazy pavement sign was reported more frequently in patients required hospitalization or ICU and was reported in 53 (56.9%) of patients required hospitalization and in 29 (40.2%) patients needed ICU, and it was reported in 11 (39.2%) deceased patients. Air bronchogram was reported more frequently in patients with poor prognosis than patients with good prognosis (16/100; 26% Vs 12/220; 5.4%). The mean CT severity score for patients with poor prognosis was 15.2. The mean CT severity score for patients with good prognosis 8.7., with statistically significant difference (P = 0.001).
Conclusion Our results confirm the important role of the initial CT findings in the prediction of clinical outcome and short-term prognosis. Some signs like subpleural lines, halo sign, reversed halo sign and nodular shape of the lesions predict mild disease and favorable prognosis. The crazy paving sign, dense vessel sign, consolidation, diffuse shape and high severity score predict more severe disease and probably warrant early hospitalization. The high severity score is most important in prediction of unfavorable prognosis. The nodular shape of the lesions is the most important predictor of good prognosis.
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Teo K, Chen D, Hsu J, Lai Y, Chang C, Hsueh P, Lan J, Hsu J. Screening and characterization of myositis-related autoantibodies in COVID-19 patients. Clin Transl Sci 2022; 16:140-150. [PMID: 36271647 PMCID: PMC9841303 DOI: 10.1111/cts.13434] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Revised: 08/06/2022] [Accepted: 09/27/2022] [Indexed: 02/06/2023] Open
Abstract
An efficient host immune response against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2, COVID-19) appears to be crucial for controlling and resolving this viral infection. However, many studies have reported autoimmune characteristics in severe COVID-19 patients. Moreover, clinical observations have revealed that COVID-19-associated acute distress respiratory syndrome shares many features in common with inflammatory myopathy including interstitial lung disease (ILD), most particularly rapidly progressive (RP)-ILD. This study explored this phenomenon by seeking to identify and characterize myositis-specific and related autoantibodies in 25 COVID-19 patients with mild or severe symptoms. Line blot analysis with the EUROLINE Myopathies Ag kit identified 9 (36%) patients with COVID-19 with one or more autoantibodies against several myositis-related antigens (Jo-1, Ku, Mi-2β, PL-7, PL-12, PM-Scl 75, PM-Scl 100, Ro-52, and SRP); no anti-MDA5 antibodies were detected. As the presence of antibodies identified by line blots was unrelated to disease severity, we further characterized the autoantibodies by radioimmunoassay, in which [35 S]methionine-labeled K562 cellular antigens were precipitated and visualized by gel electrophoresis. This result was confirmed by an immunoprecipitation assay and immunoblotting; 2 patients exhibited anti-Ku70 and anti-Ku80 antibodies. Our data suggest that it is necessary to use more than one method to characterize and evaluate autoantibodies in people recovered from COVID-19, in order to avoid misinterpreting those autoantibodies as diagnostic markers for autoimmune diseases.
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Affiliation(s)
- Kai‐Fa Teo
- Graduate Institute of Biomedical SciencesChina Medical UniversityTaichungTaiwan
| | - Der‐Yuan Chen
- School of MedicineChina Medical UniversityTaichungTaiwan,Translational Medicine LaboratoryChina Medical University HospitalTaichungTaiwan,Rheumatology and Immunology CenterChina Medical University HospitalTaichungTaiwan
| | - Jeh‐Ting Hsu
- Department of Information ManagementHsing Wu UniversityNew TaipeiTaiwan
| | - Yi‐Hua Lai
- School of MedicineChina Medical UniversityTaichungTaiwan,Rheumatology and Immunology CenterChina Medical University HospitalTaichungTaiwan,Rheumatic Diseases Research CenterChina Medical University HospitalTaichungTaiwan
| | - Ching‐Kun Chang
- Translational Medicine LaboratoryChina Medical University HospitalTaichungTaiwan,Rheumatology and Immunology CenterChina Medical University HospitalTaichungTaiwan
| | - Po‐Ren Hsueh
- School of MedicineChina Medical UniversityTaichungTaiwan,Departments of Laboratory Medicine and Internal MedicineChina Medical University HospitalTaichungTaiwan,Ph.D. Program for Aging, School of MedicineChina Medical UniversityTaichungTaiwan,Departments of Laboratory Medicine and Internal MedicineNational Taiwan University Hospital, National Taiwan University College of MedicineTaipeiTaiwan
| | - Joung‐Liang Lan
- School of MedicineChina Medical UniversityTaichungTaiwan,Rheumatology and Immunology CenterChina Medical University HospitalTaichungTaiwan,Rheumatic Diseases Research CenterChina Medical University HospitalTaichungTaiwan
| | - Jye‐Lin Hsu
- Graduate Institute of Biomedical SciencesChina Medical UniversityTaichungTaiwan,Drug Development CenterChina Medical UniversityTaichungTaiwan
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Oscullo G, Gómez-Olivas JD, Beauperthuy T, Bekki A, Garcia-Ortega A, Matera MG, Cazzola M, Martinez-Garcia MA. Bronchiectasis and COVID-19 infection: a two-way street. Chin Med J (Engl) 2022; 135:2398-2404. [PMID: 36476558 PMCID: PMC9945180 DOI: 10.1097/cm9.0000000000002447] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Indexed: 12/13/2022] Open
Abstract
ABSTRACT Bronchiectasis (BE) has been linked to past viral infections such as influenza, measles, or adenovirus. Two years ago, a new pandemic viral infection severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) broke out and it still persists today, and a significant proportion of surviving patients have radiological and clinical sequelae, including BE. Our aim was to thoroughly review the information available in the literature on the bidirectional relationship between SARS-CoV-2 infection and the development of BE, as well as the impact of this infection on patients already suffering from BE. Available information indicates that only a small percentage of patients in the acute phase of coronavirus disease 2019 (COVID-19) pneumonia develop BE, although the latter is recognized as one of the radiological sequelae of COVID-19 pneumonia, especially when it is caused by traction. The severity of the initial pneumonia is the main risk factor for the development of future BE, but during the COVID-19 pandemic, exacerbations in BE patients were reduced by approximately 50%. Finally, the impact of BE on the prognosis of patients with COVID-19 pneumonia is not yet known.
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Affiliation(s)
- Grace Oscullo
- Department of Pneumology, Hospital Universitario y Politécnico la Fe de Valencia, Valencia 46012, Spain
| | - Jose Daniel Gómez-Olivas
- Department of Pneumology, Hospital Universitario y Politécnico la Fe de Valencia, Valencia 46012, Spain
| | - Thais Beauperthuy
- Department of Pneumology, Hospital Universitario y Politécnico la Fe de Valencia, Valencia 46012, Spain
| | - Amina Bekki
- Department of Pneumology, Hospital Universitario y Politécnico la Fe de Valencia, Valencia 46012, Spain
| | - Alberto Garcia-Ortega
- Department of Pneumology, Hospital Universitario y Politécnico la Fe de Valencia, Valencia 46012, Spain
| | - Maria Gabriella Matera
- Department of Experimental Medicine, University of Campania "Luigi Vanvitelli", Naples 80121, Italy
| | - Mario Cazzola
- Department of Experimental Medicine, University of Rome Tor Vergata, Rome 00185, Italy
| | - Miguel Angel Martinez-Garcia
- Department of Pneumology, Hospital Universitario y Politécnico la Fe de Valencia, Valencia 46012, Spain
- CIBERES de enfermedades respiratorias, Instituto de Salud Carlos III, Madrid 41263, Spain
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Suri JS, Agarwal S, Saba L, Chabert GL, Carriero A, Paschè A, Danna P, Mehmedović A, Faa G, Jujaray T, Singh IM, Khanna NN, Laird JR, Sfikakis PP, Agarwal V, Teji JS, R Yadav R, Nagy F, Kincses ZT, Ruzsa Z, Viskovic K, Kalra MK. Multicenter Study on COVID-19 Lung Computed Tomography Segmentation with varying Glass Ground Opacities using Unseen Deep Learning Artificial Intelligence Paradigms: COVLIAS 1.0 Validation. J Med Syst 2022; 46:62. [PMID: 35988110 PMCID: PMC9392994 DOI: 10.1007/s10916-022-01850-y] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Accepted: 08/02/2022] [Indexed: 11/09/2022]
Abstract
Variations in COVID-19 lesions such as glass ground opacities (GGO), consolidations, and crazy paving can compromise the ability of solo-deep learning (SDL) or hybrid-deep learning (HDL) artificial intelligence (AI) models in predicting automated COVID-19 lung segmentation in Computed Tomography (CT) from unseen data leading to poor clinical manifestations. As the first study of its kind, "COVLIAS 1.0-Unseen" proves two hypotheses, (i) contrast adjustment is vital for AI, and (ii) HDL is superior to SDL. In a multicenter study, 10,000 CT slices were collected from 72 Italian (ITA) patients with low-GGO, and 80 Croatian (CRO) patients with high-GGO. Hounsfield Units (HU) were automatically adjusted to train the AI models and predict from test data, leading to four combinations-two Unseen sets: (i) train-CRO:test-ITA, (ii) train-ITA:test-CRO, and two Seen sets: (iii) train-CRO:test-CRO, (iv) train-ITA:test-ITA. COVILAS used three SDL models: PSPNet, SegNet, UNet and six HDL models: VGG-PSPNet, VGG-SegNet, VGG-UNet, ResNet-PSPNet, ResNet-SegNet, and ResNet-UNet. Two trained, blinded senior radiologists conducted ground truth annotations. Five types of performance metrics were used to validate COVLIAS 1.0-Unseen which was further benchmarked against MedSeg, an open-source web-based system. After HU adjustment for DS and JI, HDL (Unseen AI) > SDL (Unseen AI) by 4% and 5%, respectively. For CC, HDL (Unseen AI) > SDL (Unseen AI) by 6%. The COVLIAS-MedSeg difference was < 5%, meeting regulatory guidelines.Unseen AI was successfully demonstrated using automated HU adjustment. HDL was found to be superior to SDL.
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Affiliation(s)
- Jasjit S Suri
- Stroke Diagnostic and Monitoring Division, AtheroPoint™, Roseville, CA, USA.
- Advanced Knowledge Engineering Centre, GBTI, Roseville, CA, USA.
| | - Sushant Agarwal
- Advanced Knowledge Engineering Centre, GBTI, Roseville, CA, USA
- Department of Computer Science Engineering, Pranveer Singh Institute of Technology, Kanpur, Uttar Pradesh, India
| | - Luca Saba
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), Cagliari, Italy
| | - Gian Luca Chabert
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), Cagliari, Italy
| | - Alessandro Carriero
- Depart of Radiology, "Maggiore Della Carità" Hospital, University of Piemonte Orientale, Via Solaroli 17, 28100, Novara, Italy
| | - Alessio Paschè
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), Cagliari, Italy
| | - Pietro Danna
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), Cagliari, Italy
| | | | - Gavino Faa
- Department of Pathology - AOU of Cagliari, Cagliari, Italy
| | - Tanay Jujaray
- Advanced Knowledge Engineering Centre, GBTI, Roseville, CA, USA
- Dept of Molecular, Cell and Developmental Biology, University of California, Santa Cruz, CA, USA
| | - Inder M Singh
- Stroke Diagnostic and Monitoring Division, AtheroPoint™, Roseville, CA, USA
| | - Narendra N Khanna
- Department of Cardiology, Indraprastha APOLLO Hospitals, New Delhi, India
| | - John R Laird
- Heart and Vascular Institute, Adventist Health St. Helena, St Helena, CA, USA
| | - Petros P Sfikakis
- Rheumatology Unit, National Kapodistrian University of Athens, Athens, Greece
| | | | - Jagjit S Teji
- Ann and Robert H. Lurie Children's Hospital of Chicago, Chicago, USA
| | | | - Ferenc Nagy
- Internal Medicine Department, University of Szeged, Szeged, 6725, Hungary
| | | | - Zoltan Ruzsa
- Invasive Cardiology Division, University of Szeged, Budapest, Hungary
| | | | - Mannudeep K Kalra
- Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA, USA
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12
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Okan S, Okan F, Duran Yücesoy F. Evaluation of pulmonary function and exercise capacity after COVID-19 pneumonia. Heart Lung 2022; 54:1-6. [PMID: 35305515 PMCID: PMC8913294 DOI: 10.1016/j.hrtlng.2022.03.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2021] [Revised: 03/07/2022] [Accepted: 03/07/2022] [Indexed: 11/20/2022]
Abstract
BACKGROUND Individuals who suffer from coronavirus disease 2019 (COVID-19) pneumonia may experience pulmonary dysfunction during the chronic period due to pulmonary parenchymal damage after acute disease. OBJECTIVES The aim of the present study was to evaluate the pulmonary function and exercise capacity of patients treated for COVID 19 pneumonia after discharge. METHODS In this cross-sectional study, 79 people who were hospitalized with COVID-19 between March and October 2020 were evaluated at least two months after discharge. A pulmonary function test and a six-minute walk test were administered to the individuals included in the study. RESULTS Restrictive-type disorder was detected in 21.5% of the individuals who were evaluated at least two months after discharge. The forced expiratory volume in the first second (FEV1) and the forced vital capacity (FVC) values of the pulmonary function tests were significantly lower in the individuals with severe/critical clinical disease compared to those with moderate disease (p = 0.004 and p = 0.001, respectively). Although the six-minute walk test (6MWT) distances were lower in the severe/critical group than in the moderate group, the difference was not statistically significant (p > 0.05). CONCLUSIONS Individuals who are discharged after hospitalization for COVID-19 pneumonia may develop a restrictive type of pulmonary dysfunction. Therefore, survivors of COVID-19 pneumonia should be evaluated for pulmonary function and rehabilitation needs and should be provided with treatment as required.
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Affiliation(s)
- S Okan
- Associate Professor, Department of Physical Therapy and Rehabilitation Tokat, Tokat State Hospital, Yeni mah, Merkez, Tokat 60100, Turkey.
| | - F Okan
- Assistant Professor, Department of Public Health Nursing Tokat, Faculty of Health Sience, Gaziosmanpasa University, Turkey
| | - F Duran Yücesoy
- Department of Pulmonary Diseases Tokat, Tokat State Hospital, Turkey
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13
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Agarwal M, Agarwal S, Saba L, Chabert GL, Gupta S, Carriero A, Pasche A, Danna P, Mehmedovic A, Faa G, Shrivastava S, Jain K, Jain H, Jujaray T, Singh IM, Turk M, Chadha PS, Johri AM, Khanna NN, Mavrogeni S, Laird JR, Sobel DW, Miner M, Balestrieri A, Sfikakis PP, Tsoulfas G, Misra DP, Agarwal V, Kitas GD, Teji JS, Al-Maini M, Dhanjil SK, Nicolaides A, Sharma A, Rathore V, Fatemi M, Alizad A, Krishnan PR, Yadav RR, Nagy F, Kincses ZT, Ruzsa Z, Naidu S, Viskovic K, Kalra MK, Suri JS. Eight pruning deep learning models for low storage and high-speed COVID-19 computed tomography lung segmentation and heatmap-based lesion localization: A multicenter study using COVLIAS 2.0. Comput Biol Med 2022; 146:105571. [PMID: 35751196 PMCID: PMC9123805 DOI: 10.1016/j.compbiomed.2022.105571] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2022] [Revised: 04/05/2022] [Accepted: 04/26/2022] [Indexed: 12/12/2022]
Abstract
BACKGROUND COVLIAS 1.0: an automated lung segmentation was designed for COVID-19 diagnosis. It has issues related to storage space and speed. This study shows that COVLIAS 2.0 uses pruned AI (PAI) networks for improving both storage and speed, wiliest high performance on lung segmentation and lesion localization. METHOD ology: The proposed study uses multicenter ∼9,000 CT slices from two different nations, namely, CroMed from Croatia (80 patients, experimental data), and NovMed from Italy (72 patients, validation data). We hypothesize that by using pruning and evolutionary optimization algorithms, the size of the AI models can be reduced significantly, ensuring optimal performance. Eight different pruning techniques (i) differential evolution (DE), (ii) genetic algorithm (GA), (iii) particle swarm optimization algorithm (PSO), and (iv) whale optimization algorithm (WO) in two deep learning frameworks (i) Fully connected network (FCN) and (ii) SegNet were designed. COVLIAS 2.0 was validated using "Unseen NovMed" and benchmarked against MedSeg. Statistical tests for stability and reliability were also conducted. RESULTS Pruning algorithms (i) FCN-DE, (ii) FCN-GA, (iii) FCN-PSO, and (iv) FCN-WO showed improvement in storage by 92.4%, 95.3%, 98.7%, and 99.8% respectively when compared against solo FCN, and (v) SegNet-DE, (vi) SegNet-GA, (vii) SegNet-PSO, and (viii) SegNet-WO showed improvement by 97.1%, 97.9%, 98.8%, and 99.2% respectively when compared against solo SegNet. AUC > 0.94 (p < 0.0001) on CroMed and > 0.86 (p < 0.0001) on NovMed data set for all eight EA model. PAI <0.25 s per image. DenseNet-121-based Grad-CAM heatmaps showed validation on glass ground opacity lesions. CONCLUSIONS Eight PAI networks that were successfully validated are five times faster, storage efficient, and could be used in clinical settings.
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Affiliation(s)
- Mohit Agarwal
- Department of Computer Science Engineering, Bennett University, India
| | - Sushant Agarwal
- Department of Computer Science Engineering, PSIT, Kanpur, India; Advanced Knowledge Engineering Centre, Global Biomedical Technologies, Inc., Roseville, CA 95661, USA
| | - Luca Saba
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), Cagliari, Italy
| | - Gian Luca Chabert
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), Cagliari, Italy
| | - Suneet Gupta
- Department of Computer Science Engineering, Bennett University, India
| | - Alessandro Carriero
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), Cagliari, Italy
| | - Alessio Pasche
- Depart of Radiology, "Maggiore della Carità" Hospital, University of Piemonte Orientale, Via Solaroli 17, 28100, Novara, Italy
| | - Pietro Danna
- Depart of Radiology, "Maggiore della Carità" Hospital, University of Piemonte Orientale, Via Solaroli 17, 28100, Novara, Italy
| | | | - Gavino Faa
- Department of Pathology - AOU of Cagliari, Italy
| | - Saurabh Shrivastava
- College of Computing Sciences and IT, Teerthanker Mahaveer University, Moradabad, 244001, India
| | - Kanishka Jain
- College of Computing Sciences and IT, Teerthanker Mahaveer University, Moradabad, 244001, India
| | - Harsh Jain
- College of Computing Sciences and IT, Teerthanker Mahaveer University, Moradabad, 244001, India
| | - Tanay Jujaray
- Dept of Molecular, Cell and Developmental Biology, University of California, Santa Cruz, CA, USA
| | | | - Monika Turk
- The Hanse-Wissenschaftskolleg Institute for Advanced Study, Delmenhorst, Germany
| | | | - Amer M Johri
- Division of Cardiology, Queen's University, Kingston, Ontario, Canada
| | - Narendra N Khanna
- Department of Cardiology, Indraprastha APOLLO Hospitals, New Delhi, India
| | - Sophie Mavrogeni
- Cardiology Clinic, Onassis Cardiac Surgery Center, Athens, Greece
| | - John R Laird
- Heart and Vascular Institute, Adventist Health St. Helena, St Helena, CA, USA
| | - David W Sobel
- Minimally Invasive Urology Institute, Brown University, Providence, RI, USA
| | - Martin Miner
- Men's Health Center, Miriam Hospital Providence, Rhode Island, USA
| | - Antonella Balestrieri
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), Cagliari, Italy
| | - Petros P Sfikakis
- Rheumatology Unit, National Kapodistrian University of Athens, Greece
| | - George Tsoulfas
- Aristoteleion University of Thessaloniki, Thessaloniki, Greece
| | | | | | - George D Kitas
- Academic Affairs, Dudley Group NHS Foundation Trust, Dudley, UK; Arthritis Research UK Epidemiology Unit, Manchester University, Manchester, UK
| | - Jagjit S Teji
- Ann and Robert H. Lurie Children's Hospital of Chicago, Chicago, USA
| | - Mustafa Al-Maini
- Allergy, Clinical Immunology and Rheumatology Institute, Toronto, Canada
| | | | - Andrew Nicolaides
- Vascular Screening and Diagnostic Centre and Univ. of Nicosia Medical School, Cyprus
| | - Aditya Sharma
- Division of Cardiovascular Medicine, University of Virginia, Charlottesville, VA, USA
| | | | - Mostafa Fatemi
- Dept. of Physiology & Biomedical Engg., Mayo Clinic College of Medicine and Science, MN, USA
| | - Azra Alizad
- Dept. of Radiology, Mayo Clinic College of Medicine and Science, MN, USA
| | | | | | - Frence Nagy
- Department of Radiology, University of Szeged, 6725, Hungary
| | | | - Zoltan Ruzsa
- Invasive Cardiology Division, University of Szeged, Budapest, Hungary
| | - Subbaram Naidu
- Electrical Engineering Department, University of Minnesota, Duluth, MN, USA
| | | | - Manudeep K Kalra
- Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
| | - Jasjit S Suri
- College of Computing Sciences and IT, Teerthanker Mahaveer University, Moradabad, 244001, India; Stroke Diagnostic and Monitoring Division, AtheroPoint™, Roseville, CA, USA.
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14
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Suri JS, Agarwal S, Chabert GL, Carriero A, Paschè A, Danna PSC, Saba L, Mehmedović A, Faa G, Singh IM, Turk M, Chadha PS, Johri AM, Khanna NN, Mavrogeni S, Laird JR, Pareek G, Miner M, Sobel DW, Balestrieri A, Sfikakis PP, Tsoulfas G, Protogerou AD, Misra DP, Agarwal V, Kitas GD, Teji JS, Al-Maini M, Dhanjil SK, Nicolaides A, Sharma A, Rathore V, Fatemi M, Alizad A, Krishnan PR, Nagy F, Ruzsa Z, Fouda MM, Naidu S, Viskovic K, Kalra MK. COVLIAS 2.0-cXAI: Cloud-Based Explainable Deep Learning System for COVID-19 Lesion Localization in Computed Tomography Scans. Diagnostics (Basel) 2022; 12:1482. [PMID: 35741292 PMCID: PMC9221733 DOI: 10.3390/diagnostics12061482] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Revised: 06/07/2022] [Accepted: 06/13/2022] [Indexed: 02/07/2023] Open
Abstract
Background: The previous COVID-19 lung diagnosis system lacks both scientific validation and the role of explainable artificial intelligence (AI) for understanding lesion localization. This study presents a cloud-based explainable AI, the “COVLIAS 2.0-cXAI” system using four kinds of class activation maps (CAM) models. Methodology: Our cohort consisted of ~6000 CT slices from two sources (Croatia, 80 COVID-19 patients and Italy, 15 control patients). COVLIAS 2.0-cXAI design consisted of three stages: (i) automated lung segmentation using hybrid deep learning ResNet-UNet model by automatic adjustment of Hounsfield units, hyperparameter optimization, and parallel and distributed training, (ii) classification using three kinds of DenseNet (DN) models (DN-121, DN-169, DN-201), and (iii) validation using four kinds of CAM visualization techniques: gradient-weighted class activation mapping (Grad-CAM), Grad-CAM++, score-weighted CAM (Score-CAM), and FasterScore-CAM. The COVLIAS 2.0-cXAI was validated by three trained senior radiologists for its stability and reliability. The Friedman test was also performed on the scores of the three radiologists. Results: The ResNet-UNet segmentation model resulted in dice similarity of 0.96, Jaccard index of 0.93, a correlation coefficient of 0.99, with a figure-of-merit of 95.99%, while the classifier accuracies for the three DN nets (DN-121, DN-169, and DN-201) were 98%, 98%, and 99% with a loss of ~0.003, ~0.0025, and ~0.002 using 50 epochs, respectively. The mean AUC for all three DN models was 0.99 (p < 0.0001). The COVLIAS 2.0-cXAI showed 80% scans for mean alignment index (MAI) between heatmaps and gold standard, a score of four out of five, establishing the system for clinical settings. Conclusions: The COVLIAS 2.0-cXAI successfully showed a cloud-based explainable AI system for lesion localization in lung CT scans.
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Affiliation(s)
- Jasjit S. Suri
- Stroke Diagnostic and Monitoring Division, AtheroPoint™, Roseville, CA 95661, USA; (I.M.S.); (P.S.C.)
- Advanced Knowledge Engineering Centre, GBTI, Roseville, CA 95661, USA;
| | - Sushant Agarwal
- Advanced Knowledge Engineering Centre, GBTI, Roseville, CA 95661, USA;
- Department of Computer Science Engineering, PSIT, Kanpur 209305, India
| | - Gian Luca Chabert
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), 09123 Cagliari, Italy; (G.L.C.); (A.P.); (P.S.C.D.); (L.S.); (A.B.)
| | - Alessandro Carriero
- Department of Radiology, “Maggiore della Carità” Hospital, University of Piemonte Orientale (UPO), Via Solaroli 17, 28100 Novara, Italy;
| | - Alessio Paschè
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), 09123 Cagliari, Italy; (G.L.C.); (A.P.); (P.S.C.D.); (L.S.); (A.B.)
| | - Pietro S. C. Danna
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), 09123 Cagliari, Italy; (G.L.C.); (A.P.); (P.S.C.D.); (L.S.); (A.B.)
| | - Luca Saba
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), 09123 Cagliari, Italy; (G.L.C.); (A.P.); (P.S.C.D.); (L.S.); (A.B.)
| | - Armin Mehmedović
- Department of Radiology, University Hospital for Infectious Diseases, 10000 Zagreb, Croatia; (A.M.); (K.V.)
| | - Gavino Faa
- Department of Pathology, Azienda Ospedaliero Universitaria (A.O.U.), 09124 Cagliari, Italy;
| | - Inder M. Singh
- Stroke Diagnostic and Monitoring Division, AtheroPoint™, Roseville, CA 95661, USA; (I.M.S.); (P.S.C.)
| | - Monika Turk
- The Hanse-Wissenschaftskolleg Institute for Advanced Study, 27753 Delmenhorst, Germany;
| | - Paramjit S. Chadha
- Stroke Diagnostic and Monitoring Division, AtheroPoint™, Roseville, CA 95661, USA; (I.M.S.); (P.S.C.)
| | - Amer M. Johri
- Department of Medicine, Division of Cardiology, Queen’s University, Kingston, ON K7L 3N6, Canada;
| | - Narendra N. Khanna
- Department of Cardiology, Indraprastha APOLLO Hospitals, New Delhi 110076, India;
| | - Sophie Mavrogeni
- Cardiology Clinic, Onassis Cardiac Surgery Center, 17674 Athens, Greece;
| | - John R. Laird
- Heart and Vascular Institute, Adventist Health St. Helena, St. Helena, CA 94574, USA;
| | - Gyan Pareek
- Minimally Invasive Urology Institute, Brown University, Providence, RI 02912, USA; (G.P.); (D.W.S.)
| | - Martin Miner
- Men’s Health Center, Miriam Hospital, Providence, RI 02912, USA;
| | - David W. Sobel
- Minimally Invasive Urology Institute, Brown University, Providence, RI 02912, USA; (G.P.); (D.W.S.)
| | - Antonella Balestrieri
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), 09123 Cagliari, Italy; (G.L.C.); (A.P.); (P.S.C.D.); (L.S.); (A.B.)
| | - Petros P. Sfikakis
- Rheumatology Unit, National Kapodistrian University of Athens, 17674 Athens, Greece;
| | - George Tsoulfas
- Department of Surgery, Aristoteleion University of Thessaloniki, 54124 Thessaloniki, Greece;
| | - Athanasios D. Protogerou
- Cardiovascular Prevention and Research Unit, Department of Pathophysiology, National & Kapodistrian University of Athens, 15772 Athens, Greece;
| | | | - Vikas Agarwal
- Department of Immunology, SGPIMS, Lucknow 226014, India; (D.P.M.); (V.A.)
| | - George D. Kitas
- Academic Affairs, Dudley Group NHS Foundation Trust, Dudley DY1 2HQ, UK;
- Arthritis Research UK Epidemiology Unit, Manchester University, Manchester M13 9PL, UK
| | - Jagjit S. Teji
- Ann and Robert H. Lurie Children’s Hospital of Chicago, Chicago, IL 60611, USA;
| | - Mustafa Al-Maini
- Allergy, Clinical Immunology and Rheumatology Institute, Toronto, ON M5G 1N8, Canada;
| | | | - Andrew Nicolaides
- Vascular Screening and Diagnostic Centre, University of Nicosia Medical School, Engomi 2408, Cyprus;
| | - Aditya Sharma
- Division of Cardiovascular Medicine, University of Virginia, Charlottesville, VA 22902, USA;
| | - Vijay Rathore
- AtheroPoint LLC., Roseville, CA 95661, USA; (S.K.D.); (V.R.)
| | - Mostafa Fatemi
- Department of Physiology & Biomedical Engineering, Mayo Clinic College of Medicine and Science, Rochester, MN 55905, USA;
| | - Azra Alizad
- Department of Radiology, Mayo Clinic College of Medicine and Science, Rochester, MN 55905, USA;
| | | | - Ferenc Nagy
- Internal Medicine Department, University of Szeged, 6725 Szeged, Hungary;
| | - Zoltan Ruzsa
- Invasive Cardiology Division, University of Szeged, 1122 Budapest, Hungary;
| | - Mostafa M. Fouda
- Department of ECE, Idaho State University, Pocatello, ID 83209, USA;
| | - Subbaram Naidu
- Electrical Engineering Department, University of Minnesota, Duluth, MN 55812, USA;
| | - Klaudija Viskovic
- Department of Radiology, University Hospital for Infectious Diseases, 10000 Zagreb, Croatia; (A.M.); (K.V.)
| | - Mannudeep K. Kalra
- Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114, USA;
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15
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EVALUATION OF COMPUTED TOMOGRAPHY AND PCR RESULTS OF PATIENTS ADMITTED TO PANDEMIC HOSPITAL IN TERMS OF COVID-19. JOURNAL OF CONTEMPORARY MEDICINE 2022. [DOI: 10.16899/jcm.1066691] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
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16
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EDNC: Ensemble Deep Neural Network for COVID-19 Recognition. Tomography 2022; 8:869-890. [PMID: 35314648 PMCID: PMC8938826 DOI: 10.3390/tomography8020071] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Revised: 03/15/2022] [Accepted: 03/16/2022] [Indexed: 12/24/2022] Open
Abstract
The automatic recognition of COVID-19 diseases is critical in the present pandemic since it relieves healthcare staff of the burden of screening for infection with COVID-19. Previous studies have proven that deep learning algorithms can be utilized to aid in the diagnosis of patients with potential COVID-19 infection. However, the accuracy of current COVID-19 recognition models is relatively low. Motivated by this fact, we propose three deep learning architectures, F-EDNC, FC-EDNC, and O-EDNC, to quickly and accurately detect COVID-19 infections from chest computed tomography (CT) images. Sixteen deep learning neural networks have been modified and trained to recognize COVID-19 patients using transfer learning and 2458 CT chest images. The proposed EDNC has then been developed using three of sixteen modified pre-trained models to improve the performance of COVID-19 recognition. The results suggested that the F-EDNC method significantly enhanced the recognition of COVID-19 infections with 97.75% accuracy, followed by FC-EDNC and O-EDNC (97.55% and 96.12%, respectively), which is superior to most of the current COVID-19 recognition models. Furthermore, a localhost web application has been built that enables users to easily upload their chest CT scans and obtain their COVID-19 results automatically. This accurate, fast, and automatic COVID-19 recognition system will relieve the stress of medical professionals for screening COVID-19 infections.
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BiebaÛ CM, Desmet JN, Dubbeldam A, Cockmartin L, Coudyzer WM, Coolen J, Verschakelen JA, De Wever W. Radiological findings in low-dose CT for COVID-19 pneumonia in 182 patients: Correlation of signs and severity with patient outcome. Medicine (Baltimore) 2022; 101:e28950. [PMID: 35244053 PMCID: PMC8896423 DOI: 10.1097/md.0000000000028950] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Accepted: 02/10/2022] [Indexed: 01/04/2023] Open
Abstract
To characterize computed tomography (CT) findings of coronavirus disease 2019 (COVID-19) pneumonia and their value in outcome prediction.Chest CTs of 182 patients with a confirmed diagnosis of COVID-19 infection by real-time reverse transcription polymerase chain reaction were evaluated for the presence of CT-abnormalities and their frequency. Regarding the patient outcome each patient was categorized in 5 progressive stages and the duration of hospitalization was determined. Regression analysis was performed to find which CT findings are predictive for patient outcome and to assess prognostic factors for the hospitalization duration.Multivariate statistical analysis confirmed a higher age (OR = 1.023, P = .025), a higher total visual severity score (OR = 1.038, P = .002) and the presence of crazy paving (OR = 2.160, P = .034) as predictive parameters for patient outcome. A higher total visual severity score (+0.134 days; P = .012) and the presence of pleural effusion (+13.985 days, P = 0.005) were predictive parameters for a longer hospitalization duration. Moreover, a higher sensitivity of chest CT (false negatives 10.4%; true positives 78.6%) in comparison to real-time reverse transcription polymerase chain reaction was obtained.An increasing percentage of lung opacity as well as the presence of crazy paving and a higher age are associated with a worse patient outcome. The presence of a higher total visual severity score and pleural effusion are significant predictors for a longer hospitalization duration. These results are underscoring the value of chest CT as a diagnostic and prognostic tool in the pandemic outbreak of COVID-19, to facilitate fast detection and to preserve the limited (intensive) care capacity only for the most vulnerable patients.
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Affiliation(s)
| | - Jeroen N. Desmet
- Department of Radiology, University Hospitals Leuven, Leuven, Belgium
| | - Adriana Dubbeldam
- Department of Radiology, University Hospitals Leuven, Leuven, Belgium
| | - Lesley Cockmartin
- Department of Radiology, University Hospitals Leuven, Leuven, Belgium
| | | | - Johan Coolen
- Department of Radiology, University Hospitals Leuven, Leuven, Belgium
| | | | - Walter De Wever
- Department of Radiology, University Hospitals Leuven, Leuven, Belgium
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18
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Poerio A, Carlicchi E, Lotrecchiano L, Praticò C, Mistè G, Scavello S, Morsiani M, Zompatori M, Ferrari R. Evolution of COVID-19 Pulmonary Fibrosis-Like Residual Changes Over Time - Longitudinal Chest CT up to 9 Months After Disease Onset: a Single-Center Case Series. SN COMPREHENSIVE CLINICAL MEDICINE 2022; 4:57. [PMID: 35194572 PMCID: PMC8852861 DOI: 10.1007/s42399-022-01140-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Accepted: 02/08/2022] [Indexed: 01/13/2023]
Abstract
The aim of the study was to evaluate the temporal evolution of fibrotic-like pulmonary interstitial abnormalities secondary to Sars-CoV-2 virus (COVID-19) pneumonia detected on chest-CTs of patients hospitalized for COVID-19 infection. We retrospectively reviewed chest-CTs obtained up to 9 months after disease onset in a group of patients with COVID-19 pneumonia and CT features suggestive of lung fibrosis at the first follow-up after hospital discharge. We observed a complete and unexpected resolution of all interstitial abnormalities, including reticulations and bronchial dilatation, in a period of about 6-9 months after discharge. Interstitial fibrotic-like changes detectable in the first months after COVID-19 pneumonia could be slowly or very slowly resolving but still completely reversible and probably secondary to an organizing pneumonia reaction.
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Affiliation(s)
- Antonio Poerio
- Radiology Unit, S. Maria Della Scaletta Hospital, via Montericco 4, 40026 Imola, BO Italy
| | - Eleonora Carlicchi
- Postgraduate School in Radiodiagnostics, Università Degli Studi Di Milano, Milan, Italy
| | - Ludovica Lotrecchiano
- Department of Radiology, IRCCS Ospedale San Raffaele Turro, via Stamira d’Ancona 20, 20127 Milan, Italy
| | - Chiara Praticò
- Emergency Care Unit, S. Maria Della Scaletta Hospital, via Montericco 4, 40026 Imola, BO Italy
| | - Giacomo Mistè
- Internal Medicine Unit, S. Maria Della Scaletta Hospital, via Montericco 4, 40026 Imola, BO Italy
| | - Saverio Scavello
- Internal Medicine Unit, S. Maria Della Scaletta Hospital, via Montericco 4, 40026 Imola, BO Italy
| | - Miria Morsiani
- Radiology Unit, S. Maria Della Scaletta Hospital, via Montericco 4, 40026 Imola, BO Italy
| | - Maurizio Zompatori
- Department of Radiology, Multimedica IRCCS, San Giuseppe Hospital, Milano, Italy
| | - Rodolfo Ferrari
- Emergency Care Unit, S. Maria Della Scaletta Hospital, via Montericco 4, 40026 Imola, BO Italy
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19
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Imaging findings in acute pediatric coronavirus disease 2019 (COVID-19) pneumonia and multisystem inflammatory syndrome in children (MIS-C). Pediatr Radiol 2022; 52:1985-1997. [PMID: 35616701 PMCID: PMC9132751 DOI: 10.1007/s00247-022-05393-9] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Revised: 04/06/2022] [Accepted: 05/03/2022] [Indexed: 12/04/2022]
Abstract
The two primary manifestations of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in children are acute coronavirus disease 2019 (COVID-19) pneumonia and multisystem inflammatory syndrome (MIS-C). While most pediatric cases of acute COVID-19 disease are mild or asymptomatic, some children are at risk for developing severe pneumonia. In MIS-C, children present a few weeks after SARS-CoV-2 exposure with a febrile illness that can rapidly progress to shock and multiorgan dysfunction. In both diseases, the clinical and laboratory findings can be nonspecific and present a diagnostic challenge. Thoracic imaging is commonly obtained to assist with initial workup, assessment of disease progression, and guidance of therapy. This paper reviews the radiologic findings of acute COVID-19 pneumonia and MIS-C, highlights the key distinctions between the entities, and summarizes our understanding of the role of imaging in managing SARS-CoV-2-related illness in children.
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20
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Shen ZY, Yan XC, You XD, Zhang XW. CT Imaging Research Progress in COVID-19. Curr Med Imaging 2022; 18:267-274. [PMID: 34465280 PMCID: PMC8972255 DOI: 10.2174/1573405617666210816091217] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2021] [Revised: 06/01/2021] [Accepted: 07/14/2021] [Indexed: 11/22/2022]
Abstract
The highly contagious novel Coronavirus Disease 2019 (COVID-19) broke out at the end of 2019 and has lasted for nearly one year, and the pandemic is still rampant around the world. The diagnosis of COVID-19 is on the basis of the combination of epidemiological history, clinical symptoms, and laboratory and imaging examinations. Among them, imaging examination is of importance in the diagnosis of patients with suspected clinical cases, the investigation of asymptomatic infections and family clustering, the judgment of patient recovery, rediagnosis after disease recurrence, and prognosis prediction. This article reviews the research progress of CT imaging examination in the COVID-19 pandemic.
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Affiliation(s)
- Zhi Yong Shen
- Department of Radiology, Nantong Tumor Hospital, Affiliated Tumor Hospital of Nantong University, Jiangsu 226361, PR China
| | - Xun Cheng Yan
- Department of Radiology, Affiliated Rugao Hospital of Nantong University, Jiangsu 226500, PR China
| | - Xiao Dong You
- Department of Ear-Nose-Throat, Affiliated Hospital of Nantong University, Jiangsu 226001, PR China
| | - Xue Wen Zhang
- Department of Ear-Nose-Throat, Huai'an Second People's Hospital, Affiliated Huai'an Hospital of Xuzhou Medical University, Jiangsu 223002, PR China
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21
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Wu J, Tang J, Zhang T, Chen Y, Du C. Follow‐up CT of “reversed halo sign” in SARS‐CoV‐2 delta VOC pneumonia: A report of two cases. J Med Virol 2021; 94:1289-1291. [PMID: 34931334 DOI: 10.1002/jmv.27533] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2021] [Revised: 11/26/2021] [Accepted: 12/18/2021] [Indexed: 11/08/2022]
Affiliation(s)
- Jing Wu
- Department of Radiology Nanjing First Hospital, Nanjing Medical University Nanjing Jiangsu China
| | - Jie Tang
- Department of Radiology The Second Hospital of Nanjing, Nanjing University of Chinese Medicine Nanjing Jiangsu China
| | - Tao Zhang
- Department of Radiology Nanjing First Hospital, Nanjing Medical University Nanjing Jiangsu China
| | - Yu‐Chen Chen
- Department of Radiology Nanjing First Hospital, Nanjing Medical University Nanjing Jiangsu China
| | - Chao Du
- Department of Radiology The Second Hospital of Nanjing, Nanjing University of Chinese Medicine Nanjing Jiangsu China
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22
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McCann C, Shoeib M, Rashid MI, Kostoulas N. Pneumatocele formation following COVID-19 pneumonia. Is there a role for surgical intervention? Asian Cardiovasc Thorac Ann 2021; 30:2184923211059866. [PMID: 34874785 PMCID: PMC9174975 DOI: 10.1177/02184923211059866] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
COVID-19 mainly causes a lower respiratory tract illness, meaning there has been great interest in the chest and lung radiological findings seen during the course of the disease. Most of this interest has centred around the computed tomographic findings. Most commonly, computed tomographic images report ground-glass opacities but a less common finding, and potential complication associated with COVID-19, is pneumatocele formation. In this case series, we describe the presentation and management of three patients with large pneumatoceles that developed during the recovery phase of COVID-19. A conservative approach is most recommended, with surgical intervention reserved for complicated cases that cause cardiorespiratory compromise.
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Affiliation(s)
- Cameron McCann
- Greater Glasgow and
Clyde, Golden Jubilee National
Hospital, UK
| | - Mohamed Shoeib
- Greater Glasgow and
Clyde, Golden Jubilee National
Hospital, UK
| | | | - Nikos Kostoulas
- Greater Glasgow and
Clyde, Golden Jubilee National
Hospital, UK
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23
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Islam F, Bibi S, Meem AFK, Islam MM, Rahaman MS, Bepary S, Rahman MM, Rahman MM, Elzaki A, Kajoak S, Osman H, ElSamani M, Khandaker MU, Idris AM, Emran TB. Natural Bioactive Molecules: An Alternative Approach to the Treatment and Control of COVID-19. Int J Mol Sci 2021; 22:12638. [PMID: 34884440 PMCID: PMC8658031 DOI: 10.3390/ijms222312638] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2021] [Revised: 11/11/2021] [Accepted: 11/19/2021] [Indexed: 02/07/2023] Open
Abstract
Several coronaviruses (CoVs) have been associated with serious health hazards in recent decades, resulting in the deaths of thousands around the globe. The recent coronavirus pandemic has emphasized the importance of discovering novel and effective antiviral medicines as quickly as possible to prevent more loss of human lives. Positive-sense RNA viruses with group spikes protruding from their surfaces and an abnormally large RNA genome enclose CoVs. CoVs have already been related to a range of respiratory infectious diseases possibly fatal to humans, such as MERS, SARS, and the current COVID-19 outbreak. As a result, effective prevention, treatment, and medications against human coronavirus (HCoV) is urgently needed. In recent years, many natural substances have been discovered with a variety of biological significance, including antiviral properties. Throughout this work, we reviewed a wide range of natural substances that interrupt the life cycles for MERS and SARS, as well as their potential application in the treatment of COVID-19.
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Affiliation(s)
- Fahadul Islam
- Department of Pharmacy, Daffodil International University, Dhaka 1207, Bangladesh; (F.I.); (A.F.K.M.); (M.M.I.); (M.S.R.); (S.B.); (M.M.R.); (M.M.R.)
| | - Shabana Bibi
- Yunnan Herbal Laboratory, College of Ecology and Environmental Sciences, Yunnan University, Kunming 650091, China;
- International Joint Research Center for Sustainable Utilization of Cordyceps Bioresources in China and Southeast Asia, Yunnan University, Kunming 650091, China
| | - Atkia Farzana Khan Meem
- Department of Pharmacy, Daffodil International University, Dhaka 1207, Bangladesh; (F.I.); (A.F.K.M.); (M.M.I.); (M.S.R.); (S.B.); (M.M.R.); (M.M.R.)
| | - Md. Mohaimenul Islam
- Department of Pharmacy, Daffodil International University, Dhaka 1207, Bangladesh; (F.I.); (A.F.K.M.); (M.M.I.); (M.S.R.); (S.B.); (M.M.R.); (M.M.R.)
| | - Md. Saidur Rahaman
- Department of Pharmacy, Daffodil International University, Dhaka 1207, Bangladesh; (F.I.); (A.F.K.M.); (M.M.I.); (M.S.R.); (S.B.); (M.M.R.); (M.M.R.)
| | - Sristy Bepary
- Department of Pharmacy, Daffodil International University, Dhaka 1207, Bangladesh; (F.I.); (A.F.K.M.); (M.M.I.); (M.S.R.); (S.B.); (M.M.R.); (M.M.R.)
| | - Md. Mizanur Rahman
- Department of Pharmacy, Daffodil International University, Dhaka 1207, Bangladesh; (F.I.); (A.F.K.M.); (M.M.I.); (M.S.R.); (S.B.); (M.M.R.); (M.M.R.)
| | - Md. Mominur Rahman
- Department of Pharmacy, Daffodil International University, Dhaka 1207, Bangladesh; (F.I.); (A.F.K.M.); (M.M.I.); (M.S.R.); (S.B.); (M.M.R.); (M.M.R.)
| | - Amin Elzaki
- Department of Radiological Sciences, College of Applied Medical Sciences, Taif University, Taif 21944, Saudi Arabia; (A.E.); (S.K.); (H.O.); (M.E.)
| | - Samih Kajoak
- Department of Radiological Sciences, College of Applied Medical Sciences, Taif University, Taif 21944, Saudi Arabia; (A.E.); (S.K.); (H.O.); (M.E.)
| | - Hamid Osman
- Department of Radiological Sciences, College of Applied Medical Sciences, Taif University, Taif 21944, Saudi Arabia; (A.E.); (S.K.); (H.O.); (M.E.)
| | - Mohamed ElSamani
- Department of Radiological Sciences, College of Applied Medical Sciences, Taif University, Taif 21944, Saudi Arabia; (A.E.); (S.K.); (H.O.); (M.E.)
| | - Mayeen Uddin Khandaker
- Centre for Applied Physics and Radiation Technologies, School of Engineering and Technology, Sunway University, Bandar Sunway 47500, Selangor, Malaysia;
| | - Abubakr M. Idris
- Department of Chemistry, College of Science, King Khalid University, Abha 62529, Saudi Arabia;
- Research Center for Advanced Materials Science (RCAMS), King Khalid University, Abha 62529, Saudi Arabia
| | - Talha Bin Emran
- Department of Pharmacy, BGC Trust University Bangladesh, Chittagong 4381, Bangladesh
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24
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Lee JH, Hong H, Kim H, Lee CH, Goo JM, Yoon SH. CT Examinations for COVID-19: A Systematic Review of Protocols, Radiation Dose, and Numbers Needed to Diagnose and Predict. TAEHAN YONGSANG UIHAKHOE CHI 2021; 82:1505-1523. [PMID: 36238884 PMCID: PMC9431975 DOI: 10.3348/jksr.2021.0096] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/21/2021] [Revised: 07/25/2021] [Accepted: 07/28/2021] [Indexed: 05/31/2023]
Abstract
Purpose Although chest CT has been discussed as a first-line test for coronavirus disease 2019 (COVID-19), little research has explored the implications of CT exposure in the population. To review chest CT protocols and radiation doses in COVID-19 publications and explore the number needed to diagnose (NND) and the number needed to predict (NNP) if CT is used as a first-line test. Materials and Methods We searched nine highly cited radiology journals to identify studies discussing the CT-based diagnosis of COVID-19 pneumonia. Study-level information on the CT protocol and radiation dose was collected, and the doses were compared with each national diagnostic reference level (DRL). The NND and NNP, which depends on the test positive rate (TPR), were calculated, given a CT sensitivity of 94% (95% confidence interval [CI]: 91%-96%) and specificity of 37% (95% CI: 26%-50%), and applied to the early outbreak in Wuhan, New York, and Italy. Results From 86 studies, the CT protocol and radiation dose were reported in 81 (94.2%) and 17 studies (19.8%), respectively. Low-dose chest CT was used more than twice as often as standard-dose chest CT (39.5% vs.18.6%), while the remaining studies (44.2%) did not provide relevant information. The radiation doses were lower than the national DRLs in 15 of the 17 studies (88.2%) that reported doses. The NND was 3.2 scans (95% CI: 2.2-6.0). The NNPs at TPRs of 50%, 25%, 10%, and 5% were 2.2, 3.6, 8.0, 15.5 scans, respectively. In Wuhan, 35418 (TPR, 58%; 95% CI: 27710-56755) to 44840 (TPR, 38%; 95% CI: 35161-68164) individuals were estimated to have undergone CT examinations to diagnose 17365 patients. During the early surge in New York and Italy, daily NNDs changed up to 5.4 and 10.9 times, respectively, within 10 weeks. Conclusion Low-dose CT protocols were described in less than half of COVID-19 publications, and radiation doses were frequently lacking. The number of populations involved in a first-line diagnostic CT test could vary dynamically according to daily TPR; therefore, caution is required in future planning.
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25
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Wu J, Tang J, Zhang T, Chen YC, Du C. SARS-CoV-2 Delta VOC pneumonia with CT follow-ups: A case report. J Med Virol 2021; 94:807-810. [PMID: 34581445 PMCID: PMC8662009 DOI: 10.1002/jmv.27361] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2021] [Revised: 09/06/2021] [Accepted: 09/26/2021] [Indexed: 11/26/2022]
Affiliation(s)
- Jing Wu
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, Jiangsu, P. R. China
| | - Jie Tang
- Department of Radiology, The Second Hospital of Nanjing, Nanjing University of Chinese Medicine, Nanjing, Jiangsu, P. R. China
| | - Tao Zhang
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, Jiangsu, P. R. China
| | - Yu-Chen Chen
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, Jiangsu, P. R. China
| | - Chao Du
- Department of Radiology, The Second Hospital of Nanjing, Nanjing University of Chinese Medicine, Nanjing, Jiangsu, P. R. China
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26
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Morelli C, Francavilla M, Stabile Ianora AA, Cozzolino M, Gualano A, Stellacci G, Sacco A, Lorusso F, Pedote P, De Ceglie M, Scardapane A. The Multifaceted COVID-19: CT Aspects of Its Atypical Pulmonary and Abdominal Manifestations and Complications in Adults and Children. A Pictorial Review. Microorganisms 2021; 9:microorganisms9102037. [PMID: 34683358 PMCID: PMC8541408 DOI: 10.3390/microorganisms9102037] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2021] [Revised: 09/21/2021] [Accepted: 09/23/2021] [Indexed: 12/12/2022] Open
Abstract
Our daily experience in a COVID hospital has allowed us to learn about this disease in many of its changing and unusual aspects. Some of these uncommon manifestations, however, appeared more frequently than others, giving shape to a multifaceted COVID-19 disease. This pictorial review has the aim to describe the radiological aspects of atypical presentations and of some complications of COVID-19 disease in adults and children and provide a simple guide for radiologists to become familiar with the multiform aspects of this disease.
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Affiliation(s)
- Chiara Morelli
- Interdisciplinary Department of Medicine, Section of Diagnostic Imaging, University of Bari Medical School, 70124 Bari, Italy; (A.A.S.I.); (M.C.); (A.G.); (A.S.); (F.L.); (P.P.); (M.D.C.); (A.S.)
- Correspondence:
| | | | - Amato Antonio Stabile Ianora
- Interdisciplinary Department of Medicine, Section of Diagnostic Imaging, University of Bari Medical School, 70124 Bari, Italy; (A.A.S.I.); (M.C.); (A.G.); (A.S.); (F.L.); (P.P.); (M.D.C.); (A.S.)
| | - Monica Cozzolino
- Interdisciplinary Department of Medicine, Section of Diagnostic Imaging, University of Bari Medical School, 70124 Bari, Italy; (A.A.S.I.); (M.C.); (A.G.); (A.S.); (F.L.); (P.P.); (M.D.C.); (A.S.)
| | - Alessandra Gualano
- Interdisciplinary Department of Medicine, Section of Diagnostic Imaging, University of Bari Medical School, 70124 Bari, Italy; (A.A.S.I.); (M.C.); (A.G.); (A.S.); (F.L.); (P.P.); (M.D.C.); (A.S.)
| | | | - Antonello Sacco
- Interdisciplinary Department of Medicine, Section of Diagnostic Imaging, University of Bari Medical School, 70124 Bari, Italy; (A.A.S.I.); (M.C.); (A.G.); (A.S.); (F.L.); (P.P.); (M.D.C.); (A.S.)
| | - Filomenamila Lorusso
- Interdisciplinary Department of Medicine, Section of Diagnostic Imaging, University of Bari Medical School, 70124 Bari, Italy; (A.A.S.I.); (M.C.); (A.G.); (A.S.); (F.L.); (P.P.); (M.D.C.); (A.S.)
| | - Pasquale Pedote
- Interdisciplinary Department of Medicine, Section of Diagnostic Imaging, University of Bari Medical School, 70124 Bari, Italy; (A.A.S.I.); (M.C.); (A.G.); (A.S.); (F.L.); (P.P.); (M.D.C.); (A.S.)
| | - Michele De Ceglie
- Interdisciplinary Department of Medicine, Section of Diagnostic Imaging, University of Bari Medical School, 70124 Bari, Italy; (A.A.S.I.); (M.C.); (A.G.); (A.S.); (F.L.); (P.P.); (M.D.C.); (A.S.)
| | - Arnaldo Scardapane
- Interdisciplinary Department of Medicine, Section of Diagnostic Imaging, University of Bari Medical School, 70124 Bari, Italy; (A.A.S.I.); (M.C.); (A.G.); (A.S.); (F.L.); (P.P.); (M.D.C.); (A.S.)
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27
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Tung-Chen Y, Martí de Gracia M, Parra-Gordo ML, Díez-Tascón A, Agudo-Fernández S, Ossaba-Vélez S. Usefulness of Lung Ultrasound Follow-up in Patients Who Have Recovered From Coronavirus Disease 2019. JOURNAL OF ULTRASOUND IN MEDICINE : OFFICIAL JOURNAL OF THE AMERICAN INSTITUTE OF ULTRASOUND IN MEDICINE 2021; 40:1971-1974. [PMID: 33159704 DOI: 10.1002/jum.15556] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/06/2020] [Revised: 09/24/2020] [Accepted: 10/16/2020] [Indexed: 06/11/2023]
Abstract
Coronavirus disease 2019 is caused by severe acute respiratory syndrome coronavirus 2 infection, which tends to be mild. Even in these cases, our understanding is still incomplete, particularly regarding its sequelae and long-term outcomes. We describe 3 recovered patients who had coronavirus disease 2019, with long-persisting symptoms after recovery, in whom chest computed tomographic and concurrent lung ultrasound examinations were performed. It is possible to correlate the findings from lung ultrasound with the symptoms and the fibrosis or residual abnormalities present on chest computed tomography. Lung ultrasound, which is easy to use, without side effects or radiation, helps monitor the disease resolution or assess early progression to lung fibrosis, as exemplified in the cases reported.
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Affiliation(s)
- Yale Tung-Chen
- Department of Internal Medicine, Hospital Universitario La Paz, Madrid, Spain
| | | | | | - Aurea Díez-Tascón
- Department of Emergency Radiology, Hospital Universitario La Paz, Madrid, Spain
| | | | - Silvia Ossaba-Vélez
- Department of Emergency Radiology, Hospital Universitario La Paz, Madrid, Spain
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28
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Lu W, Wei J, Xu T, Ding M, Li X, He M, Chen K, Yang X, She H, Huang B. Quantitative CT for detecting COVID‑19 pneumonia in suspected cases. BMC Infect Dis 2021; 21:836. [PMID: 34412614 PMCID: PMC8374412 DOI: 10.1186/s12879-021-06556-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2020] [Accepted: 08/10/2021] [Indexed: 01/08/2023] Open
Abstract
Background Corona Virus Disease 2019 (COVID-19) is currently a worldwide pandemic and has a huge impact on public health and socio-economic development. The purpose of this study is to explore the diagnostic value of the quantitative computed tomography (CT) method by using different threshold segmentation techniques to distinguish between patients with or without COVID-19 pneumonia. Methods A total of 47 patients with suspected COVID-19 were retrospectively analyzed, including nine patients with positive real-time fluorescence reverse transcription polymerase chain reaction (RT-PCR) test (confirmed case group) and 38 patients with negative RT-PCR test (excluded case group). An improved 3D convolutional neural network (VB-Net) was used to automatically extract lung lesions. Eight different threshold segmentation methods were used to define the ground glass opacity (GGO) and consolidation. The receiver operating characteristic (ROC) curves were used to compare the performance of various parameters with different thresholds for diagnosing COVID-19 pneumonia. Results The volume of GGO (VOGGO) and GGO percentage in the whole lung (GGOPITWL) were the most effective values for diagnosing COVID-19 at a threshold of − 300 HU, with areas under the curve (AUCs) of 0.769 and 0.769, sensitivity of 66.67 and 66.67%, specificity of 94.74 and 86.84%. Compared with VOGGO or GGOPITWL at a threshold of − 300 Hounsfield units (HU), the consolidation percentage in the whole lung (CPITWL) with thresholds at − 400 HU, − 350 HU, and − 250 HU were statistically different. There were statistical differences in the infection volume and percentage of the whole lung, right lung, and lobes between the two groups. VOGGO, GGOPITWL, and volume of consolidation (VOC) were also statistically different at the threshold of − 300 HU. Conclusions Quantitative CT provides an image quantification method for the auxiliary diagnosis of COVID-19 and is expected to assist in confirming patients with COVID-19 pneumonia in suspected cases. Supplementary Information The online version contains supplementary material available at 10.1186/s12879-021-06556-z.
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Affiliation(s)
- Weiping Lu
- Ningxia Medical University, Yinchuan, 750004, Ningxia, China.,Department of Radiology, Gongli Hospital, 219 Miaopu Road, Pudong New Area, Shanghai, 200135, China
| | - Jianguo Wei
- Ningxia Medical University, Yinchuan, 750004, Ningxia, China.,Department of Radiology, Gongli Hospital, 219 Miaopu Road, Pudong New Area, Shanghai, 200135, China
| | - Tingting Xu
- Department of Radiology, Gongli Hospital, 219 Miaopu Road, Pudong New Area, Shanghai, 200135, China
| | - Miao Ding
- Department of Radiology, Gongli Hospital, 219 Miaopu Road, Pudong New Area, Shanghai, 200135, China
| | - Xiaoyan Li
- Department of Radiology, Gongli Hospital, 219 Miaopu Road, Pudong New Area, Shanghai, 200135, China
| | - Mengxue He
- Department of Radiology, Gongli Hospital, 219 Miaopu Road, Pudong New Area, Shanghai, 200135, China
| | - Kai Chen
- Department of Radiology, Gongli Hospital, 219 Miaopu Road, Pudong New Area, Shanghai, 200135, China
| | - Xiaodan Yang
- Department of Radiology, Gongli Hospital, 219 Miaopu Road, Pudong New Area, Shanghai, 200135, China
| | - Huiyuan She
- Department of Infectious Diseases, Gongli Hospital, 219 Miaopu Road, Pudong New Area, Shanghai, 200135, China.
| | - Bingcang Huang
- Department of Radiology, Gongli Hospital, 219 Miaopu Road, Pudong New Area, Shanghai, 200135, China.
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29
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Nabavi S, Ejmalian A, Moghaddam ME, Abin AA, Frangi AF, Mohammadi M, Rad HS. Medical imaging and computational image analysis in COVID-19 diagnosis: A review. Comput Biol Med 2021; 135:104605. [PMID: 34175533 PMCID: PMC8219713 DOI: 10.1016/j.compbiomed.2021.104605] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2020] [Revised: 06/21/2021] [Accepted: 06/21/2021] [Indexed: 12/11/2022]
Abstract
Coronavirus disease (COVID-19) is an infectious disease caused by a newly discovered coronavirus. The disease presents with symptoms such as shortness of breath, fever, dry cough, and chronic fatigue, amongst others. The disease may be asymptomatic in some patients in the early stages, which can lead to increased transmission of the disease to others. This study attempts to review papers on the role of imaging and medical image computing in COVID-19 diagnosis. For this purpose, PubMed, Scopus and Google Scholar were searched to find related studies until the middle of 2021. The contribution of this study is four-fold: 1) to use as a tutorial of the field for both clinicians and technologists, 2) to comprehensively review the characteristics of COVID-19 as presented in medical images, 3) to examine automated artificial intelligence-based approaches for COVID-19 diagnosis, 4) to express the research limitations in this field and the methods used to overcome them. Using machine learning-based methods can diagnose the disease with high accuracy from medical images and reduce time, cost and error of diagnostic procedure. It is recommended to collect bulk imaging data from patients in the shortest possible time to improve the performance of COVID-19 automated diagnostic methods.
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Affiliation(s)
- Shahabedin Nabavi
- Faculty of Computer Science and Engineering, Shahid Beheshti University, Tehran, Iran.
| | - Azar Ejmalian
- Anesthesiology Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | | | - Ahmad Ali Abin
- Faculty of Computer Science and Engineering, Shahid Beheshti University, Tehran, Iran
| | - Alejandro F Frangi
- Centre for Computational Imaging and Simulation Technologies in Biomedicine (CISTIB), School of Computing, University of Leeds, Leeds, UK
| | - Mohammad Mohammadi
- Department of Medical Physics, Royal Adelaide Hospital, Adelaide, South Australia, Australia; School of Physical Sciences, The University of Adelaide, Adelaide, South Australia, Australia
| | - Hamidreza Saligheh Rad
- Quantitative MR Imaging and Spectroscopy Group (QMISG), Tehran University of Medical Sciences, Tehran, Iran
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Jafari R, Jonaidi-Jafari N, Maghsoudi H, Dehghanpoor F, Schoepf UJ, Ulversoy KA, Saburi A. "Pulmonary target sign" as a diagnostic feature in chest computed tomography of COVID-19. World J Radiol 2021; 13:233-242. [PMID: 34367510 PMCID: PMC8326149 DOI: 10.4329/wjr.v13.i7.233] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Revised: 03/30/2021] [Accepted: 06/15/2021] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND In chest computed tomography (CT) scan, bilateral peripheral multifocal ground-glass opacities, linear opacities, reversed halo sign, and crazy-paving pattern are suggestive for coronavirus disease 2019 (COVID-19) in clinically suspicious cases, but they are not specific for the diagnosis, as other viral pneumonias, like influenza and some viral pneumonia may show similar imaging findings. AIM To find a specific imaging feature of the disease would be a welcome guide in diagnosis and management of challenging cases. METHODS Chest CT imaging findings of 650 patients admitted to a university Hospital in Tehran, Iran between January 2020 and July 2020 with confirmed COVID-19 infection by RT-PCR were reviewed by two expert radiologists. In addition to common non-specific imaging findings of COVID-19 pneumonia, radiologic characteristics of "pulmonary target sign" (PTS) were assessed. PTS is defined as a circular appearance of non-involved pulmonary parenchyma, which encompass a central hyperdense dot surrounded by ground-glass or alveolar opacities. RESULTS PTS were presented in 32 cases (frequency 4.9%). The location of the lesions in 31 of the 32 cases (96.8%) was peripheral, while 4 of the 31 cases had lesions both peripherally and centrally. In 25 cases, the lesions were located near the pleural surface and considered pleural based and half of the lesions (at least one lesion) were in the lower segments and lobes of the lungs. 22 cases had multiple lesions with a > 68% frequency. More than 87% of cases had an adjacent bronchovascular bundle. Ground-glass opacities were detectable adjacent or close to the lesions in 30 cases (93%) and only in 7 cases (21%) was consolidation adjacent to the lesions. CONCLUSION Although it is not frequent in COVID-19, familiarity with this feature may help radiologists and physicians distinguish the disease from other viral and non-infectious pneumonias in challenging cases.
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Affiliation(s)
- Ramezan Jafari
- Department of Radiology, Faculty of Medicine, Baqiyatallah University of Medical Sciences, Tehran 11151877, Iran
- Health Research Center, Baqiyatallah University of Medical Sciences, Tehran 11151877, Iran
| | | | - Houshyar Maghsoudi
- Department of Radiology, Faculty of Medicine, Baqiyatallah University of Medical Sciences, Tehran 11151877, Iran
| | - Fatemeh Dehghanpoor
- Department of Radiology, Faculty of Medicine, Baqiyatallah University of Medical Sciences, Tehran 11151877, Iran
| | - U Joseph Schoepf
- Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC 29425, United States
| | - Kyle A Ulversoy
- Faculty of Medicine, Augusta University/University of Georgia Medical Partnership, Athens, GA 30606, United States
| | - Amin Saburi
- Health Research Center, Baqiyatallah University of Medical Sciences, Tehran 11151877, Iran
- Chemical Injuries Research Center, Systems Biology and Poisonings Institute, Baqiyatallah University of Medical Sciences, Tehran 11151877, Iran
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Shen YT, Chen L, Yue WW, Xu HX. Digital Technology-Based Telemedicine for the COVID-19 Pandemic. Front Med (Lausanne) 2021; 8:646506. [PMID: 34295908 PMCID: PMC8289897 DOI: 10.3389/fmed.2021.646506] [Citation(s) in RCA: 43] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2020] [Accepted: 05/31/2021] [Indexed: 12/23/2022] Open
Abstract
In the year 2020, the coronavirus disease 2019 (COVID-19) crisis intersected with the development and maturation of several digital technologies including the internet of things (IoT) with next-generation 5G networks, artificial intelligence (AI) that uses deep learning, big data analytics, and blockchain and robotic technology, which has resulted in an unprecedented opportunity for the progress of telemedicine. Digital technology-based telemedicine platform has currently been established in many countries, incorporated into clinical workflow with four modes, including "many to one" mode, "one to many" mode, "consultation" mode, and "practical operation" mode, and has shown to be feasible, effective, and efficient in sharing epidemiological data, enabling direct interactions among healthcare providers or patients across distance, minimizing the risk of disease infection, improving the quality of patient care, and preserving healthcare resources. In this state-of-the-art review, we gain insight into the potential benefits of demonstrating telemedicine in the context of a huge health crisis by summarizing the literature related to the use of digital technologies in telemedicine applications. We also outline several new strategies for supporting the use of telemedicine at scale.
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Affiliation(s)
- Yu-Ting Shen
- Department of Medical Ultrasound, Shanghai Tenth People's Hospital, Ultrasound Research and Education Institute, Tongji University Cancer Center, Shanghai Engineering Research Center of Ultrasound Diagnosis and Treatment, Tongji University School of Medicine, Shanghai, China
| | - Liang Chen
- Department of Gastroenterology, Shanghai Tenth People's Hospital, Shanghai, China
| | - Wen-Wen Yue
- Department of Medical Ultrasound, Shanghai Tenth People's Hospital, Ultrasound Research and Education Institute, Tongji University Cancer Center, Shanghai Engineering Research Center of Ultrasound Diagnosis and Treatment, Tongji University School of Medicine, Shanghai, China
| | - Hui-Xiong Xu
- Department of Medical Ultrasound, Shanghai Tenth People's Hospital, Ultrasound Research and Education Institute, Tongji University Cancer Center, Shanghai Engineering Research Center of Ultrasound Diagnosis and Treatment, Tongji University School of Medicine, Shanghai, China
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Berta L, Rizzetto F, De Mattia C, Lizio D, Felisi M, Colombo PE, Carrazza S, Gelmini S, Bianchi L, Artioli D, Travaglini F, Vanzulli A, Torresin A. Automatic lung segmentation in COVID-19 patients: Impact on quantitative computed tomography analysis. Phys Med 2021; 87:115-122. [PMID: 34139383 PMCID: PMC9188767 DOI: 10.1016/j.ejmp.2021.06.001] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Revised: 05/05/2021] [Accepted: 06/04/2021] [Indexed: 12/04/2022] Open
Abstract
Purpose To assess the impact of lung segmentation accuracy in an automatic pipeline for quantitative analysis of CT images. Methods Four different platforms for automatic lung segmentation based on convolutional neural network (CNN), region-growing technique and atlas-based algorithm were considered. The platforms were tested using CT images of 55 COVID-19 patients with severe lung impairment. Four radiologists assessed the segmentations using a 5-point qualitative score (QS). For each CT series, a manually revised reference segmentation (RS) was obtained. Histogram-based quantitative metrics (QM) were calculated from CT histogram using lung segmentationsfrom all platforms and RS. Dice index (DI) and differences of QMs (ΔQMs) were calculated between RS and other segmentations. Results Highest QS and lower ΔQMs values were associated to the CNN algorithm. However, only 45% CNN segmentations were judged to need no or only minimal corrections, and in only 17 cases (31%), automatic segmentations provided RS without manual corrections. Median values of the DI for the four algorithms ranged from 0.993 to 0.904. Significant differences for all QMs calculated between automatic segmentations and RS were found both when data were pooled together and stratified according to QS, indicating a relationship between qualitative and quantitative measurements. The most unstable QM was the histogram 90th percentile, with median ΔQMs values ranging from 10HU and 158HU between different algorithms. Conclusions None of tested algorithms provided fully reliable segmentation. Segmentation accuracy impacts differently on different quantitative metrics, and each of them should be individually evaluated according to the purpose of subsequent analyses.
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Affiliation(s)
- L Berta
- Department of Medical Physics, ASST Grande Ospedale Metropolitano Niguarda, Piazza Ospedale Maggiore 3, 20162 Milan, Italy
| | - F Rizzetto
- Department of Radiology, ASST Grande Ospedale Metropolitano Niguarda, Piazza Ospedale Maggiore 3, 20162 Milan, Italy; Postgraduate School of Diagnostic and Interventional Radiology, Università degli Studi di Milano, via Festa del Perdono 7, 20122, Milan, Italy
| | - C De Mattia
- Department of Medical Physics, ASST Grande Ospedale Metropolitano Niguarda, Piazza Ospedale Maggiore 3, 20162 Milan, Italy
| | - D Lizio
- Department of Medical Physics, ASST Grande Ospedale Metropolitano Niguarda, Piazza Ospedale Maggiore 3, 20162 Milan, Italy
| | - M Felisi
- Department of Medical Physics, ASST Grande Ospedale Metropolitano Niguarda, Piazza Ospedale Maggiore 3, 20162 Milan, Italy
| | - P E Colombo
- Department of Medical Physics, ASST Grande Ospedale Metropolitano Niguarda, Piazza Ospedale Maggiore 3, 20162 Milan, Italy
| | - S Carrazza
- Department of Physics, Università degli Studi di Milano, via Giovanni Celoria 16, 20133 Milan, Italy; Department of Physics, INFN Sezione di Milano, via Giovanni Celoria 16, 20133 Milan, Italy
| | - S Gelmini
- Department of Physics, Università degli Studi di Milano, via Giovanni Celoria 16, 20133 Milan, Italy
| | - L Bianchi
- Department of Radiology, ASST Grande Ospedale Metropolitano Niguarda, Piazza Ospedale Maggiore 3, 20162 Milan, Italy; Postgraduate School of Diagnostic and Interventional Radiology, Università degli Studi di Milano, via Festa del Perdono 7, 20122, Milan, Italy
| | - D Artioli
- Department of Radiology, ASST Grande Ospedale Metropolitano Niguarda, Piazza Ospedale Maggiore 3, 20162 Milan, Italy
| | - F Travaglini
- Department of Radiology, ASST Grande Ospedale Metropolitano Niguarda, Piazza Ospedale Maggiore 3, 20162 Milan, Italy
| | - A Vanzulli
- Department of Radiology, ASST Grande Ospedale Metropolitano Niguarda, Piazza Ospedale Maggiore 3, 20162 Milan, Italy; Department of Oncology and Hemato-Oncology, Università degli Studi di Milano, via Festa del Perdono 7, 20122, Milan, Italy
| | - A Torresin
- Department of Medical Physics, ASST Grande Ospedale Metropolitano Niguarda, Piazza Ospedale Maggiore 3, 20162 Milan, Italy; Department of Physics, Università degli Studi di Milano, via Giovanni Celoria 16, 20133 Milan, Italy.
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Sureka B, Garg PK, Saxena S, Garg MK, Misra S. Role of radiology in RT-PCR negative COVID-19 pneumonia: Review and recommendations. J Family Med Prim Care 2021; 10:1814-1817. [PMID: 34195108 PMCID: PMC8208214 DOI: 10.4103/jfmpc.jfmpc_2108_20] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2020] [Revised: 12/24/2020] [Accepted: 03/23/2021] [Indexed: 12/12/2022] Open
Abstract
Currently, RT-PCR is the gold standard for diagnosing SARS-CoV-2 infection. However, due to the time-consuming laboratory tests and the low positivity rate of RT-PCR, it cannot be an ideal screening tool for infected population. In this review article, we have reviewed studies related to RT-PCR and CT chest and we would like to give our recommendations. Depending upon the patient's clinical symptoms and radiology imaging typical of viral pneumonia compatible with COVID-19 infection, clinicians need to consider isolation of these patients early even if the RT-PCR test is negative.
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Affiliation(s)
- Binit Sureka
- Department of Diagnostic and Interventional Radiology, All India Institute of Medical Sciences (AIIMS), Basni, Jodhpur, Rajasthan, India
| | - Pawan Kumar Garg
- Department of Diagnostic and Interventional Radiology, All India Institute of Medical Sciences (AIIMS), Basni, Jodhpur, Rajasthan, India
| | - Suvinay Saxena
- Department of Diagnostic and Interventional Radiology, All India Institute of Medical Sciences (AIIMS), Basni, Jodhpur, Rajasthan, India
| | - Mahendra Kumar Garg
- Department of General Medicine, All India Institute of Medical Sciences (AIIMS), Basni, Jodhpur, Rajasthan, India
| | - Sanjeev Misra
- Department of Director and Professor Surgical Oncology, All India Institute of Medical Sciences (AIIMS), Basni, Jodhpur, Rajasthan, India
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Agnello F, Rabiolo L, Grassedonio E, Toia P, Midiri F, Spatafora L, Matteini F, Tesè L, La Grutta L, Galia M. Imaging the COVID-19: a practical guide. Monaldi Arch Chest Dis 2021. [PMID: 33794596 DOI: 10.4081/monaldi.2021.1630] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2020] [Accepted: 01/17/2021] [Indexed: 01/08/2023] Open
Abstract
The Coronavirus Disease 2019 (COVID-19) represents the first medical catastrophe of the new millennium. Although imaging is not a screening test for COVID-19, it plays a crucial role in evaluation and follow-up of COVID-19 patients. In this paper, we will review typical and atypical imaging findings of COVID-19.
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Affiliation(s)
- Francesco Agnello
- Department of Radiology, Policlinico "Paolo Giaccone", University of Palermo.
| | - Lidia Rabiolo
- Department of Radiology, Policlinico "Paolo Giaccone", University of Palermo.
| | | | - Patrizia Toia
- Department of Radiology, Policlinico "Paolo Giaccone", University of Palermo.
| | - Federico Midiri
- Department of Radiology, Policlinico "Paolo Giaccone", University of Palermo.
| | | | - Francesco Matteini
- Department of Radiology, Policlinico "Paolo Giaccone", University of Palermo.
| | - Lorenzo Tesè
- Department of Radiology, Azienda Ospedali Riuniti Villa Sofia-Cervello, Palermo.
| | - Ludovico La Grutta
- Department of Radiology, Policlinico "Paolo Giaccone", University of Palermo.
| | - Massimo Galia
- Department of Radiology, Policlinico "Paolo Giaccone", University of Palermo.
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Rizzetto F, Perillo N, Artioli D, Travaglini F, Cuccia A, Zannoni S, Tombini V, Di Domenico SL, Albertini V, Bergamaschi M, Cazzaniga M, De Mattia C, Torresin A, Vanzulli A. Correlation between lung ultrasound and chest CT patterns with estimation of pulmonary burden in COVID-19 patients. Eur J Radiol 2021; 138:109650. [PMID: 33743491 PMCID: PMC7948674 DOI: 10.1016/j.ejrad.2021.109650] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2020] [Revised: 02/21/2021] [Accepted: 03/09/2021] [Indexed: 02/08/2023]
Abstract
Purpose The capability of lung ultrasound (LUS) to distinguish the different pulmonary patterns of COVID-19 and quantify the disease burden compared to chest CT is still unclear. Methods PCR-confirmed COVID-19 patients who underwent both LUS and chest CT at the Emergency Department were retrospectively analysed. In both modalities, twelve peripheral lung zones were identified and given a Severity Score basing on main lesion pattern. On CT scans the well-aerated lung volume (%WALV) was visually estimated. Per-patient and per-zone assessments of LUS classification performance taking CT findings as reference were performed, further revisioning the images in case of discordant results. Correlations between number of disease-positive lung zones, Severity Score and %WALV on both LUS and CT were assessed. The area under receiver operating characteristic curve (AUC) was calculated to determine LUS performance in detecting %WALV ≤ 70 %. Results The study included 219 COVID-19 patients with abnormal chest CT. LUS correctly identified as positive 217 (99 %) patients, but per-zone analysis showed sensitivity = 75 % and specificity = 66 %. The revision of the 121 (55 %) cases with positive LUS and negative CT revealed COVID-compatible lesions in 42 (38 %) CT scans. Number of disease-positive zones, Severity Score and %WALV between LUS and CT showed moderate correlations. The AUCs for LUS Severity Score and number of LUS-positive zones did not differ in detecting %WALV ≤ 70 %. Conclusion LUS in COVID-19 is valuable for case identification but shows only moderate correlation with CT findings as for lesion patterns and severity quantification. The number of disease-positive lung zones in LUS alone was sufficient to discriminate relevant disease burden.
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Affiliation(s)
- Francesco Rizzetto
- Department of Radiology, ASST Grande Ospedale Metropolitano Niguarda, Piazza Ospedale Maggiore 3, 20162, Milan, Italy.
| | - Noemi Perillo
- Department of Radiology, ASST Grande Ospedale Metropolitano Niguarda, Piazza Ospedale Maggiore 3, 20162, Milan, Italy
| | - Diana Artioli
- Department of Radiology, ASST Grande Ospedale Metropolitano Niguarda, Piazza Ospedale Maggiore 3, 20162, Milan, Italy
| | - Francesca Travaglini
- Department of Radiology, ASST Grande Ospedale Metropolitano Niguarda, Piazza Ospedale Maggiore 3, 20162, Milan, Italy
| | - Alessandra Cuccia
- Department of Radiology, ASST Grande Ospedale Metropolitano Niguarda, Piazza Ospedale Maggiore 3, 20162, Milan, Italy
| | - Stefania Zannoni
- Department of Radiology, ASST Grande Ospedale Metropolitano Niguarda, Piazza Ospedale Maggiore 3, 20162, Milan, Italy
| | - Valeria Tombini
- Emergency Department, ASST Grande Ospedale Metropolitano Niguarda, Piazza Ospedale Maggiore 3, 20162, Milan, Italy
| | - Sandro Luigi Di Domenico
- Emergency Department, ASST Grande Ospedale Metropolitano Niguarda, Piazza Ospedale Maggiore 3, 20162, Milan, Italy
| | - Valentina Albertini
- Postgraduate School of Emergency Medicine and Critical Care, Università degli Studi di Milano-Bicocca, Piazza dell'Ateneo Nuovo 1, 20126, Milan, Italy
| | - Marta Bergamaschi
- Emergency Department, ASST Grande Ospedale Metropolitano Niguarda, Piazza Ospedale Maggiore 3, 20162, Milan, Italy
| | - Michela Cazzaniga
- Emergency Department, ASST Grande Ospedale Metropolitano Niguarda, Piazza Ospedale Maggiore 3, 20162, Milan, Italy
| | - Cristina De Mattia
- Department of Medical Physics, ASST Grande Ospedale Metropolitano Niguarda, Piazza Ospedale Maggiore 3, 20162, Milan, Italy
| | - Alberto Torresin
- Department of Medical Physics, ASST Grande Ospedale Metropolitano Niguarda, Piazza Ospedale Maggiore 3, 20162, Milan, Italy; Department of Physics, Università degli Studi di Milano, via Giovanni Celoria 16, 20133, Milan, Italy
| | - Angelo Vanzulli
- Department of Radiology, ASST Grande Ospedale Metropolitano Niguarda, Piazza Ospedale Maggiore 3, 20162, Milan, Italy; Department of Oncology and Hemato-Oncology, Università degli Studi di Milano, via Festa del Perdono 7, 20122, Milan, Italy
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Preoperative Noninvasive Mapping Allowed Targeted Concomitant Surgical Ablation and Revealed COVID-19 Infection. Case Rep Cardiol 2021; 2021:6651361. [PMID: 33728072 PMCID: PMC7938258 DOI: 10.1155/2021/6651361] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2020] [Revised: 02/19/2021] [Accepted: 02/26/2021] [Indexed: 11/18/2022] Open
Abstract
In March 2020, a 64-year-old female with mitral valve insufficiency and persistent atrial fibrillation underwent preoperative noninvasive mapping for developing an ablation strategy. In the computed tomography (CT) scan, typical signs of COVID-19 were described. Since the consecutive polymerase chain reaction (PCR) test was negative, the severely symptomatic patient was planned for urgent surgery. Noninvasive mapping showed that atrial fibrillation was maintained by left atrial structures and pulmonary veins only. On admission day, the preoperative routine COVID-19 PCR test was positive, and after recovery, uneventful mitral valve repair with cryoablation of the left atrium and pulmonary veins was performed. Our case describes the potential benefit of preoperative noninvasive mapping for the development of a surgical ablation strategy, as well as the challenges in managing urgent surgical patients during the COVID-19 pandemic and the corresponding diagnostic relevance of CT.
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Chest CT findings in RT-PCR positive asymptomatic COVID-19 patients. Clin Imaging 2021; 77:37-42. [PMID: 33640789 PMCID: PMC7846889 DOI: 10.1016/j.clinimag.2021.01.030] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2020] [Revised: 01/21/2021] [Accepted: 01/27/2021] [Indexed: 01/01/2023]
Abstract
Purpose To investigate chest computed tomography (CT) findings in asymptomatic patients tested positive for coronavirus disease (COVID-19) by reverse transcription-polymerase chain reaction (RT-PCR). Material and methods The chest CT images of 64 patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) who were RT-PCR test–positive but asymptomatic were retrospectively evaluated for the appearance and distribution of abnormal parenchymal findings. Results Of the 64 patients (mean age 59.4 ± 12; range 23–85), 42 (65%) were female, and 22 (35%) were male, and 16 (25%) of the patients had no abnormal findings on chest CT. Of the remaining 48 patients, lung involvement was bilateral in 32 (67%). Right upper lobe in 26 (54%), right middle lobe in 20 (42%), right lower lobe in 38 (79%), left upper lobe in 27 (56%), and left lower lobe were affected in 34 (71%) patients. The mean number of opacities detected in patients was 7.5 ± 5.7. The opacities were located only peripherally/subpleural in 22 (46%), only centrally/peribronchovascular in 5 (10%), and mixed in 21 (44%) patients. The frequency of pure ground glass opacities (GGO) was 63% GGO with a crazy-paving pattern or consolidation was 33%. Pure consolidation was detected in only two (4%) patients. Parenchymal opacities were only round in 27 (56%), only geographic demarcated in 3 (6%), only patchy in 2 (4%), and mixed in 16 (33%) patients. Conclusion Chest CT was normal in only one-quarter of the asymptomatic patients. CT findings in asymptomatic COVID-19 patients were often peripherally located, mostly round-shaped GGO.
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Procaccini FL, Alcázar Arroyo R, Albalate Ramón M, Torres Aguilera E, Martín Navarro J, Ryan Murua P, Cintra Cabrera M, Ortega Díaz M, Puerta Carretero M, de Sequera Ortiz P. Acute kidney injury in 3182 patients admitted with COVID-19: a single-center, retrospective, case-control study. Clin Kidney J 2021; 14:1557-1569. [PMID: 34079618 PMCID: PMC7929007 DOI: 10.1093/ckj/sfab021] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2020] [Accepted: 01/11/2021] [Indexed: 01/08/2023] Open
Abstract
Background Acute kidney injury (AKI) may develop in coronavirus disease 2019 (COVID-19) patients and may be associated with a worse outcome. The aim of this study is to describe AKI incidence during the first 45 days of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic in Spain, its reversibility and the association with mortality. Methods This was an observational retrospective case-control study based on patients hospitalized between 1 March and 15 April 2020 with SARS-CoV-2 infection and AKI. Confirmed AKI cases were compared with stable kidney function patients for baseline characteristics, analytical data, treatment and renal outcome. Patients with end-stage kidney disease were excluded. Results AKI incidence was 17.22% among 3182 admitted COVID-19 patients and acute kidney disease (AKD) incidence was 6.82%. The most frequent causes of AKI were prerenal (68.8%) and sepsis (21.9%). Odds ratio (OR) for AKI was increased in patients with pre-existent hypertension [OR 2.58, 95% confidence interval (CI) 1.71-3.89] and chronic kidney disease (CKD) (OR 2.14, 95% CI 1.33-3.42) and in those with respiratory distress (OR 2.37, 95% CI 1.52-3.70). Low arterial pressure at admission increased the risk for Stage 3 AKI (OR 1.65, 95% CI 1.09-2.50). Baseline kidney function was not recovered in 45.73% of overall AKI cases and in 52.75% of AKI patients with prior CKD. Mortality was 38.5% compared with 13.4% of the overall sample population. AKI increased mortality risk at any time of hospitalization (hazard ratio 1.45, 95% CI 1.09-1.93). Conclusions AKI is frequent in COVID-19 patients and is associated with mortality, independently of acute respiratory distress syndrome. AKD was also frequent and merits adequate follow-up.
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Affiliation(s)
- Fabio L Procaccini
- Department of Nephrology, University Hospital Infanta Leonor, Madrid, Spain
| | | | | | | | | | - Pablo Ryan Murua
- Department of Internal Medicine, University Hospital Infanta Leonor, Madrid, Spain
| | | | - Mayra Ortega Díaz
- Department of Nephrology, University Hospital Infanta Leonor, Madrid, Spain
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Tudoran C, Tudoran M, Lazureanu VE, Marinescu AR, Pop GN, Pescariu AS, Enache A, Cut TG. Evidence of Pulmonary Hypertension after SARS-CoV-2 Infection in Subjects without Previous Significant Cardiovascular Pathology. J Clin Med 2021; 10:199. [PMID: 33430492 PMCID: PMC7827420 DOI: 10.3390/jcm10020199] [Citation(s) in RCA: 43] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2020] [Revised: 12/23/2020] [Accepted: 01/05/2021] [Indexed: 12/17/2022] Open
Abstract
BACKGROUND Coronavirus (Covid-19) infection represents a serious medical condition, often associated with cardiovascular complications, pulmonary hypertension (PH), and right ventricle dysfunction (RVD). The aim of this study is to show, by means of transthoracic echocardiography (TTE), the presence of an increased estimated systolic pressure in the pulmonary artery (esPAP) and altered right ventricular global longitudinal strain (RV-GLS) in patients without history of PH. METHODS In a group of 91 patients, aged under 55 years, hospitalized for a moderate Covid-19 infection, a thorough cardiologic and TTE examination were performed two months after discharge. Their initial thorax computer-tomography (TCT) images and laboratory data were accessed from the electronic data base of the hospital. RESULTS We observed an increased prevalence of PH (7.69%) and RVD (10.28%), significantly correlated with the initial levels of the TCT score and inflammatory factors (p ˂ 0.001), but borderline changes were observed in more patients. Multivariate regression analysis showed that these factors and RV-GLS explain 89.5% of elevated esPAP. CONCLUSIONS In COVID-19 infection, PH and RVD are common complications, being encountered after the recovery even in moderate cases. It appears to be a connection between their severity and the extent of the initial pulmonary injury and of the inflammatory response.
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Affiliation(s)
- Cristina Tudoran
- Department VII, Internal Medicine II, Discipline of Cardiology, University of Medicine and Pharmacy “Victor Babes” Timisoara, E. Murgu Square, Nr. 2, 300041 Timisoara, Romania;
| | - Mariana Tudoran
- Department VII, Internal Medicine II, Discipline of Cardiology, University of Medicine and Pharmacy “Victor Babes” Timisoara, E. Murgu Square, Nr. 2, 300041 Timisoara, Romania;
| | - Voichita Elena Lazureanu
- Department XIII, Discipline of Infectious Diseases, University of Medicine and Pharmacy “Victor Babes” Timisoara, E. Murgu Square, Nr. 2, 300041 Timisoara, Romania; (V.E.L.); (A.R.M.)
| | - Adelina Raluca Marinescu
- Department XIII, Discipline of Infectious Diseases, University of Medicine and Pharmacy “Victor Babes” Timisoara, E. Murgu Square, Nr. 2, 300041 Timisoara, Romania; (V.E.L.); (A.R.M.)
| | - Gheorghe Nicusor Pop
- University of Medicine and Pharmacy “Victor Babes” Timisoara, E. Murgu Square, Nr. 2, 300041 Timisoara, Romania; (G.N.P.); (A.S.P.); (T.G.C.)
| | - Alexandru Silvius Pescariu
- University of Medicine and Pharmacy “Victor Babes” Timisoara, E. Murgu Square, Nr. 2, 300041 Timisoara, Romania; (G.N.P.); (A.S.P.); (T.G.C.)
| | - Alexandra Enache
- Department VIII, Discipline of Forensic Medicine, University of Medicine and Pharmacy “Victor Babes” Timisoara, E. Murgu Square, Nr. 2, 300041 Timisoara, Romania;
| | - Talida Georgiana Cut
- University of Medicine and Pharmacy “Victor Babes” Timisoara, E. Murgu Square, Nr. 2, 300041 Timisoara, Romania; (G.N.P.); (A.S.P.); (T.G.C.)
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Jafari R, Maghsoudi H, Saburi A. A Unique Feature of COVID-19 Infection in Chest CT; "Pulmonary Target" Appearance. Acad Radiol 2021; 28:146-147. [PMID: 33246787 PMCID: PMC7680083 DOI: 10.1016/j.acra.2020.11.004] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2020] [Revised: 11/07/2020] [Accepted: 11/10/2020] [Indexed: 12/17/2022]
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Blaize M, Mayaux J, Luyt CE, Lampros A, Fekkar A. COVID-19-related Respiratory Failure and Lymphopenia Do Not Seem Associated with Pneumocystosis. Am J Respir Crit Care Med 2020; 202:1734-1736. [PMID: 32941062 PMCID: PMC7737598 DOI: 10.1164/rccm.202007-2938le] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Affiliation(s)
- Marion Blaize
- Groupe Hospitalier La Pitié-Salpêtrière Paris, France
| | - Julien Mayaux
- Groupe Hospitalier La Pitié-Salpêtrière Paris, France
| | - Charles-Edouard Luyt
- Groupe Hospitalier La Pitié-Salpêtrière Paris, France.,UMRS_1166-ICAN Institute of Cardiometabolism and Nutrition Paris, France and
| | | | - Arnaud Fekkar
- Groupe Hospitalier La Pitié-Salpêtrière Paris, France.,Centre d'Immunologie et des Maladies Infectieuses Cimi-Paris, France
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Asare-Boateng K, Mensah YB, Mensah NA, Oliver-Commey J, Oduro-Mensah E. A review of chest radiographic patterns in mild to moderate novel corona virus disease 2019 at an urban hospital in Ghana. Ghana Med J 2020; 54:46-51. [PMID: 33976441 PMCID: PMC8087370 DOI: 10.4314/gmj.v54i4s.8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
INTRODUCTION The novel corona virus disease 2019 (COVID-19) was diagnosed in Wuhan, China in December 2019 and, in Ghana, in March 2020. As of 30th July 2020, Ghana had recorded 35,142 cases. COVID-19 which can be transmitted by both symptomatic and asymptomatic individuals usually manifest as pneumonia with symptoms like fever, cough, dyspnoea and fatigue. The current non-availability of a vaccine or drug for COVID-19 management calls for early detection and isolation of affected individuals. Chest imaging has become an integral part of patient management with chest radiography serving as a primary imaging modality in many centres. METHODS The study was a retrospective study conducted at Ga East Municipal Hospital (GEMH). Chest radiographs of patients with mild to moderate disease managed at GEMH were evaluated. The age, gender, symptom status, comorbidities and chest x-ray findings of the patients were documented. RESULTS 11.4 % of the patients had some form of respiratory abnormality on chest radiography with 88.9% showing COVID-19 pneumonia features. 93.8% showed ground glass opacities (GGO), with 3.1% each showing consolidation (CN) only and CN with GGO. There was a significant association between COVID-19 radiographic features and patient's age, symptom status and comorbidities but not with gender. CONCLUSION Most radiographs were normal with only 11% showing COVID-19-like abnormality. There was a significant association between age, symptom status and comorbidities with the presence of COVID-19 like features but not for gender. There was no association between the extent of the lung changes and patient characteristics. FUNDING None declared.
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Affiliation(s)
| | - Yaw B Mensah
- Department of Radiology, University of Ghana Medical School, Korle Bu, Accra
| | - Naa Adjeley Mensah
- University of Ghana, Regional Institute of Population Studies, Legon, Accra
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Picchi G, Mari A, Ricciardi A, Carucci AC, Sinatti G, Cosimini B, Di Norcia M, Iapadre N, Balsano C, Grimaldi A. Three Cases of COVID-19 Pneumonia in Female Patients in Italy Who Had Pulmonary Fibrosis on Follow-Up Lung Computed Tomography Imaging. AMERICAN JOURNAL OF CASE REPORTS 2020; 21:e926921. [PMID: 33219200 PMCID: PMC7690330 DOI: 10.12659/ajcr.926921] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2020] [Revised: 10/07/2020] [Accepted: 09/25/2020] [Indexed: 12/13/2022]
Abstract
BACKGROUND Since December 2019, an outbreak caused by a novel coronavirus infection (severe acute respiratory syndrome coronavirus 2, SARS-CoV-2) occurred in Wuhan, China, and it rapidly spread all over the world. The clinical spectrum of coronavirus disease 2019 (COVID-19) is wide, with acute respiratory distress syndrome (ARDS) occurring in 15% of patients affected, requiring high oxygen support. Currently, there is no clearly effective antiviral therapy. Steroids and immunomodulators are under investigation for potential activity. Little is known about middle and long-term sequelae on respiratory function. According to some authors, COVID-19 could cause pulmonary fibrosis. We report 3 cases of pulmonary fibrosis detected on follow-up computed tomography (CT) imaging in 3 female patients who recovered from COVID-19 pneumonia in Italy (L'Aquila, Abruzzo). CASE REPORT All patients were female and had no significant previous respiratory disease or history of smoke exposure, and none had received high-flow oxygen support during treatment of the disease. In all cases, late onset of mild dyspnea, slow and incomplete respiratory recovery, and early evidence of fibrous signs on chest CT scan were characteristic of the clinical course. CONCLUSIONS This report focuses on a possible scenario of long-term lung damage in COVID-19 pneumonia survivors. Limitations are lack of long-term follow-up and functional data in the very early phase. It is advantageous that all COVID-19 pneumonia patients undergo serial chest CT and spirometry long-term follow-up for at least 1 year to assess residual damage. This is particularly relevant in those with slow respiratory recovery and long hospitalization.
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Affiliation(s)
- Giovanna Picchi
- Department of Infectious Disease, San Salvatore Hospital, L’Aquila, Italy
| | - Alessia Mari
- Department of Infectious Disease, San Salvatore Hospital, L’Aquila, Italy
| | - Alessandra Ricciardi
- Department of Infectious Disease, IRCCS San Matteo Polyclinic Foundation, Pavia, Italy
| | | | - Gaia Sinatti
- Department of Life, Health and Environmental Science, L’Aquila University, L’Aquila, Italy
| | - Benedetta Cosimini
- Department of Life, Health and Environmental Science, L’Aquila University, L’Aquila, Italy
| | - Monica Di Norcia
- Division of Internal Medicine and Nephrology, School of Internal Medicine, L’Aquila University, L’Aquila, Italy
| | - Nerio Iapadre
- Department of Infectious Disease, San Salvatore Hospital, L’Aquila, Italy
| | - Clara Balsano
- Department of Life, Health and Environmental Science, L’Aquila University, L’Aquila, Italy
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Contribution of CT Features in the Diagnosis of COVID-19. Can Respir J 2020; 2020:1237418. [PMID: 33224361 PMCID: PMC7670585 DOI: 10.1155/2020/1237418] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2020] [Revised: 09/19/2020] [Accepted: 10/28/2020] [Indexed: 02/06/2023] Open
Abstract
The outbreak of novel coronavirus disease 2019 (COVID-19) first occurred in Wuhan, Hubei Province, China, and spread across the country and worldwide quickly. It has been defined as a major global health emergency by the World Health Organization (WHO). As this is a novel virus, its diagnosis is crucial to clinical treatment and management. To date, real-time reverse transcription-polymerase chain reaction (RT-PCR) has been recognized as the diagnostic criterion for COVID-19. However, the results of RT-PCR can be complemented by the features obtained in chest computed tomography (CT). In this review, we aim to discuss the diagnosis and main CT features of patients with COVID-19 based on the results of the published literature, in order to enhance the understanding of COVID-19 and provide more detailed information regarding treatment.
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45
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Marchiori E, Nobre LF, Hochhegger B, Zanetti G. The reversed halo sign: Considerations in the context of the COVID-19 pandemic. Thromb Res 2020; 195:228-230. [PMID: 32799128 PMCID: PMC7397932 DOI: 10.1016/j.thromres.2020.08.001] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2020] [Revised: 07/12/2020] [Accepted: 08/01/2020] [Indexed: 02/07/2023]
Affiliation(s)
- Edson Marchiori
- Federal University of Rio de Janeiro, Av. Pedro Calmon, 550 - Cidade Universitária da Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil.
| | - Luiz Felipe Nobre
- Federal University of Santa Catarina, R. Eng. Agronômico Andrei Cristian Ferreira, s/n - Trindade, Florianópolis, Santa Catarina, Brazil; R. Desemb. Pedro Silva, 2800, ap.303B. Coqueiros, CEP 88080-701 Florianópolis, Santa Catarina, Brazil
| | - Bruno Hochhegger
- Irmandade Santa Casa de Misericórdia de Porto Alegre, Rua Professor Annes Dias, 295 - Centro Histórico, Porto Alegre, Rio Grande do Sul, Brazil; Rua João Alfredo, 558/301, CEP 90050-230 Porto Alegre, Brazil
| | - Gláucia Zanetti
- Federal University of Rio de Janeiro, Av. Pedro Calmon, 550 - Cidade Universitária da Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil; Rua Coronel Veiga, 733/504. Centro. CEP 25655-504 Petrópolis, Rio de Janeiro, Brazil
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Adams HJA, Kwee TC, Yakar D, Hope MD, Kwee RM. Chest CT Imaging Signature of Coronavirus Disease 2019 Infection: In Pursuit of the Scientific Evidence. Chest 2020; 158:1885-1895. [PMID: 32592709 PMCID: PMC7314684 DOI: 10.1016/j.chest.2020.06.025] [Citation(s) in RCA: 85] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2020] [Revised: 06/01/2020] [Accepted: 06/12/2020] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Chest CT may be used for the diagnosis of coronavirus disease 2019 (COVID-19), but clear scientific evidence is lacking. Therefore, we systematically reviewed and meta-analyzed the chest CT imaging signature of COVID-19. RESEARCH QUESTION What is the chest CT imaging signature of COVID-19 infection? STUDY DESIGN AND METHODS A systematic literature search was performed for original studies on chest CT imaging findings in patients with COVID-19. Methodologic quality of studies was evaluated. Pooled prevalence of chest CT imaging findings were calculated with the use of a random effects model in case of between-study heterogeneity (predefined as I2 ≥50); otherwise, a fixed effects model was used. RESULTS Twenty-eight studies were included. The median number of patients with COVID-19 per study was 124 (range, 50-476), comprising a total of 3,466 patients. Median prevalence of symptomatic patients was 99% (range, >76.3%-100%). Twenty-seven of the studies (96%) had a retrospective design. Methodologic quality concerns were present with either risk of or actual referral bias (13 studies), patient spectrum bias (eight studies), disease progression bias (26 studies), observer variability bias (27 studies), and test review bias (14 studies). Pooled prevalence was 10.6% for normal chest CT imaging findings. Pooled prevalences were 90.0% for posterior predilection, 81.0% for ground-glass opacity, 75.8% for bilateral abnormalities, 73.1% for left lower lobe involvement, 72.9% for vascular thickening, and 72.2% for right lower lobe involvement. Pooled prevalences were 5.2% for pleural effusion, 5.1% for lymphadenopathy, 4.1% for airway secretions/tree-in-bud sign, 3.6% for central lesion distribution, 2.7% for pericardial effusion, and 0.7% for cavitation/cystic changes. Pooled prevalences of other CT imaging findings ranged between 10.5% and 63.2%. INTERPRETATION Studies on chest CT imaging findings in COVID-19 suffer from methodologic quality concerns. More high-quality research is necessary to establish diagnostic CT criteria for COVID-19. Based on the available evidence that requires cautious interpretation, several chest CT imaging findings appear to be suggestive of COVID-19, but normal chest CT imaging findings do not exclude COVID-19, not even in symptomatic patients.
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Affiliation(s)
- Hugo J A Adams
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, The Netherlands
| | - Thomas C Kwee
- Department of Radiology, Nuclear Medicine and Molecular Imaging, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.
| | - Derya Yakar
- Department of Radiology, Nuclear Medicine and Molecular Imaging, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Michael D Hope
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA; Radiology Service, Veterans Affairs Medical Center, San Francisco, CA
| | - Robert M Kwee
- Department of Radiology, Zuyderland Medical Center, Heerlen/Sittard/Geleen, The Netherlands
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Bressem KK, Adams LC, Albrecht J, Petersen A, Thieß HM, Niehues A, Niehues SM, Vahldiek JL. Is lung density associated with severity of COVID-19? Pol J Radiol 2020; 85:e600-e606. [PMID: 33204375 PMCID: PMC7654311 DOI: 10.5114/pjr.2020.100788] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2020] [Accepted: 09/03/2020] [Indexed: 02/07/2023] Open
Abstract
PURPOSE Emphysema and chronic obstructive lung disease were previously identified as major risk factors for severe disease progression in COVID-19. Computed tomography (CT)-based lung-density analysis offers a fast, reliable, and quantitative assessment of lung density. Therefore, we aimed to assess the benefit of CT-based lung density measurements to predict possible severe disease progression in COVID-19. MATERIAL AND METHODS Thirty COVID-19-positive patients were included in this retrospective study. Lung density was quantified based on routinely acquired chest CTs. Presence of COVID-19 was confirmed by reverse transcription polymerase chain reaction (RT-PCR). Wilcoxon test was used to compare two groups of patients. A multivariate regression analysis, adjusted for age and sex, was employed to model the relative increase of risk for severe disease, depending on the measured densities. RESULTS Intensive care unit (ICU) patients or patients requiring mechanical ventilation showed a lower proportion of medium- and low-density lung volume compared to patients on the normal ward, but a significantly larger volume of high-density lung volume (12.26 dl IQR 4.65 dl vs. 7.51 dl vs. IQR 5.39 dl, p = 0.039). In multivariate regression analysis, high-density lung volume was identified as a significant predictor of severe disease. CONCLUSIONS The amount of high-density lung tissue showed a significant association with severe COVID-19, with odds ratios of 1.42 (95% CI: 1.09-2.00) and 1.37 (95% CI: 1.03-2.11) for requiring intensive care and mechanical ventilation, respectively. Acknowledging our small sample size as an important limitation; our study might thus suggest that high-density lung tissue could serve as a possible predictor of severe COVID-19.
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Affiliation(s)
- Keno K. Bressem
- Correspondence address: Dr. Keno K. Bressem, Charité Universitätsmedizin Berlin, Berlin, Germany, e-mail:
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Knol WG, Thuijs DJFM, Odink AE, Maurovich-Horvat P, de Jong PA, Krestin GP, Bogers AJJC, Budde RPJ. Preoperative Chest Computed Tomography Screening for Coronavirus Disease 2019 in Asymptomatic Patients Undergoing Cardiac Surgery. Semin Thorac Cardiovasc Surg 2020; 33:417-424. [PMID: 32979478 PMCID: PMC7567660 DOI: 10.1053/j.semtcvs.2020.09.027] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2020] [Accepted: 09/19/2020] [Indexed: 02/07/2023]
Abstract
Due to the outbreak of Severe Acute Respiratory Syndrome coronavirus (SARS-Cov-2), an efficient COVID-19 screening strategy is required for patients undergoing cardiac surgery. The objective of this prospective observational study was to evaluate the role of preoperative computed tomography (CT) screening for COVID-19 in a population of COVID-19 asymptomatic patients scheduled for cardiac surgery. Between the 29th of March and the 26th of May 2020, patients asymptomatic for COVID-19 underwent a CT-scan the day before surgery, with reverse-transcriptase polymerase-chain reaction (RT-PCR) reserved for abnormal scan results. The primary endpoint was the prevalence of abnormal scans, which was evaluated using the CO-RADS score, a COVID-19 specific grading system. In a secondary analysis, the rate of abnormal scans was compared between the screening cohort and matched historical controls who underwent routine preoperative CT-screening prior to the SARS-Cov-2 outbreak. Of the 109 patients that underwent CT-screening, an abnormal scan result was observed in 7.3% (95% confidence interval: 3.2–14.0%). One patient, with a normal screening CT, was tested positive for COVID-19, with the first positive RT-PCR on the ninth day after surgery. A rate of preoperative CT-scan abnormalities of 8% (n = 8) was found in the unexposed historical controls (P > 0.999). In asymptomatic patients undergoing cardiac surgery, preoperative screening for COVID-19 using computed tomography will identify pulmonary abnormalities in a small percentage of patients that do not seem to have COVID-19. Depending on the prevalence of COVID-19, this results in an unfavorable positive predictive value of CT screening. Care should be taken when considering CT as a screening tool prior to cardiac surgery.
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Affiliation(s)
- Wiebe G Knol
- Department of Cardiothoracic Surgery, Erasmus University Medical Center, Rotterdam, The Netherlands; Department of Radiology and Nuclear Medicine, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Daniel J F M Thuijs
- Department of Cardiothoracic Surgery, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Arlette E Odink
- Department of Radiology and Nuclear Medicine, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Pál Maurovich-Horvat
- Department of Radiology, Medical Imaging Centre, Semmelweis University, Budapest, Hungary
| | - Pim A de Jong
- Department of Radiology, University Medical Center Utrecht and Utrecht University, The Netherlands
| | - Gabriel P Krestin
- Department of Radiology and Nuclear Medicine, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Ad J J C Bogers
- Department of Cardiothoracic Surgery, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Ricardo P J Budde
- Department of Radiology and Nuclear Medicine, Erasmus University Medical Center, Rotterdam, The Netherlands.
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Marvisi M, Ferrozzi F, Balzarini L, Mancini C, Ramponi S, Uccelli M. First report on clinical and radiological features of COVID-19 pneumonitis in a Caucasian population: Factors predicting fibrotic evolution. Int J Infect Dis 2020; 99:485-488. [PMID: 32841688 PMCID: PMC7443096 DOI: 10.1016/j.ijid.2020.08.054] [Citation(s) in RCA: 35] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2020] [Revised: 08/16/2020] [Accepted: 08/19/2020] [Indexed: 01/13/2023] Open
Abstract
BACKGROUND At the end of February, the Lombardy region (Northern Italy) was involved in the pandemic spread of the new COVID-19. We here summarize the clinical and radiological characteristics of 90 confirmed cases and analyze their role in predicting the evolution of fibrosis. METHODS We retrospectively analyzed the clinical and radiological data of 90 patients with COVID-19 pneumonitis. All subjects underwent an HRCT study on the day of admission and eight weeks later, and were treated with lopinavir + ritonavir (Kaletra) 400/100 mg two times a day or darunavir + ritonavir two times a day, and Hydroxychloroquine 200 mg two times a day. Pulmonary fibrosis was defined according to the Fleischner Society glossary of terms for thoracic imaging. RESULTS Twenty-three patients developed pulmonary fibrosis (25.5%): 15 were males, whose mean age was 75 ± 15. The majority were active smokers (60.8%) and had comorbidities (78.2%), above all, hypertension (47.8%), and diabetes (34.7%). Interestingly, in our series of cases, the "reversed halo sign" is frequent (63%) and seems to be a typical COVID-19 pneumonitis pattern. The patients showing fibrosis had a higher grade of systemic inflammation (ESR and PCR) and appeared to have bone marrow inhibition with a significant reduction in platelets, leukocytes, and hemoglobin. CONCLUSIONS To conclude, our data showed that the reversed halo sign associated with a ground-glass pattern may be a typical HRCT pattern of COVID-19 pneumonitis. The evolution to pulmonary fibrosis is frequent in older males and patients with comorbidities and bone marrow involvement.
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Affiliation(s)
- Maurizio Marvisi
- Dept. of Internal Medicine and Pneumology, Istituto Figlie di San Camillo, Cremona, Italy.
| | | | - Laura Balzarini
- Dept. of Internal Medicine and Pneumology, Istituto Figlie di San Camillo, Cremona, Italy
| | - Chiara Mancini
- Dept. of Internal Medicine and Pneumology, Istituto Figlie di San Camillo, Cremona, Italy
| | - Sara Ramponi
- Dept. of Internal Medicine and Pneumology, Istituto Figlie di San Camillo, Cremona, Italy
| | - Mario Uccelli
- Dept. of Radiology, Istituto Figlie di San Camillo, Cremona, Italy
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Mehan WA, Yoon BC, Lang M, Li MD, Rincon S, Buch K. Paraspinal Myositis in Patients with COVID-19 Infection. AJNR Am J Neuroradiol 2020; 41:1949-1952. [PMID: 32763902 DOI: 10.3174/ajnr.a6711] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2020] [Accepted: 06/12/2020] [Indexed: 01/07/2023]
Abstract
Myalgia is a previously reported symptom in patients with COVID-19 infection; however, the presence of paraspinal myositis has not been previously reported. We report MR imaging findings of the spine obtained in a cohort of 9 patients with COVID-19 infection who presented to our hospital between March 3, 2020 and May 6, 2020. We found that 7 of 9 COVID-19 patients (78%) who underwent MR imaging of the spine had MR imaging evidence of paraspinal myositis, characterized by intramuscular edema and/or enhancement. Five of these 7 patients had a prolonged hospital course (greater than 25 days). Our knowledge of the imaging manifestations of COVID-19 infection is expanding. It is important for clinicians>a to be aware of the relatively high frequency of paraspinal myositis in this small cohort of patients with COVID-19 infection.
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Affiliation(s)
- W A Mehan
- From the Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts
| | - B C Yoon
- From the Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts
| | - M Lang
- From the Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts
| | - M D Li
- From the Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts
| | - S Rincon
- From the Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts
| | - K Buch
- From the Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts.
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