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Jeon D, Kim SH, Kim J, Jeong H, Uhm C, Oh H, Cho K, Cho Y, Park IH, Oh J, Kim JJ, Hwang JY, Lee HJ, Lee HY, Seo JY, Shin JS, Seong JK, Nam KT. Discovery of a new long COVID mouse model via systemic histopathological comparison of SARS-CoV-2 intranasal and inhalation infection. Biochim Biophys Acta Mol Basis Dis 2024; 1870:167347. [PMID: 39019092 DOI: 10.1016/j.bbadis.2024.167347] [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: 02/29/2024] [Revised: 07/03/2024] [Accepted: 07/03/2024] [Indexed: 07/19/2024]
Abstract
Intranasal infection is commonly used to establish a SARS-CoV-2 mouse model due to its non-invasive procedures and a minimal effect from the operation itself. However, mice intranasally infected with SARS-CoV-2 have a high mortality rate, which limits the utility of this model for exploring therapeutic strategies and the sequelae of non-fatal COVID-19 cases. To resolve these limitations, an aerosolised viral administration method has been suggested. However, an in-depth pathological analysis comparing the two models is lacking. Here, we show that inhalation and intranasal SARS-CoV-2 (106 PFU) infection models established in K18-hACE2 mice develop unique pathological features in both the respiratory and central nervous systems, which could be directly attributed to the infection method. While the inhalation-infection model exhibited relatively milder pathological parameters, it closely mimicked the prevalent chest CT pattern observed in COVID-19 patients with focal, peripheral lesions and fibrotic scarring in the recuperating lung. We also found the evidence of direct neuron-invasion from the olfactory receptor neurons to the olfactory bulb in the intranasal model and showed the trigeminal nerve as an alternative route of transmission to the brain in inhalation infected mice. Even after viral clearance confirmed at 14 days post-infection, mild lesions were still found in the brain of inhalation-infected mice. These findings suggest that the inhalation-infection model has advantages over the intranasal-infection model in closely mimicking the pathological features of non-fatal symptoms of COVID-19, demonstrating its potential to study the sequelae and possible interventions for long COVID.
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Affiliation(s)
- Donghun Jeon
- Department of Biomedical Sciences, Brain Korea 21 Project, Yonsei University College of Medicine, Seoul, South Korea
| | - Sung-Hee Kim
- Department of Biomedical Sciences, Brain Korea 21 Project, Yonsei University College of Medicine, Seoul, South Korea
| | - Jiseon Kim
- Department of Biomedical Sciences, Brain Korea 21 Project, Yonsei University College of Medicine, Seoul, South Korea
| | - Haengdueng Jeong
- Department of Biomedical Sciences, Brain Korea 21 Project, Yonsei University College of Medicine, Seoul, South Korea
| | - Chanyang Uhm
- Department of Biomedical Sciences, Brain Korea 21 Project, Yonsei University College of Medicine, Seoul, South Korea
| | - Heeju Oh
- Department of Biomedical Sciences, Brain Korea 21 Project, Yonsei University College of Medicine, Seoul, South Korea
| | - Kyungrae Cho
- Department of Biomedical Sciences, Brain Korea 21 Project, Yonsei University College of Medicine, Seoul, South Korea
| | - Yejin Cho
- Department of Biomedical Sciences, Brain Korea 21 Project, Yonsei University College of Medicine, Seoul, South Korea
| | - In Ho Park
- Department of Biomedical Sciences, Brain Korea 21 Project, Yonsei University College of Medicine, Seoul, South Korea; Institute of Immunology and Immunological Diseases, Yonsei University College of Medicine, Seoul, South Korea
| | - Jooyeon Oh
- Department of Microbiology, Yonsei University College of Medicine, Seoul, South Korea
| | - Jeong Jin Kim
- Department of Biomedical Sciences, Brain Korea 21 Project, Yonsei University College of Medicine, Seoul, South Korea
| | - Ji-Yeon Hwang
- Preclinical Research Center, Seoul National University Bundang Hospital, Seongnam, South Korea
| | - Hyo-Jung Lee
- Department of Periodontology, Section of Dentistry, Seoul National University Bundang Hospital, Seongnam, South Korea
| | - Ho-Young Lee
- Department of Nuclear Medicine, Seoul National University Bundang Hospital, Seongnam, South Korea; Department of Nuclear Medicine, Seoul National University, College of Medicine, Seoul, South Korea
| | - Jun-Young Seo
- Department of Biomedical Sciences, Brain Korea 21 Project, Yonsei University College of Medicine, Seoul, South Korea
| | - Jeon-Soo Shin
- Department of Biomedical Sciences, Brain Korea 21 Project, Yonsei University College of Medicine, Seoul, South Korea; Institute of Immunology and Immunological Diseases, Yonsei University College of Medicine, Seoul, South Korea; Department of Microbiology, Yonsei University College of Medicine, Seoul, South Korea
| | - Je Kyung Seong
- Korea Mouse Phenotyping Center (KMPC), Seoul National University, Seoul, South Korea; Laboratory of Developmental Biology and Genomics, Research Institute for Veterinary Science, BK 21 PLUS Program for Creative Veterinary Science Research, College of Veterinary Medicine, Seoul National University, Seoul, South Korea; BIO MAX Institute, Seoul National University, Seoul, South Korea; Interdisciplinary Program for Bioinformatics, Seoul National University, Seoul, South Korea.
| | - Ki Taek Nam
- Department of Biomedical Sciences, Brain Korea 21 Project, Yonsei University College of Medicine, Seoul, South Korea.
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Loo C, Treacy MG, Toerien L, Msellati A, Catanzano T. Emergency Presentations of Coronavirus Disease (COVID-19): A Review of the Literature and Radiologic Perspective. Semin Ultrasound CT MR 2024; 45:332-338. [PMID: 38996944 DOI: 10.1053/j.sult.2024.07.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/14/2024]
Abstract
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is responsible for the debilitating global pandemic known as Coronavirus disease (COVID-19). In this paper, we highlight the widespread manifestations and complications across disease systems. In addition, we present their relevant imaging findings to inform appropriate investigations and management in patients presenting to the Emergency Department with COVID-19 and its respective sequalae. Of note, we outline considerations for diagnosis of long COVID, an important medium to long term sequalae in patients with previous COVID-19 infections.
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Affiliation(s)
- Caitlyn Loo
- School of Medicine, University College Dublin, Belfield, Ireland; Department of Surgery, Mater Misericordiae University Hospital, Dublin, Ireland
| | - Molly Godson Treacy
- Department of Radiology, Mater Misericordiae University Hospital, Dublin, Ireland
| | - Lara Toerien
- Department of Radiology, Mater Misericordiae University Hospital, Dublin, Ireland
| | | | - Tara Catanzano
- Department of Radiology, Baystate Health, Springfield, MA.
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Laro J, Xue B, Zheng J, Ness M, Perlman S, McCall LI. SARS-CoV-2 infection unevenly impacts metabolism in the coronal periphery of the lungs. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.22.595414. [PMID: 38952797 PMCID: PMC11216382 DOI: 10.1101/2024.05.22.595414] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/03/2024]
Abstract
COVID-19 significantly decreases amino acids, fatty acids, and most eicosanoidsSARS-CoV-2 preferentially localizes to central lung tissueMetabolic disturbance is highest in peripheral tissue, not central like viral loadSpatial metabolomics allows detection of metabolites not altered overallSARS-CoV-2, the virus responsible for COVID-19, is a highly contagious virus that can lead to hospitalization and death. COVID-19 is characterized by its involvement in the lungs, particularly the lower lobes. To improve patient outcomes and treatment options, a better understanding of how SARS-CoV-2 impacts the body, particularly the lower respiratory system, is required. In this study, we sought to understand the spatial impact of COVID-19 on the lungs of mice infected with mouse-adapted SARS2-N501Y MA30 . Overall, infection caused a decrease in fatty acids, amino acids, and most eicosanoids. When analyzed by segment, viral loads were highest in central lung tissue, while metabolic disturbance was highest in peripheral tissue. Infected peripheral lung tissue was characterized by lower levels of fatty acids and amino acids when compared to central lung tissue. This study highlights the spatial impacts of SARS-CoV-2 and helps explain why peripheral lung tissue is most damaged by COVID-19.
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Bucher AM, Henzel K, Meyer HJ, Ehrengut C, Müller L, Schramm D, Akinina A, Drechsel M, Kloeckner R, Isfort P, Sähn MJ, Fink M, More D, Melekh B, Meinel FG, Dreger F, May M, Siegler L, Münzfeld H, Ruppel R, Penzkofer T, Kim MS, Balzer M, Borggrefe J, Surov A. Pericardial Effusion Predicts Clinical Outcomes in Patients with COVID-19: A Nationwide Multicenter Study. Acad Radiol 2024; 31:1784-1791. [PMID: 38155024 DOI: 10.1016/j.acra.2023.12.003] [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: 08/28/2023] [Revised: 11/25/2023] [Accepted: 12/01/2023] [Indexed: 12/30/2023]
Abstract
RATIONALE AND OBJECTIVES The prognostic role of pericardial effusion (PE) in Covid 19 is unclear. The aim of the present study was to estimate the prognostic role of PE in patients with Covid 19 in a large multicentre setting. MATERIALS AND METHODS This retrospective study is a part of the German multicenter project RACOON (Radiological Cooperative Network of the Covid 19 pandemic). The acquired sample comprises 1197 patients, 363 (30.3%) women and 834 (69.7%) men. In every case, chest computed tomography was analyzed for PE. Data about 30-day mortality, need for mechanical ventilation and need for intensive care unit (ICU) admission were collected. Data were evaluated by means of descriptive statistics. Group differences were calculated with Mann-Whitney test and Fisher exact test. Uni-and multivariable regression analyses were performed. RESULTS Overall, 46.4% of the patients were admitted to ICU, mechanical lung ventilation was performed in 26.6% and 30-day mortality was 24%. PE was identified in 159 patients (13.3%). The presence of PE was associated with 30-day mortality: HR= 1.54, CI 95% (1.05; 2.23), p = 0.02 (univariable analysis), and HR= 1.60, CI 95% (1.03; 2.48), p = 0.03 (multivariable analysis). Furthermore, density of PE was associated with the need for intubation (OR=1.02, CI 95% (1.003; 1.05), p = 0.03) and the need for ICU admission (OR=1.03, CI 95% (1.005; 1.05), p = 0.01) in univariable regression analysis. The presence of PE was associated with 30-day mortality in male patients, HR= 1.56, CI 95%(1.01-2.43), p = 0.04 (multivariable analysis). In female patients, none of PE values predicted clinical outcomes. CONCLUSION The prevalence of PE in Covid 19 is 13.3%. PE is an independent predictor of 30-day mortality in male patients with Covid 19. In female patients, PE plays no predictive role.
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Affiliation(s)
- Andreas Michael Bucher
- Institute of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Frankfut, Germany (A.M.B., K.H.)
| | - Kristina Henzel
- Institute of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Frankfut, Germany (A.M.B., K.H.)
| | - Hans Jonas Meyer
- Department of Radiology, University Hospital of Leipzig, Leipzig, Germany (H.J.M., C.E.)
| | - Constantin Ehrengut
- Department of Radiology, University Hospital of Leipzig, Leipzig, Germany (H.J.M., C.E.)
| | - Lukas Müller
- Department of Diagnostic and Interventional Radiology, University Medical Center Mainz, Mainz, Germany (L.M.)
| | - Dominik Schramm
- Department of Radiology University Hospital of Halle, Halle, Germany (D.S., A.A., M.D.)
| | - Alena Akinina
- Department of Radiology University Hospital of Halle, Halle, Germany (D.S., A.A., M.D.)
| | - Michelle Drechsel
- Department of Radiology University Hospital of Halle, Halle, Germany (D.S., A.A., M.D.)
| | - Roman Kloeckner
- Department of Radiology University Hospital Schleswig-Holstein-Campus Luebeck, Luebeck, Germany (R.K.)
| | - Peter Isfort
- Department of Radiology University Hospital of Aachen, Aachen, Germany (P.I., M.J.S.)
| | - Marwin-Jonathan Sähn
- Department of Radiology University Hospital of Aachen, Aachen, Germany (P.I., M.J.S.)
| | - Matthias Fink
- Clinic for Diagnostic and Interventional Radiology, University Hospital Heidelberg, Heidelberg, Germany (M.F., D.M.)
| | - Dorottya More
- Clinic for Diagnostic and Interventional Radiology, University Hospital Heidelberg, Heidelberg, Germany (M.F., D.M.)
| | - Bohdan Melekh
- Department of Radiology and Nuclear Medicine, University Hospital of Magdeburg, Magdeburg, Germany (B.M., A.S.)
| | - Felix G Meinel
- Department of Radiology University Hospital of Rostock, Rostock, Germany (F.G.M., F.D.)
| | - Franziska Dreger
- Department of Radiology University Hospital of Rostock, Rostock, Germany (F.G.M., F.D.)
| | - Matthias May
- Department of Radiology University Hospital of Erlangen, Erlangen, Germany (M.M., L.S.)
| | - Lisa Siegler
- Department of Radiology University Hospital of Erlangen, Erlangen, Germany (M.M., L.S.)
| | - Hanna Münzfeld
- Department of Radiology University Hospital of Berlin, Berlin, Germany (H.M., R.R., T.P.)
| | - Richard Ruppel
- Department of Radiology University Hospital of Berlin, Berlin, Germany (H.M., R.R., T.P.)
| | - Tobias Penzkofer
- Department of Radiology University Hospital of Berlin, Berlin, Germany (H.M., R.R., T.P.)
| | - Moon-Sung Kim
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany (M.S.K., B.M.)
| | - Miriam Balzer
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany (M.S.K., B.M.)
| | - Jan Borggrefe
- Department of Radiology, Neuroradiology and Nuclear Medicine, Johannes Wesling University Hospital, Ruhr-University-Bochum, Bochum, Germany (J.B., A.S.)
| | - Alexey Surov
- Department of Radiology and Nuclear Medicine, University Hospital of Magdeburg, Magdeburg, Germany (B.M., A.S.); Department of Radiology, Neuroradiology and Nuclear Medicine, Johannes Wesling University Hospital, Ruhr-University-Bochum, Bochum, Germany (J.B., A.S.).
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5
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Trinh CD, Le VN, Le VNB, Pham NT, Le VD. Lung abnormalities on computed tomography of Vietnamese patients with COVID-19 and the association with medical variables. IJID REGIONS 2024; 10:183-190. [PMID: 38351902 PMCID: PMC10862005 DOI: 10.1016/j.ijregi.2024.01.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Revised: 01/14/2024] [Accepted: 01/15/2024] [Indexed: 02/16/2024]
Abstract
Objectives Patients with COVID-19 may experience a lung injury without presenting clinical symptoms. Early detection of lung injury in patients with COVID-19 is required to enhance prediction and prevent severe progression. Methods Lung lesions in patients with COVID-19 were defined using the Fleischner Society terminology. Chest computed tomography lesions and their correlation with demographic characteristics and medical variables were identified. Results Patients with mild and moderate COVID-19 had up to 45% lung injuries, whereas critical patients had 55%. However, patients with mild and moderate COVID-19 typically had low-level lung injuries. Ground-glass (68.1%), consolidation (48.8%), opacity (36.3%), and nodular (6.9%) lung lesions were the most prevalent in patients with COVID-19. Patients with COVID-19 infected with the Delta variant had worse lung injury than those infected with the Alpha and Omicron. People vaccinated with ≥2 doses showed a lower risk of lung injury than those vaccinated with <1 dose. Patients <18 years old were less likely to have a lung injury than patients >18 years old. The treatment outcomes were unaffected by the severity of the lung injury. Conclusion Patients with mild COVID-19 had a similar risk of lung injury as patients with severe COVID-19. Thus, using chest computed tomography to detect lung injury can enhance the treatment outcomes and reduce the patient's risk of pulmonary complications.
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Affiliation(s)
- Cong Dien Trinh
- Departments of Infectious Disease, Military Hospital 103, Hanoi, Vietnam
| | - Van Nam Le
- Departments of Infectious Disease, Military Hospital 103, Hanoi, Vietnam
| | | | - Ngoc Thach Pham
- Micobiology and Molecular Biology Department, National Hospital for Tropical Diseases, Hanoi, Vietnam
| | - Van Duyet Le
- Micobiology and Molecular Biology Department, National Hospital for Tropical Diseases, Hanoi, Vietnam
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Catarata MJ, Creamer AW, Dias M, Toland S, Chaabouni M, Verbeke K, Vieira Naia J, Hassan M, Naidu SB, Lynch GA, Blyth KG, Rahman NM, Hardavella G. ERS International Congress 2023: highlights from the Thoracic Oncology Assembly. ERJ Open Res 2024; 10:00860-2023. [PMID: 38410708 PMCID: PMC10895436 DOI: 10.1183/23120541.00860-2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2023] [Accepted: 11/07/2023] [Indexed: 02/28/2024] Open
Abstract
Lung cancer is the leading cause of cancer mortality in the world. It greatly affects the patients' quality of life, and is thus a challenge for the daily practice in respiratory medicine. Advances in the genetic knowledge of thoracic tumours' mutational landscape, and the development of targeted therapies and immune checkpoint inhibitors, have led to a paradigm shift in the treatment of lung cancer and pleural mesothelioma. During the 2023 European Respiratory Society Congress in Milan, Italy, experts from all over the world presented their high-quality research and reviewed best clinical practices. Lung cancer screening, management of early stages of lung cancer, application of artificial intelligence and biomarkers were discussed and they will be summarised here.
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Affiliation(s)
- Maria Joana Catarata
- Pulmonology Department, Hospital de Braga, Braga, Portugal
- Tumour and Microenvironment Interactions Group, I3S – Institute for Health Research and Innovation, University of Porto, Porto, Portugal
| | | | - Margarida Dias
- Pulmonology Department, Centro Hospitalar Vila Nova de Gaia/Espinho, Vila Nova de Gaia, Portugal
| | - Sile Toland
- Department of Medicine, Letterkenny University Hospital, Letterkenny, Ireland
| | - Malek Chaabouni
- Asklepios Klinik Altona, Department of Internal Medicine II, Pulmonology and Thoracic Oncology Section, Hamburg, Germany
| | - Koen Verbeke
- Department of Respiratory Medicine, University Hospital Saint-Pierre, Université Libre de Bruxelles, Brussels, Belgium
| | | | - Maged Hassan
- Chest Diseases Department, Alexandria University Faculty of Medicine, Alexandria, Egypt
| | | | - Geraldine A. Lynch
- Academic Respiratory Unit, University of Bristol Medical School, Bristol, UK
| | - Kevin G. Blyth
- Queen Elizabeth University Hospital, Glasgow, UK
- Institute of Infection, Immunity and Inflammation, University of Glasgow, Glasgow, UK
| | - Najib M. Rahman
- Oxford University Hospitals NHS Foundation Trust, Oxford NIHR Biomedical Research Centre, John Radcliffe Hospital, Headington, UK
| | - Georgia Hardavella
- 9th Department of Respiratory Medicine, Sotiria Athens Chest Diseases Hospital, Athens, Greece
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Geudens V, Van Slambrouck J, Aerts G, Willems L, Goos T, Kaes J, Zajacova A, Gyselinck I, Aelbrecht C, Vermaut A, Beeckmans H, Vermant M, De Fays C, Sacreas A, Aversa L, Orlitova M, Vanstapel A, Josipovic I, Boone MN, McDonough JE, Weynand B, Pilette C, Janssens W, Dupont L, Wuyts WA, Verleden GM, Van Raemdonck DE, Vos R, Gayan-Ramirez G, Ceulemans LJ, Vanaudenaerde BM. COVID-19 progression in hospitalized patients using follow-up in vivo CT and ex vivo microCT. J Thorac Dis 2023; 15:3646-3661. [PMID: 37559650 PMCID: PMC10407474 DOI: 10.21037/jtd-22-1488] [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] [Received: 10/21/2022] [Accepted: 05/31/2023] [Indexed: 08/11/2023]
Abstract
BACKGROUND Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes coronavirus disease-19 (COVID-19) which can lead to acute respiratory distress syndrome (ARDS) and evolve to pulmonary fibrosis. Computed tomography (CT) is used to study disease progression and describe radiological patterns in COVID-19 patients. This study aimed to assess disease progression regarding lung volume and density over time on follow-up in vivo chest CT and give a unique look at parenchymal and morphological airway changes in "end-stage" COVID-19 lungs using ex vivo microCT. METHODS Volumes and densities of the lung/lobes of three COVID-19 patients were assessed using follow-up in vivo CT and ex vivo whole lung microCT scans. Airways were quantified by airway segmentations on whole lung microCT and small-partition microCT. As controls, three discarded healthy donor lungs were used. Histology was performed in differently affected regions in the COVID-19 lungs. RESULTS In vivo, COVID-19 lung volumes decreased while density increased over time, mainly in lower lobes as previously shown. Ex vivo COVID-19 lung volumes decreased by 60% and all lobes were smaller compared to controls. Airways were more visible on ex vivo microCT in COVID-19, probably due to fibrosis and increased airway diameter. In addition, small-partition microCT showed more deformation of (small) airway morphology and fibrotic organization in severely affected regions with heterogeneous distributions within the same lung which was confirmed by histology. CONCLUSIONS COVID-19-ARDS and subsequent pulmonary fibrosis alters lung architecture and airway morphology which is described using in vivo CT, ex vivo microCT, and histology.
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Affiliation(s)
- Vincent Geudens
- Laboratory of Respiratory Diseases and Thoracic Surgery, BREATHE, Department of Chrometa, KU Leuven, Leuven, Belgium
| | - Jan Van Slambrouck
- Laboratory of Respiratory Diseases and Thoracic Surgery, BREATHE, Department of Chrometa, KU Leuven, Leuven, Belgium
| | - Gitte Aerts
- Laboratory of Respiratory Diseases and Thoracic Surgery, BREATHE, Department of Chrometa, KU Leuven, Leuven, Belgium
| | - Lynn Willems
- Laboratory of Respiratory Diseases and Thoracic Surgery, BREATHE, Department of Chrometa, KU Leuven, Leuven, Belgium
| | - Tinne Goos
- Laboratory of Respiratory Diseases and Thoracic Surgery, BREATHE, Department of Chrometa, KU Leuven, Leuven, Belgium
| | - Janne Kaes
- Laboratory of Respiratory Diseases and Thoracic Surgery, BREATHE, Department of Chrometa, KU Leuven, Leuven, Belgium
| | - Andrea Zajacova
- Prague Lung Transplant Program, Department of Pneumology, Motol University Hospital, 2nd Faculty of Medicine, Charles University in Prague, Prague, Czech Republic
| | - Iwein Gyselinck
- Laboratory of Respiratory Diseases and Thoracic Surgery, BREATHE, Department of Chrometa, KU Leuven, Leuven, Belgium
| | - Celine Aelbrecht
- Laboratory of Respiratory Diseases and Thoracic Surgery, BREATHE, Department of Chrometa, KU Leuven, Leuven, Belgium
| | - Astrid Vermaut
- Laboratory of Respiratory Diseases and Thoracic Surgery, BREATHE, Department of Chrometa, KU Leuven, Leuven, Belgium
| | - Hanne Beeckmans
- Laboratory of Respiratory Diseases and Thoracic Surgery, BREATHE, Department of Chrometa, KU Leuven, Leuven, Belgium
| | - Marie Vermant
- Laboratory of Respiratory Diseases and Thoracic Surgery, BREATHE, Department of Chrometa, KU Leuven, Leuven, Belgium
| | - Charlotte De Fays
- Department of Pneumology, Cliniques Universitaires Saint-Luc, Institute of Experimental and Clinical Research, Université Catholique de Louvain, Brussels, Belgium
| | - Annelore Sacreas
- Laboratory of Respiratory Diseases and Thoracic Surgery, BREATHE, Department of Chrometa, KU Leuven, Leuven, Belgium
| | - Lucia Aversa
- Laboratory of Respiratory Diseases and Thoracic Surgery, BREATHE, Department of Chrometa, KU Leuven, Leuven, Belgium
| | - Michaela Orlitova
- Division of Anesthesiology and Algology, Department of Cardiovascular Sciences, KU Leuven, Leuven, Belgium
| | - Arno Vanstapel
- Laboratory of Respiratory Diseases and Thoracic Surgery, BREATHE, Department of Chrometa, KU Leuven, Leuven, Belgium
| | - Ivan Josipovic
- Department of Physics and Astronomy, Centre for X-Ray Tomography (UGCT), Radiation Physics, Ghent University, Gent, Belgium
| | - Matthieu N. Boone
- Department of Physics and Astronomy, Centre for X-Ray Tomography (UGCT), Radiation Physics, Ghent University, Gent, Belgium
| | - John E. McDonough
- Section of Pulmonary, Critical Care, and Sleep Medicine, Yale University School of Medicine, New Haven, CT, USA
| | - Birgit Weynand
- Department of Imaging & Pathology, KU Leuven, Leuven, Belgium
| | - Charles Pilette
- Department of Pneumology, Cliniques Universitaires Saint-Luc, Institute of Experimental and Clinical Research, Université Catholique de Louvain, Brussels, Belgium
| | - Wim Janssens
- Laboratory of Respiratory Diseases and Thoracic Surgery, BREATHE, Department of Chrometa, KU Leuven, Leuven, Belgium
| | - Lieven Dupont
- Laboratory of Respiratory Diseases and Thoracic Surgery, BREATHE, Department of Chrometa, KU Leuven, Leuven, Belgium
| | - Wim A. Wuyts
- Laboratory of Respiratory Diseases and Thoracic Surgery, BREATHE, Department of Chrometa, KU Leuven, Leuven, Belgium
| | - Geert M. Verleden
- Laboratory of Respiratory Diseases and Thoracic Surgery, BREATHE, Department of Chrometa, KU Leuven, Leuven, Belgium
| | - Dirk E. Van Raemdonck
- Laboratory of Respiratory Diseases and Thoracic Surgery, BREATHE, Department of Chrometa, KU Leuven, Leuven, Belgium
| | - Robin Vos
- Laboratory of Respiratory Diseases and Thoracic Surgery, BREATHE, Department of Chrometa, KU Leuven, Leuven, Belgium
| | - Ghislaine Gayan-Ramirez
- Laboratory of Respiratory Diseases and Thoracic Surgery, BREATHE, Department of Chrometa, KU Leuven, Leuven, Belgium
| | - Laurens J. Ceulemans
- Laboratory of Respiratory Diseases and Thoracic Surgery, BREATHE, Department of Chrometa, KU Leuven, Leuven, Belgium
| | - Bart M. Vanaudenaerde
- Laboratory of Respiratory Diseases and Thoracic Surgery, BREATHE, Department of Chrometa, KU Leuven, Leuven, Belgium
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Takahashi T, Sumi T, Michimata H, Nagayama D, Koshino Y, Watanabe H, Yamada Y, Chiba H. Fatal diffuse alveolar hemorrhage caused by acute COVID-19 infection in an unvaccinated patient. QJM 2023; 116:521-522. [PMID: 36727497 DOI: 10.1093/qjmed/hcad018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Revised: 01/29/2023] [Accepted: 01/31/2023] [Indexed: 02/03/2023] Open
Affiliation(s)
- T Takahashi
- Department of Respiratory Medicine, Hakodate Goryoukaku Hospital, 38-3 Goryoukaku-cho, Hakodate-shi, Hokkaido 040-8611, Japan and Department of Respiratory Medicine and Allergology, Sapporo Medical University School of Medicine, Sapporo, Japan
| | - T Sumi
- Department of Respiratory Medicine, Hakodate Goryoukaku Hospital, 38-3 Goryoukaku-cho, Hakodate-shi, Hokkaido 040-8611, Japan and Department of Respiratory Medicine and Allergology, Sapporo Medical University School of Medicine, Sapporo, Japan
| | - H Michimata
- Department of Respiratory Medicine, Hakodate Goryoukaku Hospital, 38-3 Goryoukaku-cho, Hakodate-shi, Hokkaido 040-8611, Japan and Department of Respiratory Medicine and Allergology, Sapporo Medical University School of Medicine, Sapporo, Japan
| | - D Nagayama
- Department of Respiratory Medicine, Hakodate Goryoukaku Hospital, 38-3 Goryoukaku-cho, Hakodate-shi, Hokkaido 040-8611, Japan and Department of Respiratory Medicine and Allergology, Sapporo Medical University School of Medicine, Sapporo, Japan
| | - Y Koshino
- Department of Respiratory Medicine, Hakodate Goryoukaku Hospital, 38-3 Goryoukaku-cho, Hakodate-shi, Hokkaido 040-8611, Japan and Department of Respiratory Medicine and Allergology, Sapporo Medical University School of Medicine, Sapporo, Japan
| | - H Watanabe
- Department of Respiratory Medicine, Hakodate Goryoukaku Hospital, 38-3 Goryoukaku-cho, Hakodate-shi, Hokkaido 040-8611, Japan
| | - Y Yamada
- Department of Respiratory Medicine, Hakodate Goryoukaku Hospital, 38-3 Goryoukaku-cho, Hakodate-shi, Hokkaido 040-8611, Japan
| | - H Chiba
- Department of Respiratory Medicine and Allergology, Sapporo Medical University School of Medicine, Sapporo, Japan
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9
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Degli Antoni M, Crosato V, Pennati F, Borghesi A, Cristini G, Allegri R, Capone S, Bergamasco A, Soresina A, Badolato R, Maroldi R, Quiros-Roldan E, Matteelli A, Castelli F, Focà E. COVID-19 Pneumonia with Migratory Pattern in Agammaglobulinemic Patients: A Report of Two Cases and Review of Literature. Tomography 2023; 9:894-900. [PMID: 37218933 DOI: 10.3390/tomography9030073] [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: 01/30/2023] [Revised: 04/06/2023] [Accepted: 04/11/2023] [Indexed: 05/24/2023] Open
Abstract
X-linked agammaglobulinemia (XLA) is a primary immunodeficiency characterized by marked reduction in serum immunoglobulins and early-onset infections. Coronavirus Disease-2019 (COVID-19) pneumonia in immunocompromised patients presents clinical and radiological peculiarities which have not yet been completely understood. Very few cases of agammaglobulinemic patients with COVID-19 have been reported since the beginning of the pandemic in February 2020. We report two cases of migrant COVID-19 pneumonia in XLA patients.
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Affiliation(s)
- Melania Degli Antoni
- Unit of Infectious and Tropical Diseases, Department of Clinical and Experimental Sciences, University of Brescia and ASST Spedali Civili di Brescia, 25123 Brescia, Italy
| | - Verena Crosato
- Unit of Infectious and Tropical Diseases, Department of Clinical and Experimental Sciences, University of Brescia and ASST Spedali Civili di Brescia, 25123 Brescia, Italy
| | - Francesca Pennati
- Unit of Infectious and Tropical Diseases, Department of Clinical and Experimental Sciences, University of Brescia and ASST Spedali Civili di Brescia, 25123 Brescia, Italy
| | - Andrea Borghesi
- Department of Medical and Surgical Specialties, Radiological Sciences and Public Health, University of Brescia and ASST Spedali Civili of Brescia, 25123 Brescia, Italy
| | - Graziella Cristini
- Unit of Infectious and Tropical Diseases, University of Brescia and ASST Spedali Civili di Brescia, 25123 Brescia, Italy
| | - Roberto Allegri
- Unit of Infectious and Tropical Diseases, University of Brescia and ASST Spedali Civili di Brescia, 25123 Brescia, Italy
| | - Susanna Capone
- Unit of Infectious and Tropical Diseases, University of Brescia and ASST Spedali Civili di Brescia, 25123 Brescia, Italy
| | - Alberto Bergamasco
- Unit of Infectious and Tropical Diseases, University of Brescia and ASST Spedali Civili di Brescia, 25123 Brescia, Italy
| | - Annarosa Soresina
- Pediatric Immunology Unit, Ospedale dei Bambini, ASST Spedali Civili di Brescia, 25123 Brescia, Italy
| | - Raffaele Badolato
- Molecular Medicine Institute "Angelo Nocivelli", Department of Clinical and Experimental Sciences, University of Brescia, 25123 Brescia, Italy
| | - Roberto Maroldi
- Department of Medical and Surgical Specialties, Radiological Sciences and Public Health, University of Brescia and ASST Spedali Civili of Brescia, 25123 Brescia, Italy
| | - Eugenia Quiros-Roldan
- Unit of Infectious and Tropical Diseases, Department of Clinical and Experimental Sciences, University of Brescia and ASST Spedali Civili di Brescia, 25123 Brescia, Italy
| | - Alberto Matteelli
- Unit of Infectious and Tropical Diseases, Department of Clinical and Experimental Sciences, University of Brescia and ASST Spedali Civili di Brescia, 25123 Brescia, Italy
| | - Francesco Castelli
- Unit of Infectious and Tropical Diseases, Department of Clinical and Experimental Sciences, University of Brescia and ASST Spedali Civili di Brescia, 25123 Brescia, Italy
| | - Emanuele Focà
- Unit of Infectious and Tropical Diseases, Department of Clinical and Experimental Sciences, University of Brescia and ASST Spedali Civili di Brescia, 25123 Brescia, Italy
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10
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Vaz N, Franquet E, Heidari P, Chow DZ, Jacene HA, Ng TSC. COVID-19: Findings in nuclear medicine from head to toe. Clin Imaging 2023; 99:10-18. [PMID: 37043868 PMCID: PMC10081937 DOI: 10.1016/j.clinimag.2023.04.003] [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: 10/02/2022] [Accepted: 04/03/2023] [Indexed: 04/14/2023]
Abstract
COVID-19 is a multisystemic disease, and hence its potential manifestations on nuclear medicine imaging can extend beyond the lung. Therefore, it is important for the nuclear medicine physician to recognize these manifestations in the clinic. While FDG-PET/CT is not indicated routinely in COVID-19 evaluation, its unique capability to provide a functional and anatomical assessment of the entire body means that it can be a powerful tool to monitor acute, subacute, and long-term effects of COVID-19. Single-photon scintigraphy is routinely used to assess conditions such as pulmonary embolism, cardiac ischemia, and thyroiditis, and COVID-19 may present in these studies. The most common nuclear imaging finding of COVID-19 vaccination to date is hypermetabolic axillary lymphadenopathy. This may pose important diagnostic and management dilemmas in oncologic patients, particularly those with malignancies where the axilla constitutes a lymphatic drainage area. This article aims to summarize the relevant literature published since the beginning of the pandemic on the intersection between COVID-19 and nuclear medicine.
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Affiliation(s)
- Nuno Vaz
- Department of Radiology, Brigham and Women's Hospital, 75 Francis St, Boston, MA 02115, United States.
| | - Elisa Franquet
- Department of Radiology, Brigham and Women's Hospital, 75 Francis St, Boston, MA 02115, United States
| | - Pedram Heidari
- Department of Radiology, Massachusetts General Hospital, 55 Fruit St, Boston, MA 02114, United States
| | - David Z Chow
- Department of Radiology, Massachusetts General Hospital, 55 Fruit St, Boston, MA 02114, United States
| | - Heather A Jacene
- Department of Radiology, Brigham and Women's Hospital, 75 Francis St, Boston, MA 02115, United States
| | - Thomas S C Ng
- Department of Radiology, Massachusetts General Hospital, 55 Fruit St, Boston, MA 02114, United States
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11
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van Griensven J, Diro E, Yansouni CP. Hidden sources of bias in diagnostic studies: the example of visceral leishmaniasis in east Africa. THE LANCET. INFECTIOUS DISEASES 2023; 23:e108-e114. [PMID: 36400065 DOI: 10.1016/s1473-3099(22)00643-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Revised: 09/06/2022] [Accepted: 09/07/2022] [Indexed: 11/17/2022]
Abstract
A clear understanding of the accuracy of diagnostic tests is essential for the management of infections such as visceral leishmaniasis in endemic areas. Using both published and unpublished datasets from field diagnostic trials of visceral leishmaniasis in the past ten years, we show the potential effects of unrecognised sources of bias including work-up bias, referral bias, and reference test bias on the results. We outline why these biases, which can occur in diagnostic studies of any disease, can go unrecognised despite adherence to current STARD and QUADAS-2 guidelines. Using these examples and referring to others seen in studies of bacterial and viral infections, we make specific recommendations on how these biases might be avoided through specific steps in study design, study reporting, and the locations where studies are conducted.
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Affiliation(s)
- Johan van Griensven
- Department of Clinical Sciences, Institute of Tropical Medicine, Antwerp, Belgium.
| | - Ermias Diro
- Department of Medicine, Division of General Internal Medicine, University of Washington, Seattle, WA, USA
| | - Cedric P Yansouni
- J D MacLean Centre for Tropical Diseases, McGill University Health Centre, Montreal, QC, Canada; Division of Infectious Diseases and Medical Microbiology, McGill University Health Centre, Montreal, QC, Canada
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12
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Ceccon DM, Amaral PHR, Andrade LM, da Silva MIN, Andrade LAF, Moraes TFS, Bagno FF, Rocha RP, de Almeida Marques DP, Ferreira GM, Lourenço AA, Ribeiro ÁL, Coelho-dos-Reis JGA, da Fonseca FG, Gonzalez JC. New, fast, and precise method of COVID-19 detection in nasopharyngeal and tracheal aspirate samples combining optical spectroscopy and machine learning. Braz J Microbiol 2023:10.1007/s42770-023-00923-5. [PMID: 36854899 PMCID: PMC9974055 DOI: 10.1007/s42770-023-00923-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Accepted: 02/07/2023] [Indexed: 03/02/2023] Open
Abstract
Fast, precise, and low-cost diagnostic testing to identify persons infected with SARS-CoV-2 virus is pivotal to control the global pandemic of COVID-19 that began in late 2019. The gold standard method of diagnostic recommended is the RT-qPCR test. However, this method is not universally available, and is time-consuming and requires specialized personnel, as well as sophisticated laboratories. Currently, machine learning is a useful predictive tool for biomedical applications, being able to classify data from diverse nature. Relying on the artificial intelligence learning process, spectroscopic data from nasopharyngeal swab and tracheal aspirate samples can be used to leverage characteristic patterns and nuances in healthy and infected body fluids, which allows to identify infection regardless of symptoms or any other clinical or laboratorial tests. Hence, when new measurements are performed on samples of unknown status and the corresponding data is submitted to such an algorithm, it will be possible to predict whether the source individual is infected or not. This work presents a new methodology for rapid and precise label-free diagnosing of SARS-CoV-2 infection in clinical samples, which combines spectroscopic data acquisition and analysis via artificial intelligence algorithms. Our results show an accuracy of 85% for detection of SARS-CoV-2 in nasopharyngeal swab samples collected from asymptomatic patients or with mild symptoms, as well as an accuracy of 97% in tracheal aspirate samples collected from critically ill COVID-19 patients under mechanical ventilation. Moreover, the acquisition and processing of the information is fast, simple, and cheaper than traditional approaches, suggesting this methodology as a promising tool for biomedical diagnosis vis-à-vis the emerging and re-emerging viral SARS-CoV-2 variant threats in the future.
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Affiliation(s)
- Denny M. Ceccon
- Departamento de Física, Universidade Federal de Minas Gerais, Avenida Antônio Carlos, Campus Pampulha 31270-901, Belo Horizonte, Minas Gerais 6627 Brazil
| | - Paulo Henrique R. Amaral
- Departamento de Física, Universidade Federal de Minas Gerais, Avenida Antônio Carlos, Campus Pampulha 31270-901, Belo Horizonte, Minas Gerais 6627 Brazil
| | - Lídia M. Andrade
- Departamento de Física, Universidade Federal de Minas Gerais, Avenida Antônio Carlos, Campus Pampulha 31270-901, Belo Horizonte, Minas Gerais 6627 Brazil
| | - Maria I. N. da Silva
- Departamento de Física, Universidade Federal de Minas Gerais, Avenida Antônio Carlos, Campus Pampulha 31270-901, Belo Horizonte, Minas Gerais 6627 Brazil
| | - Luis A. F. Andrade
- Centro de Tecnologia Em Vacinas, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Thais F. S. Moraes
- Laboratório de Virologia Básica E Aplicada, Departamento de Microbiologia, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Flavia F. Bagno
- Centro de Tecnologia Em Vacinas, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Raissa P. Rocha
- Centro de Tecnologia Em Vacinas, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | | | - Geovane Marques Ferreira
- Laboratório de Virologia Básica E Aplicada, Departamento de Microbiologia, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Alice Aparecida Lourenço
- Laboratório de Virologia Básica E Aplicada, Departamento de Microbiologia, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Ágata Lopes Ribeiro
- Laboratório de Virologia Básica E Aplicada, Departamento de Microbiologia, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Jordana G. A. Coelho-dos-Reis
- Laboratório de Virologia Básica E Aplicada, Departamento de Microbiologia, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Flavio G. da Fonseca
- Centro de Tecnologia Em Vacinas, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil ,Laboratório de Virologia Básica E Aplicada, Departamento de Microbiologia, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - J. C. Gonzalez
- Departamento de Física, Universidade Federal de Minas Gerais, Avenida Antônio Carlos, Campus Pampulha 31270-901, Belo Horizonte, Minas Gerais 6627 Brazil
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13
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He T, Liu H, Zhang Z, Li C, Zhou Y. Research on the Application of Artificial Intelligence in Public Health Management: Leveraging Artificial Intelligence to Improve COVID-19 CT Image Diagnosis. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:1158. [PMID: 36673913 PMCID: PMC9858906 DOI: 10.3390/ijerph20021158] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/19/2022] [Revised: 12/26/2022] [Accepted: 01/02/2023] [Indexed: 05/31/2023]
Abstract
Since the start of 2020, the outbreak of the Coronavirus disease (COVID-19) has been a global public health emergency, and it has caused unprecedented economic and social disaster. In order to improve the diagnosis efficiency of COVID-19 patients, a number of researchers have conducted extensive studies on applying artificial intelligence techniques to the analysis of COVID-19-related medical images. The automatic segmentation of lesions from computed tomography (CT) images using deep learning provides an important basis for the quantification and diagnosis of COVID-19 cases. For a deep learning-based CT diagnostic method, a few of accurate pixel-level labels are essential for the training process of a model. However, the translucent ground-glass area of the lesion usually leads to mislabeling while performing the manual labeling operation, which weakens the accuracy of the model. In this work, we propose a method for correcting rough labels; that is, to hierarchize these rough labels into precise ones by performing an analysis on the pixel distribution of the infected and normal areas in the lung. The proposed method corrects the incorrectly labeled pixels and enables the deep learning model to learn the infected degree of each infected pixel, with which an aiding system (named DLShelper) for COVID-19 CT image diagnosis using the hierarchical labels is also proposed. The DLShelper targets lesion segmentation from CT images, as well as the severity grading. The DLShelper assists medical staff in efficient diagnosis by providing rich auxiliary diagnostic information (including the severity grade, the proportions of the lesion and the visualization of the lesion area). A comprehensive experiment based on a public COVID-19 CT image dataset is also conducted, and the experimental results show that the DLShelper significantly improves the accuracy of segmentation for the lesion areas and also achieves a promising accuracy for the severity grading task.
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Affiliation(s)
- Tiancheng He
- Department of Political Party and State Governance, East China University of Political Science and Law, Shanghai 201620, China
| | - Hong Liu
- Department of Political Party and State Governance, East China University of Political Science and Law, Shanghai 201620, China
- Teacher Work Department of the Party Committee, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China
| | - Zhihao Zhang
- College of Computer Science and Technology, Shanghai University of Electric Power, Shanghai 200090, China
| | - Chao Li
- Department of Computer Science, Zhijiang College of Zhejiang University of Technology, Hangzhou 310024, China
| | - Youmei Zhou
- Department of Landscape Architecture, College of Architecture and Urban Planning, Tongji University, Shanghai 200092, China
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14
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Machado MAD, Silva RRE, Namias M, Lessa AS, Neves MCLC, Silva CTA, Oliveira DM, Reina TR, Lira AAB, Almeida LM, Zanchettin C, Netto EM. Multi-center Integrating Radiomics, Structured Reports, and Machine Learning Algorithms for Assisted Classification of COVID-19 in Lung Computed Tomography. J Med Biol Eng 2023; 43:156-162. [PMID: 37077697 PMCID: PMC9990550 DOI: 10.1007/s40846-023-00781-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Accepted: 02/16/2023] [Indexed: 04/21/2023]
Abstract
Purpose To evaluate the classification performance of structured report features, radiomics, and machine learning (ML) models to differentiate between Coronavirus Disease 2019 (COVID-19) and other types of pneumonia using chest computed tomography (CT) scans. Methods Sixty-four COVID-19 subjects and 64 subjects with non-COVID-19 pneumonia were selected. The data was split into two independent cohorts: one for the structured report, radiomic feature selection and model building (n = 73), and another for model validation (n = 55). Physicians performed readings with and without machine learning support. The model's sensitivity and specificity were calculated, and inter-rater reliability was assessed using Cohen's Kappa agreement coefficient. Results Physicians performed with mean sensitivity and specificity of 83.4 and 64.3%, respectively. When assisted with machine learning, the mean sensitivity and specificity increased to 87.1 and 91.1%, respectively. In addition, machine learning improved the inter-rater reliability from moderate to substantial. Conclusion Integrating structured reports and radiomics promises assisted classification of COVID-19 in CT chest scans.
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Affiliation(s)
- Marcos A. D. Machado
- Department of Radiology, Complexo Hospitalar Universitário Prof. Edgard Santos/ Ebserh, Universidade Federal da Bahia, Salvador, Bahia 40110-040 Brazil
- Radtec Serviços em Física Médica, Salvador, Bahia 40060-330 Brazil
| | - Ronnyldo R. E. Silva
- Radtec Serviços em Física Médica, Salvador, Bahia 40060-330 Brazil
- Department of Systems and Computing, Universidade Federal de Campina Grande, Campina Grande, Paraíba 58429-900 Brazil
| | - Mauro Namias
- Department of Medical Physics, Nuclear Diagnostic Center Foundation, C1417CVE Buenos Aires, Argentina
| | - Andreia S. Lessa
- Department of Radiology, Hospital Universitário Gaffrée e Guinle, Universidade do Rio de Janeiro (UNIRIO), Rio de Janeiro, 20270-004 Brazil
| | - Margarida C. L. C. Neves
- Department of Pneumology, Complexo Hospitalar Universitário Prof. Edgard Santos/ Ebserh, Universidade Federal da Bahia, Salvador, Bahia 40110-040 Brazil
| | - Carolina T. A. Silva
- Department of Radiology, Complexo Hospitalar Universitário Prof. Edgard Santos/ Ebserh, Universidade Federal da Bahia, Salvador, Bahia 40110-040 Brazil
| | - Danillo M. Oliveira
- Department of Radiology, Hospital Universitário Alcides Carneiro/ Ebserh, Universidade Federal de Campina Grande, Campina Grande, Paraíba 58400-398 Brazil
- Northeast Regional Nuclear Science Centre (CRCN-NE), Recife, Pernambuco 50840-545 Brazil
- Nuclear Energy Department, Universidade Federal de Pernambuco, Recife, Pernambuco 50740-540 Brazil
| | - Thamiris R. Reina
- Department of Radiology, Hospital Universitário da Universidade Federal de Juiz de Fora/ Ebserh, Universidade Federal de Juiz de Fora, Juiz de Fora, Minas Gerais 36038-330 Brazil
| | - Arquimedes A. B. Lira
- Department of Radiology, Hospital Universitário Alcides Carneiro/ Ebserh, Universidade Federal de Campina Grande, Campina Grande, Paraíba 58400-398 Brazil
| | - Leandro M. Almeida
- Centro de Informática, Universidade Federal de Pernambuco, Recife, Pernambuco 50720-001 Brazil
| | - Cleber Zanchettin
- Centro de Informática, Universidade Federal de Pernambuco, Recife, Pernambuco 50720-001 Brazil
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL 60208 USA
| | - Eduardo M. Netto
- Infectious Disease Research Laboratory, Complexo Hospitalar Universitário Prof. Edgard Santos/ Ebserh, Universidade Federal da Bahia, Salvador, Bahia 40110-040 Brazil
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15
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Tanba C, Habib R, Chen T, Saleh N. Cavitary lung lesion as sequelae of mild COVID-19 in a patient with HIV. CLINICAL INFECTION IN PRACTICE 2023; 17:100218. [PMID: 36687139 PMCID: PMC9846880 DOI: 10.1016/j.clinpr.2023.100218] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Revised: 12/29/2022] [Accepted: 01/13/2023] [Indexed: 01/19/2023] Open
Abstract
Cavitary lung lesions involve a large spectrum of acute to chronic infections, chronic systemic diseases, and malignancies. During the COVID-19 pandemic, we have seen an increase in lung cavitation, mainly secondary to bacterial infection however, there have been limited reports of these lesions with mild COVID-19 disease. There has been an association between severe COVID-19 infection and secondary bacterial/fungal infections and cavitary lung lesions. We report the first case of a 32-year-old man with well-controlled HIV who presented with cough and fever from what appeared to be a cavitary lesion as a sequela of his recent COVID-19 infection.
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Affiliation(s)
- Carl Tanba
- Department of Internal Medicine, Medstar Union Memorial Hospital, Baltimore, United States,Corresponding author at: 707 president street, Baltimore, MD 21202, United States
| | - Ruba Habib
- Department of Internal Medicine, Tishreen University, Latakia, Syria
| | - Terina Chen
- Department of Pathology, Medstar Union Memorial Hospital, Baltimore, United States
| | - Nahar Saleh
- Department of Internal Medicine, Medstar Union Memorial Hospital, Baltimore, United States
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16
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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: 0.5] [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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17
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Kusmirek JE, Meyer CA. High-Resolution Computed Tomography of Cystic Lung Disease. Semin Respir Crit Care Med 2022; 43:792-808. [PMID: 36252611 DOI: 10.1055/s-0042-1755565] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
The cystic lung diseases (CLD) are characterized by the presence of multiple, thin-walled, air-filled spaces in the pulmonary parenchyma. Cyst formation may occur with congenital, autoimmune, inflammatory, infectious, or neoplastic processes. Recognition of cyst mimics such as emphysema and bronchiectasis is important to prevent diagnostic confusion and unnecessary evaluation. Chest CT can be diagnostic or may guide the workup based on cyst number, distribution, morphology, and associated lung, and extrapulmonary findings. Diffuse CLD (DCLDs) are often considered those presenting with 10 or more cysts. The more commonly encountered DCLDs include lymphangioleiomyomatosis, pulmonary Langerhans' cell histiocytosis, lymphoid interstitial pneumonia, Birt-Hogg-Dubé syndrome, and amyloidosis/light chain deposition disease.
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Affiliation(s)
- Joanna E Kusmirek
- Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin
| | - Cristopher A Meyer
- Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin
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18
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Pneumonia Frequency and Severity in Patients With Symptomatic COVID-19: Impact of mRNA and Adenovirus Vector Vaccines. AJR Am J Roentgenol 2022; 219:752-761. [PMID: 35642761 DOI: 10.2214/ajr.22.27843] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
BACKGROUND. Additional evidence of the role of COVID-19 vaccination in reducing pneumonia frequency and severity in the setting of breakthrough infection could help combat ongoing vaccine hesitancy. OBJECTIVE. The purpose of this article was to compare the frequency and severity of pneumonia on chest CT in patients with confirmed COVID-19 between patients who are unvaccinated and those who are fully vaccinated by messenger RNA (mRNA) or adenovirus vector vaccines. METHODS. This retrospective single-center study included 467 patients (250 men, 217 women; mean age, 65 ± 17 [SD] years) who underwent chest CT between December 15, 2021, and February 18, 2022, during hospitalization for symptomatic COVID-19, confirmed by reverse transcriptase-polymerase chain reaction assay. A total of 216 patients were unvaccinated, and 167 and 84 patients were fully vaccinated (defined as receipt of the second dose at least 14 days before COVID-19 diagnosis) by the BNT162b2 mRNA vaccine or the ChAdOx1-S adenovirus vector vaccine, respectively. Semiquantitative CT severity scores (CT-SS; 0-25 scale) were determined; CT-SS of 0 indicated absence of pneumonia. Presence of bilateral involvement was assessed in patients with pneumonia. Associations were explored between vaccination status and CT findings. RESULTS. The frequency of the absence of pneumonia was 15% (32/216) in unvaccinated patients, 29% (24/84) in patients fully vaccinated with ChAdOx1-S vaccine, and 51% (85/167) in patients fully vaccinated with BNT162b2 vaccine (unvaccinated and ChAdOx1-S vs BNT162b2: p < .001; unvaccinated vs ChAdOx1-S: p = .08). Mean CT-SS was significantly higher in unvaccinated patients (9.7 ± 6.1) than in patients fully vaccinated with BNT162b2 (5.2 ± 6.1) or ChAdOx1-S (6.2 ± 5.9) vaccine (both p < .001). Full vaccination was significantly associated with CT-SS independent of patient age and sex (estimate = -4.46; p < .001). Frequency of bilateral lung involvement was significantly higher in unvaccinated patients (158/184, 86%) and in patients fully vaccinated with ChAdOx1-S vaccine (54/60, 90%) than in patients fully vaccinated with BNT162b2 vaccine (47/82, 57%) (both p < .001). CONCLUSION. Pneumonia frequency and severity were lower in patients with full vaccination by mRNA and adenovirus vector vaccines who experienced breakthrough infections in comparison with unvaccinated patients. CLINICAL IMPACT. The visual observation by radiologic imaging of the protective effect of vaccination on lung injury in patients with breakthrough infections provides additional evidence supporting the clinical benefit of vaccination.
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Sarcoidosis and COVID-19: At the Cross-Road between Immunopathology and Clinical Manifestation. Biomedicines 2022; 10:biomedicines10102525. [PMID: 36289785 PMCID: PMC9599235 DOI: 10.3390/biomedicines10102525] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2022] [Revised: 09/29/2022] [Accepted: 09/30/2022] [Indexed: 02/08/2023] Open
Abstract
Coronavirus disease 2019 (COVID-19) has been associated with dysregulation of the immune system featuring inappropriate immune responses, exacerbation of inflammatory responses, and multiple organ dysfunction syndrome in patients with severe disease. Sarcoidosis, also known as Besnier-Boeck-Schaumann disease, is an idiopathic granulomatous multisystem disease characterized by dense epithelioid non-necrotizing lesions with varying degrees of lymphocytic inflammation. These two diseases have similar clinical manifestations and may influence each other at multiple levels, eventually affecting their clinical courses and prognosis. Notably, sarcoidosis patients are at high risk of severe COVID-19 pneumonia because of the underlying lung disease and chronic immunosuppressive treatment. In this narrative review, we will discuss interactions between sarcoidosis and COVID-19 in terms of clinical manifestations, treatment, and pathogenesis, including the role of the dysregulated renin-angiotensin system, altered immune responses involving increased cytokine levels and immune system hyperactivation, and cellular death pathways.
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20
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Polat G, Özdemir Ö, Ermin S, Unat DS, Şahin GV, Türk MA, Güldaval F, Susam S, Kıraklı C. Factors Affecting the Risk of Interstitial Lung Disease Development in Hospitalized Patients With COVID-19 Pneumonia. Respir Care 2022; 67:1272-1281. [PMID: 35790396 PMCID: PMC9994322 DOI: 10.4187/respcare.09816] [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] [Indexed: 11/05/2022]
Abstract
BACKGROUND Coronavirus disease 2019 (COVID-19) related chronic lung changes secondary to severe disease have become well known. The aim of this study was to determine the risk factors that affect the development of interstitial lung disease in subjects with COVID-19 pneumonia who were hospitalized. METHODS Patients hospitalized with COVID-19 pneumonia between June 2020 and March 2021 were retrospectively analyzed. Smoking histories, comorbidities, reverse transcriptase polymerase chain reaction test results, laboratory parameters at the time of the diagnosis, oxygen support, the use of corticosteroids with dosage and duration data, the need for ICU care were recorded. High-resolution computed tomographies (HRCT) were obtained for study population in their 3-6 months follow-up visit. The subjects were classified as having residual parenchymal lung disease if a follow-up HRCT revealed parenchymal abnormalities except pure ground-glass opacities (the residual disease group). The control group consisted of the subjects with normal chest radiograph or HRCT in their follow-up visit or the presence of pure ground-glass opacities. Two groups were compared for their demographic and clinical abnormalities, laboratory parameters, treatment regimens, and the need for ICU care. RESULTS The study included 446 subjects. The mean ± SD age was 58.4 ± 13.87 years, with 257 men (57.6%). Although 55 subjects had normal HRCT features on their follow-up HRCT, 157 had abnormal lung parenchymal findings. Univariate logistic regression analysis revealed statistically significant results for age, sex, corticosteroid treatment, and the need for ICU care for predicting interstitial lung disease development (P < .001, P = .003, P < .001, and P < .001, respectively). Also, the residual disease group had significantly higher leukocyte and neutrophil counts and lower lymphocyte counts (P < .001, P < .001, P = .004, respectively). Correlated with these findings, neutrophil-to-lymphocyte ratios and platelet-to-lymphocyte ratios were significantly higher in the residual disease group (P < .001 and P = .008, respectively). CONCLUSIONS Residual parenchymal disease was observed 3-6 months after discharge in one third of the subjects hospitalized with COVID-19 pneumonia. It was observed that interstitial lung disease developed more frequently in older men and in those subjects with more-severe disease parameters.
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Affiliation(s)
- Gülru Polat
- Department of Chest Diseases, University of Health Sciences, Dr Suat Seren Chest Diseases and Surgery Training and Research Hospital, Konak, Izmir, Turkey.
| | - Özer Özdemir
- Department of Chest Diseases, University of Health Sciences, Dr Suat Seren Chest Diseases and Surgery Training and Research Hospital, Konak, Izmir, Turkey
| | - Sinem Ermin
- Department of Chest Diseases, University of Health Sciences, Dr Suat Seren Chest Diseases and Surgery Training and Research Hospital, Konak, Izmir, Turkey
| | - Damla Serçe Unat
- Department of Chest Diseases, University of Health Sciences, Dr Suat Seren Chest Diseases and Surgery Training and Research Hospital, Konak, Izmir, Turkey
| | - Görkem Vayisoğlu Şahin
- Department of Chest Diseases, University of Health Sciences, Dr Suat Seren Chest Diseases and Surgery Training and Research Hospital, Konak, Izmir, Turkey
| | - Merve Ayık Türk
- Department of Chest Diseases, Bozyaka Training and Research Hospital, Karabağlar, Izmir, Turkey
| | - Filiz Güldaval
- Department of Chest Diseases, University of Health Sciences, Dr Suat Seren Chest Diseases and Surgery Training and Research Hospital, Konak, Izmir, Turkey
| | - Seher Susam
- Department of Radiology, University of Health Sciences, Dr Suat Seren Chest Diseases and Surgery Training and Research Hospital, Konak, Izmir, Turkey
| | - Cenk Kıraklı
- Department of Chest Diseases, University of Health Sciences, Dr Suat Seren Chest Diseases and Surgery Training and Research Hospital, Konak, Izmir, Turkey
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21
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He D, Fu C, Ning M, Hu X, Li S, Chen Y. Biofilms possibly harbor occult SARS-CoV-2 may explain lung cavity, re-positive and long-term positive results. Front Cell Infect Microbiol 2022; 12:971933. [PMID: 36250053 PMCID: PMC9554432 DOI: 10.3389/fcimb.2022.971933] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Accepted: 09/14/2022] [Indexed: 01/08/2023] Open
Abstract
During the COVID-19 pandemic, there have been an increasing number of COVID-19 patients with cavitary or cystic lung lesions, re-positive or long-term positive nucleic acid tests, but the mechanism is still unclear. Lung cavities may appear at long time interval from initial onset of coronavirus infection, generally during the absorption phase of the disease. The main histopathological characteristic is diffuse alveolar damage and may have more severe symptoms after initial recovery from COVID-19 and an increased mortality rate. There are many possible etiologies of pulmonary cavities in COVID-19 patients and we hypothesize that occult SARS-CoV-2, in the form of biofilm, is harbored in the airway lacuna with other pathogenic microorganisms, which may be the cause of pulmonary cavities and repeated and long-term positive nucleic acid tests.
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Affiliation(s)
- Daqian He
- Department of Thoracic Surgery I, The Third Affiliated Hospital of Kunming Medical University (Yunnan Cancer Hospital, Yunnan Cancer Center), Kunming, China
| | - Chaojiang Fu
- Emergency Department (Outpatient Chemotherapy Center), The Third Affiliated Hospital of Kunming Medical University (Yunnan Cancer Hospital, Yunnan Cancer Center), Kunming, China
| | - Mingjie Ning
- Department of Thoracic Surgery I, The Third Affiliated Hospital of Kunming Medical University (Yunnan Cancer Hospital, Yunnan Cancer Center), Kunming, China
| | - Xianglin Hu
- Department of Thoracic Surgery I, The Third Affiliated Hospital of Kunming Medical University (Yunnan Cancer Hospital, Yunnan Cancer Center), Kunming, China
| | - Shanshan Li
- Department of Anesthesiology, The Third Affiliated Hospital of Kunming Medical University (Yunnan Cancer Hospital, Yunnan Cancer Center), Kunming, China
- *Correspondence: Ying Chen, ; Shanshan Li,
| | - Ying Chen
- Department of Thoracic Surgery I, The Third Affiliated Hospital of Kunming Medical University (Yunnan Cancer Hospital, Yunnan Cancer Center), Kunming, China
- *Correspondence: Ying Chen, ; Shanshan Li,
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22
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Atypical pneumonia due to SARS-CoV-2: Clinical differentiation using the JRS pneumonia guidelines. Respir Investig 2022; 60:735-737. [PMID: 36115794 PMCID: PMC9458699 DOI: 10.1016/j.resinv.2022.08.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Revised: 08/13/2022] [Accepted: 08/15/2022] [Indexed: 11/24/2022]
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23
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Wu X, Zhang Z, Guo L, Chen H, Luo Q, Jin B, Gu W, Lu F, Chen J. FAM: focal attention module for lesion segmentation of COVID-19 CT images. JOURNAL OF REAL-TIME IMAGE PROCESSING 2022; 19:1091-1104. [PMID: 36091622 PMCID: PMC9441194 DOI: 10.1007/s11554-022-01249-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/15/2022] [Accepted: 08/12/2022] [Indexed: 05/27/2023]
Abstract
The novel coronavirus pneumonia (COVID-19) is the world's most serious public health crisis, posing a serious threat to public health. In clinical practice, automatic segmentation of the lesion from computed tomography (CT) images using deep learning methods provides an promising tool for identifying and diagnosing COVID-19. To improve the accuracy of image segmentation, an attention mechanism is adopted to highlight important features. However, existing attention methods are of weak performance or negative impact to the accuracy of convolutional neural networks (CNNs) due to various reasons (e.g. low contrast of the boundary between the lesion and the surrounding, the image noise). To address this issue, we propose a novel focal attention module (FAM) for lesion segmentation of CT images. FAM contains a channel attention module and a spatial attention module. In the spatial attention module, it first generates rough spatial attention, a shape prior of the lesion region obtained from the CT image using median filtering and distance transformation. The rough spatial attention is then input into two 7 × 7 convolution layers for correction, achieving refined spatial attention on the lesion region. FAM is individually integrated with six state-of-the-art segmentation networks (e.g. UNet, DeepLabV3+, etc.), and then we validated these six combinations on the public dataset including COVID-19 CT images. The results show that FAM improve the Dice Similarity Coefficient (DSC) of CNNs by 2%, and reduced the number of false negatives (FN) and false positives (FP) up to 17.6%, which are significantly higher than that using other attention modules such as CBAM and SENet. Furthermore, FAM significantly improve the convergence speed of the model training and achieve better real-time performance. The codes are available at GitHub (https://github.com/RobotvisionLab/FAM.git).
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Affiliation(s)
- Xiaoxin Wu
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang China
| | - Zhihao Zhang
- College of Computer Science and Technology, Shanghai University of Electric Power, Shanghai, China
| | - Lingling Guo
- College of Chemical Engineering, Zhejiang University of Technology, Hangzhou, Zhejiang China
| | - Hui Chen
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang China
| | - Qiaojie Luo
- School of Stomatology, Stomatology Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang China
| | - Bei Jin
- Department of Oral and Maxillofacial Surgery, Taizhou Hospital, Wenzhou Medical University, Taizhou, Zhejiang China
| | - Weiyan Gu
- Department of Oral and Maxillofacial Surgery, Taizhou Hospital, Wenzhou Medical University, Taizhou, Zhejiang China
| | - Fangfang Lu
- College of Computer Science and Technology, Shanghai University of Electric Power, Shanghai, China
| | - Jingjing Chen
- Zhejiang University City College, Hangzhou, Zhejiang China
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Xiao F, Sun R, Sun W, Xu D, Lan L, Li H, Liu H, Xu H. Prediction of potential severe COVID-19 patients based on CT radiomics: a retrospective study. Med Phys 2022; 49:5886-5898. [PMID: 35837868 PMCID: PMC9349830 DOI: 10.1002/mp.15841] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Revised: 05/26/2022] [Accepted: 06/20/2022] [Indexed: 01/08/2023] Open
Abstract
PURPOSE Coronavirus disease 2019 (COVID-19) is a recently declared worldwide pandemic. Triaging of patients into severe and non-severe could further help in targeted management. "Potential severe patients" is a category of patients who did not have severe symptoms at their initial diagnosis, but eventually progressed to be severe patients and are easily overlooked in the early stage. This work aimed to develop and evaluate a CT-based radiomics signature for the prediction of these potential severe COVID-19 patients. METHODS 150 COVID-19 patients were enrolled and randomly divided into cross-validation and independent test sets. First, Their clinical characteristics were screened using the univariate and multivariate logistic regression step by step. Then, radiomics features were extracted from the lesions on their chest CT images. Subsequently, the Inter- and intra-class correlation coefficients (ICC) analysis, minimum-redundancy maximum-relevance (mRMR) selection and the least absolute shrinkage and selection operator (LASSO) algorithm were used step by step for feature selection and construction of a radiomics signature. Finally, the screened clinical risk factors and constructed radiomics signature were combined for the Combined model and Radiomics+Clinics nomogram construction. The predictive performance of the Radiomics and Combined models were evaluated and compared using receiver operating characteristic curve (ROC) analysis, Hosmer-Lemeshow test and Delong test. RESULTS Clinical characteristics analysis resulted in the screening of five clinical risk factors. The combination of ICC, mRMR and LASSO methods resulted in the selection of ten radiomics features, which made up the radiomics signature. The differences in the radiomics signature between the potential severe and non-severe groups in cross-validation set and test sets were both p < 0.001. All Radiomics and Combined models showed a very good predictive performance with the accuracy and AUC of nearly or above 0.9. Additionally, we found no significant difference in the predictive performance between these two models. CONCLUSIONS A CT-based radiomics signature for the prediction of potential severe COVID-19 patients was constructed and evaluated. Constructed Radiomics and Combined model showed good feasibility and accuracy. The Radiomics+Clinical nomogram, acted as a useful tool, may assist clinicians to better identify potential severe cases to target their management in the COVID-19 pandemic prevention and control. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Feng Xiao
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Rongqing Sun
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Wenbo Sun
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Dan Xu
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Lan Lan
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Huan Li
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | | | - Haibo Xu
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, China
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25
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Ravina, Kumar A, Manjeet, Twinkle, Subodh, Narang J, Mohan H. Analytical performances of different diagnostic methods for SARS-CoV-2 virus - A review. SENSORS INTERNATIONAL 2022; 3:100197. [PMID: 35935464 PMCID: PMC9338831 DOI: 10.1016/j.sintl.2022.100197] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Revised: 07/19/2022] [Accepted: 07/19/2022] [Indexed: 12/01/2022] Open
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26
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Moeinafshar A, Yazdanpanah N, Rezaei N. Immune-based therapeutic approaches in COVID-19. Biomed Pharmacother 2022; 151:113107. [PMID: 35594701 PMCID: PMC9108029 DOI: 10.1016/j.biopha.2022.113107] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Revised: 05/02/2022] [Accepted: 05/10/2022] [Indexed: 02/01/2023] Open
Abstract
Coronavirus disease 2019 (COVID-19) is a viral disease caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), a member of the Coronaviridae family. On March 11, 2020 the World Health Organization (WHO) has named the newly emerged rapidly-spreading epidemic as a pandemic. Besides the risk-reduction measures such as physical and social distancing and vaccination, a wide range of treatment modalities have been developed; aiming to fight the disease. The immune system is known as a double-edged sword in COVID-19 pathogenesis, with respect to its role in eliminating the pathogen and in inducing complications such as cytokine storm syndrome. Hence, immune-based therapeutic approaches have become an interesting field of COVID-19 research, including corticosteroids, intravenous immunoglobulins (IVIG), interferon therapy, and more COVID-19-specific approaches such as anti-SARS-CoV-2-monoclonal antibodies. Herein, we did a comprehensive review on immune-based therapeutic approaches for COVID-19. DATA AVAILABILITY STATEMENT: Not applicable.
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Affiliation(s)
- Aysan Moeinafshar
- School of Medicine, Tehran University of Medical Sciences, Tehran, Iran,Network of Immunity in Infection, Malignancy and Autoimmunity (NIIMA), Universal Scientific Education and Research Network (USERN), Tehran, Iran
| | - Niloufar Yazdanpanah
- School of Medicine, Tehran University of Medical Sciences, Tehran, Iran,Research Center for Immunodeficiencies, Children's Medical Center, Tehran University of Medical Sciences, Tehran, Iran,Network of Immunity in Infection, Malignancy and Autoimmunity (NIIMA), Universal Scientific Education and Research Network (USERN), Tehran, Iran
| | - Nima Rezaei
- Research Center for Immunodeficiencies, Children's Medical Center, Tehran University of Medical Sciences, Tehran, Iran,Network of Immunity in Infection, Malignancy and Autoimmunity (NIIMA), Universal Scientific Education and Research Network (USERN), Tehran, Iran,Department of Immunology, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran,Correspondence to: Children's Medical Center Hospital, Dr. Qarib St, Keshavarz Blvd, Tehran 14194, Iran
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27
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Tsakok MT, Watson RA, Saujani SJ, Kong M, Xie C, Peschl H, Wing L, MacLeod FK, Shine B, Talbot NP, Benamore RE, Eyre DW, Gleeson F. Reduction in Chest CT Severity and Improved Hospital Outcomes in SARS-CoV-2 Omicron Compared with Delta Variant Infection. Radiology 2022; 306:261-269. [PMID: 35727150 PMCID: PMC9272784 DOI: 10.1148/radiol.220533] [Citation(s) in RCA: 53] [Impact Index Per Article: 26.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
Background The SARS-Cov-2 Omicron variant demonstrates rapid spread but reduced disease severity. Studies evaluating lung imaging findings of Omicron infection versus non-Omicron infection remain lacking. Purpose To compare the Omicron variant with the SARS-CoV-2 Delta variant according to their chest CT radiologic pattern, biochemical parameters, clinical severity, and hospital outcomes after adjusting for vaccination status. Materials and Methods This retrospective study included hospitalized adult patients with reverse transcriptase-polymerase chain reaction test results positive for SARS-CoV-2, with CT pulmonary angiography performed within 7 days of admission between December 1, 2021, and January 14, 2022. Multiple readers performed blinded radiologic analyses that included RSNA CT classification, chest CT severity score (CTSS) (range, 0 [least severe] to 25 [most severe]), and CT imaging features, including bronchial wall thickening. Results A total of 106 patients (Delta group, n = 66; Omicron group, n = 40) were evaluated (overall mean age, 58 years ± 18 [SD]; 58 men). In the Omicron group, 37% of CT pulmonary angiograms (15 of 40 patients) were categorized as normal compared with 15% (10 of 66 patients) of angiograms in the Delta group (P = .016). A generalized linear model was used to control for confounding variables, including vaccination status, and Omicron infection was associated with a CTSS that was 7.2 points lower than that associated with Delta infection (β = -7.2; 95% CI: -9.9, -4.5; P < .001). Bronchial wall thickening was more common with Omicron infection than with Delta infection (odds ratio [OR], 2.4; 95% CI: 1.01, 5.92; P = .04). A booster shot was associated with a protective effect for chest infection (median CTSS, 5; IQR, 0-11) when compared with unvaccinated individuals (median CTSS, 11; IQR, 7.5-14.0) (P = .03). The Delta variant was associated with a higher OR of severe disease (OR, 4.6; 95% CI: 1.2, 26; P = .01) and admission to a critical care unit (OR, 7.0; 95% CI: 1.5, 66; P = .004) when compared with the Omicron variant. Conclusion The SARS-CoV-2 Omicron variant was associated with fewer and less severe changes on chest CT images compared with the Delta variant. Patients with Omicron infection had greater frequency of bronchial wall thickening but less severe disease and improved hospital outcomes when compared with patients with Delta infection. © RSNA, 2022 Online supplemental material is available for this article.
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Affiliation(s)
- Maria T. Tsakok
- From the Department of Radiology, Oxford University Hospitals NHS Foundation Trust, Headley Way, Oxford OX3 9DU, United Kingdom (M.T.T., R.A.W., S.J.S., M.K., C.X., H.P., L.W., F.K.M., B.S., N.P.T., R.E.B., D.W.E., F.G.); and Weatherall Institute of Molecular Medicine (R.A.W.), Department of Oncology (R.A.W.), Department of Physiology, Anatomy and Genetics (N.P.T.), and Big Data Institute, Nuffield Department of Population Health (D.W.E.), University of Oxford, Oxford, United Kingdom
| | - Robert A. Watson
- From the Department of Radiology, Oxford University Hospitals NHS Foundation Trust, Headley Way, Oxford OX3 9DU, United Kingdom (M.T.T., R.A.W., S.J.S., M.K., C.X., H.P., L.W., F.K.M., B.S., N.P.T., R.E.B., D.W.E., F.G.); and Weatherall Institute of Molecular Medicine (R.A.W.), Department of Oncology (R.A.W.), Department of Physiology, Anatomy and Genetics (N.P.T.), and Big Data Institute, Nuffield Department of Population Health (D.W.E.), University of Oxford, Oxford, United Kingdom
| | - Shyamal J. Saujani
- From the Department of Radiology, Oxford University Hospitals NHS Foundation Trust, Headley Way, Oxford OX3 9DU, United Kingdom (M.T.T., R.A.W., S.J.S., M.K., C.X., H.P., L.W., F.K.M., B.S., N.P.T., R.E.B., D.W.E., F.G.); and Weatherall Institute of Molecular Medicine (R.A.W.), Department of Oncology (R.A.W.), Department of Physiology, Anatomy and Genetics (N.P.T.), and Big Data Institute, Nuffield Department of Population Health (D.W.E.), University of Oxford, Oxford, United Kingdom
| | - Mark Kong
- From the Department of Radiology, Oxford University Hospitals NHS Foundation Trust, Headley Way, Oxford OX3 9DU, United Kingdom (M.T.T., R.A.W., S.J.S., M.K., C.X., H.P., L.W., F.K.M., B.S., N.P.T., R.E.B., D.W.E., F.G.); and Weatherall Institute of Molecular Medicine (R.A.W.), Department of Oncology (R.A.W.), Department of Physiology, Anatomy and Genetics (N.P.T.), and Big Data Institute, Nuffield Department of Population Health (D.W.E.), University of Oxford, Oxford, United Kingdom
| | - Cheng Xie
- From the Department of Radiology, Oxford University Hospitals NHS Foundation Trust, Headley Way, Oxford OX3 9DU, United Kingdom (M.T.T., R.A.W., S.J.S., M.K., C.X., H.P., L.W., F.K.M., B.S., N.P.T., R.E.B., D.W.E., F.G.); and Weatherall Institute of Molecular Medicine (R.A.W.), Department of Oncology (R.A.W.), Department of Physiology, Anatomy and Genetics (N.P.T.), and Big Data Institute, Nuffield Department of Population Health (D.W.E.), University of Oxford, Oxford, United Kingdom
| | - Heiko Peschl
- From the Department of Radiology, Oxford University Hospitals NHS Foundation Trust, Headley Way, Oxford OX3 9DU, United Kingdom (M.T.T., R.A.W., S.J.S., M.K., C.X., H.P., L.W., F.K.M., B.S., N.P.T., R.E.B., D.W.E., F.G.); and Weatherall Institute of Molecular Medicine (R.A.W.), Department of Oncology (R.A.W.), Department of Physiology, Anatomy and Genetics (N.P.T.), and Big Data Institute, Nuffield Department of Population Health (D.W.E.), University of Oxford, Oxford, United Kingdom
| | - Louise Wing
- From the Department of Radiology, Oxford University Hospitals NHS Foundation Trust, Headley Way, Oxford OX3 9DU, United Kingdom (M.T.T., R.A.W., S.J.S., M.K., C.X., H.P., L.W., F.K.M., B.S., N.P.T., R.E.B., D.W.E., F.G.); and Weatherall Institute of Molecular Medicine (R.A.W.), Department of Oncology (R.A.W.), Department of Physiology, Anatomy and Genetics (N.P.T.), and Big Data Institute, Nuffield Department of Population Health (D.W.E.), University of Oxford, Oxford, United Kingdom
| | - Fiona K. MacLeod
- From the Department of Radiology, Oxford University Hospitals NHS Foundation Trust, Headley Way, Oxford OX3 9DU, United Kingdom (M.T.T., R.A.W., S.J.S., M.K., C.X., H.P., L.W., F.K.M., B.S., N.P.T., R.E.B., D.W.E., F.G.); and Weatherall Institute of Molecular Medicine (R.A.W.), Department of Oncology (R.A.W.), Department of Physiology, Anatomy and Genetics (N.P.T.), and Big Data Institute, Nuffield Department of Population Health (D.W.E.), University of Oxford, Oxford, United Kingdom
| | - Brian Shine
- From the Department of Radiology, Oxford University Hospitals NHS Foundation Trust, Headley Way, Oxford OX3 9DU, United Kingdom (M.T.T., R.A.W., S.J.S., M.K., C.X., H.P., L.W., F.K.M., B.S., N.P.T., R.E.B., D.W.E., F.G.); and Weatherall Institute of Molecular Medicine (R.A.W.), Department of Oncology (R.A.W.), Department of Physiology, Anatomy and Genetics (N.P.T.), and Big Data Institute, Nuffield Department of Population Health (D.W.E.), University of Oxford, Oxford, United Kingdom
| | - Nicholas P. Talbot
- From the Department of Radiology, Oxford University Hospitals NHS Foundation Trust, Headley Way, Oxford OX3 9DU, United Kingdom (M.T.T., R.A.W., S.J.S., M.K., C.X., H.P., L.W., F.K.M., B.S., N.P.T., R.E.B., D.W.E., F.G.); and Weatherall Institute of Molecular Medicine (R.A.W.), Department of Oncology (R.A.W.), Department of Physiology, Anatomy and Genetics (N.P.T.), and Big Data Institute, Nuffield Department of Population Health (D.W.E.), University of Oxford, Oxford, United Kingdom
| | - Rachel E. Benamore
- From the Department of Radiology, Oxford University Hospitals NHS Foundation Trust, Headley Way, Oxford OX3 9DU, United Kingdom (M.T.T., R.A.W., S.J.S., M.K., C.X., H.P., L.W., F.K.M., B.S., N.P.T., R.E.B., D.W.E., F.G.); and Weatherall Institute of Molecular Medicine (R.A.W.), Department of Oncology (R.A.W.), Department of Physiology, Anatomy and Genetics (N.P.T.), and Big Data Institute, Nuffield Department of Population Health (D.W.E.), University of Oxford, Oxford, United Kingdom
| | - David W. Eyre
- From the Department of Radiology, Oxford University Hospitals NHS Foundation Trust, Headley Way, Oxford OX3 9DU, United Kingdom (M.T.T., R.A.W., S.J.S., M.K., C.X., H.P., L.W., F.K.M., B.S., N.P.T., R.E.B., D.W.E., F.G.); and Weatherall Institute of Molecular Medicine (R.A.W.), Department of Oncology (R.A.W.), Department of Physiology, Anatomy and Genetics (N.P.T.), and Big Data Institute, Nuffield Department of Population Health (D.W.E.), University of Oxford, Oxford, United Kingdom
| | - Fergus Gleeson
- From the Department of Radiology, Oxford University Hospitals NHS Foundation Trust, Headley Way, Oxford OX3 9DU, United Kingdom (M.T.T., R.A.W., S.J.S., M.K., C.X., H.P., L.W., F.K.M., B.S., N.P.T., R.E.B., D.W.E., F.G.); and Weatherall Institute of Molecular Medicine (R.A.W.), Department of Oncology (R.A.W.), Department of Physiology, Anatomy and Genetics (N.P.T.), and Big Data Institute, Nuffield Department of Population Health (D.W.E.), University of Oxford, Oxford, United Kingdom
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Miyashita N, Nakamori Y, Ogata M, Fukuda N, Yamura A, Ishiura Y, Nomura S. Changes in diagnostic usefulness of the JRS scoring system in COVID-19 pneumonia by SARS-CoV-2 vaccination. J Infect Chemother 2022; 28:1375-1379. [PMID: 35718262 PMCID: PMC9212383 DOI: 10.1016/j.jiac.2022.06.007] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Revised: 05/19/2022] [Accepted: 06/09/2022] [Indexed: 12/23/2022]
Abstract
Introduction The Japanese Respiratory Society (JRS) scoring system is a useful tool for the rapid presumptive diagnosis of atypical pneumonia in non-elderly (aged <60 years) patients. As SARS-CoV-2 vaccination progresses, COVID-19 in elderly people has markedly reduced. We investigated changes in diagnostic usefulness of the JRS scoring system in COVID-19 pneumonia between the Delta variant group (vaccination period) and non-Delta variant group (before the vaccination period). Methods This study was conducted at five institutions and assessed a total of 1121 patients with COVID-19 pneumonia (298 had the Delta variant). During the vaccination period, the Delta variant has spread and replaced the Alfa variant. We evaluated the vaccination period as the Delta variant group. Results Among the six parameters of the JRS scoring system, matching rates of two parameters were higher in the Delta variant group than the non-Delta variant group (pre-vaccination period): age <60 years (77.5% vs 42.2%, P < 0.0001) and no or minor comorbid illness (69.1% vs 57.8%, p = 0.0007). The sensitivity of the diagnosis of atypical pneumonia in patients with COVID-19 pneumonia was significantly higher in the Delta variant group compared with the non-Delta variant group (80.2% vs 58.3%, p < 0.0001). When the diagnostic sensitivity was analyzed for different ages, the diagnostic sensitivities for the Delta variant and non-Delta variant groups were 92.6% and 95.5% for non-elderly patients and 39.1% and 32.5% for elderly patients, respectively. Conclusions Our results demonstrated that the JRS scoring system is a useful tool for distinguishing between COVID-19 pneumonia and bacterial pneumonia in the COVID-19 vaccination period, but not before the vaccination period.
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Affiliation(s)
- Naoyuki Miyashita
- First Department of Internal Medicine, Division of Respiratory Medicine, Infectious Disease and Allergology, Kansai Medical University, Japan.
| | - Yasushi Nakamori
- Department of Emergency Medicine, Kansai Medical University Medical Center, Japan
| | - Makoto Ogata
- First Department of Internal Medicine, Division of Respiratory Medicine, Infectious Disease and Allergology, Kansai Medical University, Japan
| | - Naoki Fukuda
- First Department of Internal Medicine, Division of Respiratory Medicine, Infectious Disease and Allergology, Kansai Medical University, Japan
| | - Akihisa Yamura
- First Department of Internal Medicine, Division of Respiratory Medicine, Infectious Disease and Allergology, Kansai Medical University, Japan
| | - Yoshihisa Ishiura
- First Department of Internal Medicine, Division of Respiratory Medicine, Oncology and Allergology, Kansai Medical University Medical Center, Japan
| | - Shosaku Nomura
- First Department of Internal Medicine, Division of Respiratory Medicine, Infectious Disease and Allergology, Kansai Medical University, Japan
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He D, Sun C, Shang Z, Yang Y, Chen Y. Multiple small pneumatoceles as a complication of a SARS-CoV-2 infection in a child from Myanmar. J Travel Med 2022; 29:6534725. [PMID: 35234878 PMCID: PMC8903482 DOI: 10.1093/jtm/taac026] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Revised: 02/09/2022] [Accepted: 02/14/2022] [Indexed: 11/14/2022]
Abstract
During the COVID-19 pandemic, pneumatoceles caused by 2019-nCoV are easily neglected, particularly in children. Which may potentially increase the risk of pneumothorax and pulmonary infection, we need to pay special attention to the management of population migration, especially for children.
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Affiliation(s)
- Daqian He
- Department of Thoracic Surgery I, The Third Affiliated Hospital of Kunming Medical University (Yunnan Cancer Hospital, Yunnan Cancer Center), Kunming 650118, China
| | - Cong Sun
- Department of Respiratory and Critical Care Medicine, Yunnan Province Third People's Hospital, Kunming 650011, China
| | - Zhongjun Shang
- Department of Hospital Affairs, The Third Affiliated Hospital of Kunming Medical University (Yunnan Cancer Hospital, Yunnan Cancer Center), Kunming 650118, China
| | - Yuansong Yang
- COVID-19 Treatment Group, Tengchong County Hospital of Traditional Chinese Medicine, Tengchong 650011, China
| | - Ying Chen
- Department of Thoracic Surgery I, The Third Affiliated Hospital of Kunming Medical University (Yunnan Cancer Hospital, Yunnan Cancer Center), Kunming 650118, China
- To whom correspondence should be addressed. Ying Chen, Department of Thoracic Surgery I, The Third Affiliated Hospital of Kunming Medical University (Yunnan Cancer Hospital, Yunnan Cancer Center), 510 Kunzhou Road, Kunming 650118, China. Phone: +8613759428132, E-mail:
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Ippolito D, Vernuccio F, Maino C, Cannella R, Giandola T, Ragusi M, Bigiogera V, Capodaglio C, Sironi S. Multiorgan Involvement in SARS-CoV-2 Infection: The Role of the Radiologist from Head to Toe. Diagnostics (Basel) 2022; 12:1188. [PMID: 35626344 PMCID: PMC9140872 DOI: 10.3390/diagnostics12051188] [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] [Received: 03/30/2022] [Revised: 04/29/2022] [Accepted: 05/05/2022] [Indexed: 01/08/2023] Open
Abstract
Radiology plays a crucial role for the diagnosis and management of COVID-19 patients during the different stages of the disease, allowing for early detection of manifestations and complications of COVID-19 in the different organs. Lungs are the most common organs involved by SARS-CoV-2 and chest computed tomography (CT) represents a reliable imaging-based tool in acute, subacute, and chronic settings for diagnosis, prognosis, and management of lung disease and the evaluation of acute and chronic complications. Cardiac involvement can be evaluated by using cardiac computed tomography angiography (CCTA), considered as the best choice to solve the differential diagnosis between the most common cardiac conditions: acute coronary syndrome, myocarditis, and cardiac dysrhythmia. By using compressive ultrasound it's possible to study the peripheral arteries and veins and to exclude the deep vein thrombosis, directly linked to the onset of pulmonary embolism. Moreover, CT and especially MRI can help to evaluate the gastrointestinal involvement and assess hepatic function, pancreas involvement, and exclude causes of lymphocytopenia, thrombocytopenia, and leukopenia, typical of COVID-19 patients. Finally, radiology plays a crucial role in the early identification of renal damage in COVID-19 patients, by using both CT and US. This narrative review aims to provide a comprehensive radiological analysis of commonly involved organs in patients with COVID-19 disease.
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Affiliation(s)
- Davide Ippolito
- Department of Diagnostic Radiology, San Gerardo Hospital, Via Pergolesi 33, 20900 Monza, MB, Italy; (D.I.); (C.M.); (T.G.); (M.R.); (V.B.); (C.C.)
- School of Medicine, University of Milano-Bicocca, Via Cadore 48, 20900 Monza, MB, Italy;
| | - Federica Vernuccio
- Department of Radiology, University Hospital of Padova, Via Nicolò Giustiniani, 2, 35128 Padova, PD, Italy
| | - Cesare Maino
- Department of Diagnostic Radiology, San Gerardo Hospital, Via Pergolesi 33, 20900 Monza, MB, Italy; (D.I.); (C.M.); (T.G.); (M.R.); (V.B.); (C.C.)
| | - Roberto Cannella
- Department of Biomedicine, Neuroscience and Advanced Diagnostics, University of Palermo, Via del Vespro, 129, 90127 Palermo, PA, Italy;
- Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties (PROMISE), University of Palermo, Via del Vespro, 129, 90127 Palermo, PA, Italy
| | - Teresa Giandola
- Department of Diagnostic Radiology, San Gerardo Hospital, Via Pergolesi 33, 20900 Monza, MB, Italy; (D.I.); (C.M.); (T.G.); (M.R.); (V.B.); (C.C.)
| | - Maria Ragusi
- Department of Diagnostic Radiology, San Gerardo Hospital, Via Pergolesi 33, 20900 Monza, MB, Italy; (D.I.); (C.M.); (T.G.); (M.R.); (V.B.); (C.C.)
| | - Vittorio Bigiogera
- Department of Diagnostic Radiology, San Gerardo Hospital, Via Pergolesi 33, 20900 Monza, MB, Italy; (D.I.); (C.M.); (T.G.); (M.R.); (V.B.); (C.C.)
- School of Medicine, University of Milano-Bicocca, Via Cadore 48, 20900 Monza, MB, Italy;
| | - Carlo Capodaglio
- Department of Diagnostic Radiology, San Gerardo Hospital, Via Pergolesi 33, 20900 Monza, MB, Italy; (D.I.); (C.M.); (T.G.); (M.R.); (V.B.); (C.C.)
- School of Medicine, University of Milano-Bicocca, Via Cadore 48, 20900 Monza, MB, Italy;
| | - Sandro Sironi
- School of Medicine, University of Milano-Bicocca, Via Cadore 48, 20900 Monza, MB, Italy;
- Department of Diagnostic Radiology, H Papa Giovanni XXIII, Piazza OMS 1, 24127 Bergamo, BG, Italy
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Furstenberger M, Gallais F, Freudenberger S, Kessler R, Chenard MP, Renaud-Picard B. Atypical unilateral SARS-CoV-2 pneumonia in a single lung re-transplanted patient: a case report. Transplant Proc 2022; 54:1564-1568. [PMID: 35773083 PMCID: PMC9149239 DOI: 10.1016/j.transproceed.2022.05.007] [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: 04/16/2022] [Revised: 05/02/2022] [Accepted: 05/07/2022] [Indexed: 12/15/2022]
Abstract
Background Since December 2019, the SARS-CoV-2 pandemic significantly has impacted the medical community. When infected with SARS-CoV-2, most of the patients developed bilateral pneumonia. We have herein presented the atypical case of a patient who developed unilateral SARS-CoV-2 pneumonia, affecting only the second lung allograft re-transplanted (re-LTX). Case Presentation A SARS-CoV-2 infection occurred in a 2-dose vaccinated patient with LTx with a history of second unilateral lung transplantation performed after an end-stage bronchiolitis obliterans syndrome. The first symptoms started with a flu-like syndrome, and the patient's clinical condition worsened with nonsevere acute respiratory failure requiring conventional oxygen therapy. Treatment consisted in administrating specific anti-SARS-CoV-2 monoclonal antibodies along with probabilistic antibiotherapy, anticoagulation, and steroids. On day 7, the patient was discharged from hospital. We aimed to assess this atypical unilateral pneumonia based on different explorations. A ventilation scintigraphy showed a severe ventilation decrease owing to end-stage bronchiolitis obliterans syndrome within the left first allograft, which may be associated with asymmetrical virus diffusion between the 2 lungs. We did not identify any other relevant differences with respect to the 2 donors’ clinical characteristics. Using specific immunohistochemistry staining against angiotensin converting enzyme-2 receptor, the main known receptor for SARS-CoV-2 binding on airway epithelial cells, no staining difference was observed between the 2 lung biopsies that were collected at re-LTx from each lung. Conclusions With the present case report, we aimed to highlight how this kind of unusual presentation may be caused by the difference of ventilation between the 2 lungs.
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Al-Hakeim HK, Al-Jassas HK, Morris G, Maes M. Increased ACE2, sRAGE, and Immune Activation, but Lowered Calcium and Magnesium in COVID-19. RECENT ADVANCES IN INFLAMMATION & ALLERGY DRUG DISCOVERY 2022; 16:32-43. [PMID: 35307003 DOI: 10.2174/2772270816666220318103929] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Revised: 01/17/2022] [Accepted: 01/19/2022] [Indexed: 01/08/2023]
Abstract
BACKGROUND The characterization of new biomarkers that could help externally validate the diagnosis of COVID-19 and optimize treatments is extremely important. Many studies have established changes in immune-inflammatory and antibody levels, but few studies measured the soluble receptor for the advanced glycation end product (sRAGE), angiotensin-converting enzyme 2 (ACE2), calcium, and magnesium in COVID-19. OBJECTIVE To evaluate serum advanced glycation end-product receptor (sRAGE) and angiotensin converting enzyme (ACE)2 and peripheral oxygen saturation (SpO2) and chest CT scan abnormalities (CCTA) in COVID-19. METHODS sRAGE, ACE2, interleukin (IL)-6, IL-10, C-reactive protein (CRP), calcium, magnesium, and albumin were measured in 60 COVID-19 patients and 30 healthy controls. RESULTS COVID-19 is characterized by significantly increased IL-6, CRP, IL-10, sRAGE, ACE2, and lowered SpO2, albumin, magnesium, and calcium. COVID-19 with CCTAs showed lower SpO2 and albumin. SpO2 was significantly inversely correlated with IL-6, IL-10, CRP, sRAGE, and ACE2, and positively with albumin, magnesium, and calcium. Neural networks showed that a combination of calcium, IL-6, CRP, and sRAGE yielded an accuracy of 100% in detecting COVID-19 patients, with calcium being the most important predictor followed by IL-6 and CRP. Patients with positive IgG results showed a significant elevation in the serum level of IL-6, sRAGE, and ACE2 compared to the negatively IgG patient subgroup. CONCLUSION The results show that immune-inflammatory and RAGE pathways biomarkers may be used as an external validating criterion for the diagnosis of COVID-19. Those pathways coupled with lowered SpO2, calcium, and magnesium are drug targets that may help reduce the consequences of COVID-19.
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Affiliation(s)
| | | | - Gerwyn Morris
- School of Medicine, IMPACT-the Institute for Mental and Physical Health and Clinical Translation, Deakin University, Barwon Health, Geelong, Australia
| | - Michael Maes
- School of Medicine, IMPACT-the Institute for Mental and Physical Health and Clinical Translation, Deakin University, Barwon Health, Geelong, Australia.,Department of Psychiatry, Medical University of Plovdiv, Plovdiv, Bulgaria.,Department of Psychiatry, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
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Hassan H, Ren Z, Zhou C, Khan MA, Pan Y, Zhao J, Huang B. Supervised and weakly supervised deep learning models for COVID-19 CT diagnosis: A systematic review. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 218:106731. [PMID: 35286874 PMCID: PMC8897838 DOI: 10.1016/j.cmpb.2022.106731] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Revised: 01/28/2022] [Accepted: 03/03/2022] [Indexed: 05/05/2023]
Abstract
Artificial intelligence (AI) and computer vision (CV) methods become reliable to extract features from radiological images, aiding COVID-19 diagnosis ahead of the pathogenic tests and saving critical time for disease management and control. Thus, this review article focuses on cascading numerous deep learning-based COVID-19 computerized tomography (CT) imaging diagnosis research, providing a baseline for future research. Compared to previous review articles on the topic, this study pigeon-holes the collected literature very differently (i.e., its multi-level arrangement). For this purpose, 71 relevant studies were found using a variety of trustworthy databases and search engines, including Google Scholar, IEEE Xplore, Web of Science, PubMed, Science Direct, and Scopus. We classify the selected literature in multi-level machine learning groups, such as supervised and weakly supervised learning. Our review article reveals that weak supervision has been adopted extensively for COVID-19 CT diagnosis compared to supervised learning. Weakly supervised (conventional transfer learning) techniques can be utilized effectively for real-time clinical practices by reusing the sophisticated features rather than over-parameterizing the standard models. Few-shot and self-supervised learning are the recent trends to address data scarcity and model efficacy. The deep learning (artificial intelligence) based models are mainly utilized for disease management and control. Therefore, it is more appropriate for readers to comprehend the related perceptive of deep learning approaches for the in-progress COVID-19 CT diagnosis research.
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Affiliation(s)
- Haseeb Hassan
- College of Big data and Internet, Shenzhen Technology University, Shenzhen, 518118, China; Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Shenzhen University Health Science Center, Shenzhen, China; College of Applied Sciences, Shenzhen University, Shenzhen, 518060, China
| | - Zhaoyu Ren
- College of Big data and Internet, Shenzhen Technology University, Shenzhen, 518118, China
| | - Chengmin Zhou
- College of Big data and Internet, Shenzhen Technology University, Shenzhen, 518118, China
| | - Muazzam A Khan
- Department of Computer Sciences, Quaid-i-Azam University, Islamabad, Pakistan
| | - Yi Pan
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, China
| | - Jian Zhao
- College of Big data and Internet, Shenzhen Technology University, Shenzhen, 518118, China.
| | - Bingding Huang
- College of Big data and Internet, Shenzhen Technology University, Shenzhen, 518118, China.
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Mirza MW, Siddiq A, Khan IR. A comparative study of medical image enhancement algorithms and quality assessment metrics on COVID-19 CT images. SIGNAL, IMAGE AND VIDEO PROCESSING 2022; 17:915-924. [PMID: 35493403 PMCID: PMC9037579 DOI: 10.1007/s11760-022-02214-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/02/2021] [Revised: 12/10/2021] [Accepted: 03/20/2022] [Indexed: 06/14/2023]
Abstract
Medical imaging can help doctors in better diagnosis of several conditions. During the present COVID-19 pandemic, timely detection of novel coronavirus is crucial, which can help in curing the disease at an early stage. Image enhancement techniques can improve the visual appearance of COVID-19 CT scans and speed-up the process of diagnosis. In this study, we analyze some state-of-the-art image enhancement techniques for their suitability in enhancing the CT scans of COVID-19 patients. Six quantitative metrics, Entropy, SSIM, AMBE, PSNR, EME, and EMEE, are used to evaluate the enhanced images. Two experienced radiologists were involved in the study to evaluate the performance of the enhancement techniques and the quantitative metrics used to assess them.
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Affiliation(s)
- Muhammad Waqar Mirza
- Electrical Engineering Department, Pakistan Institute of Engineering and Technology, Multan, Pakistan
| | - Asif Siddiq
- Electrical Engineering Department, Pakistan Institute of Engineering and Technology, Multan, Pakistan
| | - Ishtiaq Rasool Khan
- College of Computer Science and Engineering, University of Jeddah, Jeddah, Saudi Arabia
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Scarpiniti M, Sarv Ahrabi S, Baccarelli E, Piazzo L, Momenzadeh A. A novel unsupervised approach based on the hidden features of Deep Denoising Autoencoders for COVID-19 disease detection. EXPERT SYSTEMS WITH APPLICATIONS 2022; 192:116366. [PMID: 34937995 PMCID: PMC8675154 DOI: 10.1016/j.eswa.2021.116366] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/26/2021] [Revised: 10/15/2021] [Accepted: 11/30/2021] [Indexed: 05/02/2023]
Abstract
Chest imaging can represent a powerful tool for detecting the Coronavirus disease 2019 (COVID-19). Among the available technologies, the chest Computed Tomography (CT) scan is an effective approach for reliable and early detection of the disease. However, it could be difficult to rapidly identify by human inspection anomalous area in CT images belonging to the COVID-19 disease. Hence, it becomes necessary the exploitation of suitable automatic algorithms able to quick and precisely identify the disease, possibly by using few labeled input data, because large amounts of CT scans are not usually available for the COVID-19 disease. The method proposed in this paper is based on the exploitation of the compact and meaningful hidden representation provided by a Deep Denoising Convolutional Autoencoder (DDCAE). Specifically, the proposed DDCAE, trained on some target CT scans in an unsupervised way, is used to build up a robust statistical representation generating a target histogram. A suitable statistical distance measures how this target histogram is far from a companion histogram evaluated on an unknown test scan: if this distance is greater of a threshold, the test image is labeled as anomaly, i.e. the scan belongs to a patient affected by COVID-19 disease. Some experimental results and comparisons with other state-of-the-art methods show the effectiveness of the proposed approach reaching a top accuracy of 100% and similar high values for other metrics. In conclusion, by using a statistical representation of the hidden features provided by DDCAEs, the developed architecture is able to differentiate COVID-19 from normal and pneumonia scans with high reliability and at low computational cost.
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Affiliation(s)
- Michele Scarpiniti
- Department of Information Engineering, Electronics and Telecommunications (DIET), Sapienza University of Rome, Via Eudossiana 18, 00184 Rome, Italy
| | - Sima Sarv Ahrabi
- Department of Information Engineering, Electronics and Telecommunications (DIET), Sapienza University of Rome, Via Eudossiana 18, 00184 Rome, Italy
| | - Enzo Baccarelli
- Department of Information Engineering, Electronics and Telecommunications (DIET), Sapienza University of Rome, Via Eudossiana 18, 00184 Rome, Italy
| | - Lorenzo Piazzo
- Department of Information Engineering, Electronics and Telecommunications (DIET), Sapienza University of Rome, Via Eudossiana 18, 00184 Rome, Italy
| | - Alireza Momenzadeh
- Department of Information Engineering, Electronics and Telecommunications (DIET), Sapienza University of Rome, Via Eudossiana 18, 00184 Rome, Italy
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Long B, Carius BM, Chavez S, Liang SY, Brady WJ, Koyfman A, Gottlieb M. Clinical update on COVID-19 for the emergency clinician: Presentation and evaluation. Am J Emerg Med 2022; 54:46-57. [PMID: 35121478 PMCID: PMC8779861 DOI: 10.1016/j.ajem.2022.01.028] [Citation(s) in RCA: 125] [Impact Index Per Article: 62.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2021] [Revised: 01/01/2022] [Accepted: 01/12/2022] [Indexed: 02/07/2023] Open
Abstract
INTRODUCTION Coronavirus disease of 2019 (COVID-19) has resulted in millions of cases worldwide. As the pandemic has progressed, the understanding of this disease has evolved. OBJECTIVE This first in a two-part series on COVID-19 updates provides a focused overview of the presentation and evaluation of COVID-19 for emergency clinicians. DISCUSSION COVID-19, caused by Severe Acute Respiratory Syndrome coronavirus 2 (SARS-CoV-2), has resulted in significant morbidity and mortality worldwide. Several variants exist, including a variant of concern known as Delta (B.1.617.2 lineage) and the Omicron variant (B.1.1.529 lineage). The Delta variant is associated with higher infectivity and poor patient outcomes, and the Omicron variant has resulted in a significant increase in infections. While over 80% of patients experience mild symptoms, a significant proportion can be critically ill, including those who are older and those with comorbidities. Upper respiratory symptoms, fever, and changes in taste/smell remain the most common presenting symptoms. Extrapulmonary complications are numerous and may be severe, including the cardiovascular, neurologic, gastrointestinal, and dermatologic systems. Emergency department evaluation includes focused testing for COVID-19 and assessment of end-organ injury. Imaging may include chest radiography, computed tomography, or ultrasound. Several risk scores may assist in prognostication, including the 4C (Coronavirus Clinical Characterisation Consortium) score, quick COVID Severity Index (qCSI), NEWS2, and the PRIEST score, but these should only supplement and not replace clinical judgment. CONCLUSION This review provides a focused update of the presentation and evaluation of COVID-19 for emergency clinicians.
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Affiliation(s)
- Brit Long
- SAUSHEC, Emergency Medicine, Brooke Army Medical Center, Fort Sam Houston, TX, USA.
| | | | - Summer Chavez
- Department of Emergency Medicine, MedStar Georgetown University Hospital, 3800 Reservoir Road, NW, Washington, DC 20007, United States
| | - Stephen Y Liang
- Divisions of Emergency Medicine and Infectious Diseases, Washington University School of Medicine, 660 S. Euclid Ave, St. Louis, MO 63110, United States.
| | - William J Brady
- Department of Emergency Medicine, University of Virginia School of Medicine, Charlottesville, VA, United States.
| | - Alex Koyfman
- The University of Texas Southwestern Medical Center, Department of Emergency Medicine, 5323 Harry Hines Boulevard, Dallas, TX 75390, United States
| | - Michael Gottlieb
- Department of Emergency Medicine, Rush University Medical Center, Chicago, IL, United States
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Samanta J, Mahapatra SJ, Kumar N, Elhence A, Dhar J, Gupta A, Dhooria A, Bhalla A, Prasad M, Das A, Sharma R, Kochhar R, Garg PK, Gupta A, Elhence A, Dhooria A, Das A, Bhalla A, Puri GD, Dhar J, Yegurla J, Samanta J, Vaishnav M, Prasad M, Kumar N, Garg PK, Sethia R, Bansal R, Rana R, Kochhar R, Golla R, Biswas S, Mundhra SK, Agarwal S, Prasad S, Mahapatra SJ. Virus related acute pancreatitis and virus superinfection in the 'Dual disease' model of acute pancreatitis and SARS-Co-V2 infection: A multicentre prospective study. Pancreatology 2022; 22:339-347. [PMID: 35131169 PMCID: PMC8786679 DOI: 10.1016/j.pan.2022.01.008] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Revised: 12/15/2021] [Accepted: 01/12/2022] [Indexed: 02/08/2023]
Abstract
BACKGROUND SARS-CoV-2 can cause acute pancreatitis (AP) and SARS-CoV-2 superinfection can occur in patients with AP during prolonged hospitalisation. Our objective was to characterize SARS-CoV-2 related AP and study the impact of SARS-CoV-2 superinfection on outcomes in AP. METHODS In this multicentre prospective study, all patients with AP and SARS-CoV-2 infection between August 2020 and February 2021 were divided into two groups: SARS-CoV-2-related AP and superadded SARS-CoV-2 infection in patients with AP. The two groups were compared with each other and the whole cohort was compared with a non-COVID AP cohort. RESULTS A total of 85 patients with SARS-CoV-2 and AP (SARS-CoV-2-related AP; n = 18 and AP with SARS-CoV-2 superadded infection; n = 67) were included during the study period. They had a higher mortality [28 (32.9%) vs. 44 (19.1%), aOR 2.8 (95% CI, 1.5-5.3)] than 230 propensity matched non-COVID AP patients. Mortality in SARS-CoV-2 and AP patients was due to critical COVID. SARS-CoV-2-related- AP (n = 18) had a higher but statistically insignificant mortality than SARS-CoV-2 superinfection in AP [8/18 (44.4%) vs 20/67 (29.8%), p = 0.24]. On multivariable analysis, infection with SARS-CoV-2 (aHR 2.3; 95% CI, 1.43.7) was a predictor of in-hospital mortality in addition to organ failure (OF) in patients with AP. CONCLUSION Patients with AP and SARS-CoV-2 infection had a higher mortality than matched non-COVID AP patients which was largely attributable to the severity of COVID-19. SARS-CoV-2 related AP had higher OF and in-hospital mortality.
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Affiliation(s)
- Jayanta Samanta
- Department of Gastroenterology, Post Graduate Institute of Medical Education and Research, Chandigarh, India
| | | | - Naveen Kumar
- Department of Gastroenterology, Post Graduate Institute of Medical Education and Research, Chandigarh, India
| | - Anshuman Elhence
- Department of Gastroenterology, All India Institute of Medical Sciences, New Delhi, India
| | - Jahnvi Dhar
- Department of Gastroenterology, Post Graduate Institute of Medical Education and Research, Chandigarh, India
| | - Anany Gupta
- Department of Gastroenterology, All India Institute of Medical Sciences, New Delhi, India
| | - Anugrah Dhooria
- Department of Gastroenterology, All India Institute of Medical Sciences, New Delhi, India
| | - Ashish Bhalla
- Department of Internal Medicine, Post Graduate Institute of Medical Education and Research, Chandigarh, India
| | - Manya Prasad
- Department of Clinical Research, Institute of Liver and Biliary Sciences, New Delhi, India
| | - Aritra Das
- Consultant Epidemiologist, Bihar Technical Support Program, CARE India, Bihar, India
| | - Raju Sharma
- Department of Radiodiagnosis, All India Institute of Medical Sciences, New Delhi, India
| | - Rakesh Kochhar
- Department of Gastroenterology, Post Graduate Institute of Medical Education and Research, Chandigarh, India,Corresponding author. Department of Gastroenterology, Post Graduate Institute of Medical Education and Research, Sector-12, Chandigarh, 160012, India
| | - Pramod K. Garg
- Department of Gastroenterology, All India Institute of Medical Sciences, New Delhi, India,Corresponding author. Department of Gastroenterology, All India Institute of Medical Sciences, New Delhi 110029, India
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Laino ME, Ammirabile A, Lofino L, Lundon DJ, Chiti A, Francone M, Savevski V. Prognostic findings for ICU admission in patients with COVID-19 pneumonia: baseline and follow-up chest CT and the added value of artificial intelligence. Emerg Radiol 2022; 29:243-262. [PMID: 35048222 PMCID: PMC8769787 DOI: 10.1007/s10140-021-02008-y] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2021] [Accepted: 12/03/2021] [Indexed: 01/08/2023]
Abstract
Infection with SARS-CoV-2 has dominated discussion and caused global healthcare and economic crisis over the past 18 months. Coronavirus disease 19 (COVID-19) causes mild-to-moderate symptoms in most individuals. However, rapid deterioration to severe disease with or without acute respiratory distress syndrome (ARDS) can occur within 1-2 weeks from the onset of symptoms in a proportion of patients. Early identification by risk stratifying such patients who are at risk of severe complications of COVID-19 is of great clinical importance. Computed tomography (CT) is widely available and offers the potential for fast triage, robust, rapid, and minimally invasive diagnosis: Ground glass opacities (GGO), crazy-paving pattern (GGO with superimposed septal thickening), and consolidation are the most common chest CT findings in COVID pneumonia. There is growing interest in the prognostic value of baseline chest CT since an early risk stratification of patients with COVID-19 would allow for better resource allocation and could help improve outcomes. Recent studies have demonstrated the utility of baseline chest CT to predict intensive care unit (ICU) admission in patients with COVID-19. Furthermore, developments and progress integrating artificial intelligence (AI) with computer-aided design (CAD) software for diagnostic imaging allow for objective, unbiased, and rapid assessment of CT images.
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Affiliation(s)
- Maria Elena Laino
- Artificial Intelligence Center, IRCCS Humanitas Research Hospital, via Manzoni 56, Rozzano, 20089 Milan, Italy
| | - Angela Ammirabile
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, Pieve Emanuele, 20072 Milan, Italy
- Department of Radiology, IRCCS Humanitas Research Hospital, via Manzoni 56, Rozzano, 20089 Milan, Italy
| | - Ludovica Lofino
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, Pieve Emanuele, 20072 Milan, Italy
- Department of Radiology, IRCCS Humanitas Research Hospital, via Manzoni 56, Rozzano, 20089 Milan, Italy
| | - Dara Joseph Lundon
- Artificial Intelligence Center, IRCCS Humanitas Research Hospital, via Manzoni 56, Rozzano, 20089 Milan, Italy
| | - Arturo Chiti
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, Pieve Emanuele, 20072 Milan, Italy
- Humanitas Clinical and Research Center—IRCCS, Via Manzoni 56, 20089 Rozzano, Italy
| | - Marco Francone
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, Pieve Emanuele, 20072 Milan, Italy
- Department of Radiology, IRCCS Humanitas Research Hospital, via Manzoni 56, Rozzano, 20089 Milan, Italy
| | - Victor Savevski
- Artificial Intelligence Center, IRCCS Humanitas Research Hospital, via Manzoni 56, Rozzano, 20089 Milan, Italy
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Luger AK, Sonnweber T, Gruber L, Schwabl C, Cima K, Tymoszuk P, Gerstner AK, Pizzini A, Sahanic S, Boehm A, Coen M, Strolz CJ, Wöll E, Weiss G, Kirchmair R, Feuchtner GM, Prosch H, Tancevski I, Löffler-Ragg J, Widmann G. Chest CT of Lung Injury 1 Year after COVID-19 Pneumonia: The CovILD Study. Radiology 2022; 304:462-470. [PMID: 35348379 PMCID: PMC8988857 DOI: 10.1148/radiol.211670] [Citation(s) in RCA: 50] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
Background The long-term pulmonary sequelae of COVID-19 is not well known. Purpose To characterize patterns and rates of improvement of chest CT
abnormalities 1 year after COVID-19 pneumonia. Materials and Methods This was a secondary analysis of a prospective, multicenter observational
cohort study conducted from April 29 to August 12, 2020, to assess
pulmonary abnormalities at chest CT approximately 2, 3, and 6 months and
1 year after onset of COVID-19 symptoms. Pulmonary findings were graded
for each lung lobe using a qualitative CT severity score (CTSS) ranging
from 0 (normal) to 25 (all lobes involved). The association of
demographic and clinical factors with CT abnormalities after 1 year was
assessed with logistic regression. The rate of change of the CTSS at
follow-up CT was investigated by using the Friedmann test. Results Of 142 enrolled participants, 91 underwent a 1-year follow-up CT
examination and were included in the analysis (mean age, 59 years
± 13 [SD]; 35 women [38%]). In 49 of 91 (54%) participants, CT
abnormalities were observed: 31 of 91 (34%) participants showed subtle
subpleural reticulation, ground-glass opacities, or both, and 18 of 91
(20%) participants had extensive ground-glass opacities, reticulations,
bronchial dilation, microcystic changes, or a combination thereof. At
multivariable analysis, age of more than 60 years (odds ratio [OR], 5.8;
95% CI: 1.7, 24; P = .009), critical COVID-19 severity
(OR, 29; 95% CI: 4.8, 280; P < .001), and male
sex (OR, 8.9; 95% CI: 2.6, 36; P < .001) were
associated with persistent CT abnormalities at 1-year follow-up.
Reduction of CTSS was observed in participants at subsequent follow-up
CT (P < .001); during the study period, 49% (69
of 142) of participants had complete resolution of CT abnormalities.
Thirty-one of 49 (63%) participants with CT abnormalities showed no
further improvement after 6 months. Conclusion Long-term CT abnormalities were common 1 year after COVID-19
pneumonia. © RSNA, 2022 Online supplemental material is available for this
article. See also the editorial by Leung in this issue.
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Affiliation(s)
- Anna K Luger
- Department for Radiology, Medical University of Innsbruck, Austria
| | - Thomas Sonnweber
- Department for Radiology, Medical University of Innsbruck, Austria
| | - Leonhard Gruber
- Department for Radiology, Medical University of Innsbruck, Austria
| | | | - Katharina Cima
- Department of Internal Medicine, Medical University Innsbruck, Innsbruck 6020, Austria
| | - Piotr Tymoszuk
- Department of Internal Medicine, Medical University Innsbruck, Innsbruck 6020, Austria
| | - Anna K Gerstner
- Department for Radiology, Medical University of Innsbruck, Austria
| | - Alex Pizzini
- Department of Internal Medicine, Medical University Innsbruck, Innsbruck 6020, Austria
| | - Sabina Sahanic
- Department of Internal Medicine, Medical University Innsbruck, Innsbruck 6020, Austria
| | - Anna Boehm
- Department of Internal Medicine, Medical University Innsbruck, Innsbruck 6020, Austria
| | - Maximilian Coen
- Department of Internal Medicine, Medical University Innsbruck, Innsbruck 6020, Austria
| | - Carola J Strolz
- Department of Visceral, Transplant and Thoracic Surgery, Medical University of Innsbruck, Innsbruck, Austria
| | - Ewald Wöll
- Deptartment of Internal Medicine, St Vinzenz Hospital, Zams, Austria
| | - Günter Weiss
- Department of Internal Medicine, Medical University Innsbruck, Innsbruck 6020, Austria
| | - Rudolf Kirchmair
- Department of Internal Medicine, Medical University Innsbruck, Innsbruck 6020, Austria
| | | | - Helmut Prosch
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna General Hospital, Vienna, Austria
| | - Ivan Tancevski
- Department of Internal Medicine, Medical University Innsbruck, Innsbruck 6020, Austria
| | - Judith Löffler-Ragg
- Department of Internal Medicine, Medical University Innsbruck, Innsbruck 6020, Austria
| | - Gerlig Widmann
- Department for Radiology, Medical University of Innsbruck, Austria
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Brandi N, Ciccarese F, Rimondi MR, Balacchi C, Modolon C, Sportoletti C, Renzulli M, Coppola F, Golfieri R. An Imaging Overview of COVID-19 ARDS in ICU Patients and Its Complications: A Pictorial Review. Diagnostics (Basel) 2022; 12:846. [PMID: 35453894 PMCID: PMC9032937 DOI: 10.3390/diagnostics12040846] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Revised: 03/23/2022] [Accepted: 03/28/2022] [Indexed: 01/08/2023] Open
Abstract
A significant proportion of patients with COVID-19 pneumonia could develop acute respiratory distress syndrome (ARDS), thus requiring mechanical ventilation, and resulting in a high rate of intensive care unit (ICU) admission. Several complications can arise during an ICU stay, from both COVID-19 infection and the respiratory supporting system, including barotraumas (pneumothorax and pneumomediastinum), superimposed pneumonia, coagulation disorders (pulmonary embolism, venous thromboembolism, hemorrhages and acute ischemic stroke), abdominal involvement (acute mesenteric ischemia, pancreatitis and acute kidney injury) and sarcopenia. Imaging plays a pivotal role in the detection and monitoring of ICU complications and is expanding even to prognosis prediction. The present pictorial review describes the clinicopathological and radiological findings of COVID-19 ARDS in ICU patients and discusses the imaging features of complications related to invasive ventilation support, as well as those of COVID-19 itself in this particularly fragile population. Radiologists need to be familiar with COVID-19's possible extra-pulmonary complications and, through reliable and constant monitoring, guide therapeutic decisions. Moreover, as more research is pursued and the pathophysiology of COVID-19 is increasingly understood, the role of imaging must evolve accordingly, expanding from the diagnosis and subsequent management of patients to prognosis prediction.
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Affiliation(s)
- Nicolò Brandi
- Department of Radiology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Via Albertoni 15, 40138 Bologna, Italy; (F.C.); (C.B.); (M.R.); (F.C.); (R.G.)
| | - Federica Ciccarese
- Department of Radiology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Via Albertoni 15, 40138 Bologna, Italy; (F.C.); (C.B.); (M.R.); (F.C.); (R.G.)
| | - Maria Rita Rimondi
- Cardio-Thoracic Radiology Unit, University Hospital S.Orsola-Malpighi, 40138 Bologna, Italy; (M.R.R.); (C.M.); (C.S.)
| | - Caterina Balacchi
- Department of Radiology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Via Albertoni 15, 40138 Bologna, Italy; (F.C.); (C.B.); (M.R.); (F.C.); (R.G.)
| | - Cecilia Modolon
- Cardio-Thoracic Radiology Unit, University Hospital S.Orsola-Malpighi, 40138 Bologna, Italy; (M.R.R.); (C.M.); (C.S.)
| | - Camilla Sportoletti
- Cardio-Thoracic Radiology Unit, University Hospital S.Orsola-Malpighi, 40138 Bologna, Italy; (M.R.R.); (C.M.); (C.S.)
| | - Matteo Renzulli
- Department of Radiology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Via Albertoni 15, 40138 Bologna, Italy; (F.C.); (C.B.); (M.R.); (F.C.); (R.G.)
| | - Francesca Coppola
- Department of Radiology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Via Albertoni 15, 40138 Bologna, Italy; (F.C.); (C.B.); (M.R.); (F.C.); (R.G.)
- Italian Society of Medical and Interventional Radiology, SIRM Foundation, Via della Signora 2, 20122 Milano, Italy
| | - Rita Golfieri
- Department of Radiology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Via Albertoni 15, 40138 Bologna, Italy; (F.C.); (C.B.); (M.R.); (F.C.); (R.G.)
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Li X, Zhao Y, Lu Y, Zheng Y, Mei N, Han Q, Ruan Z, Xiao A, Qiu X, Wang D, Yin B. Performances of clinical characteristics and radiological findings in identifying COVID-19 from suspected cases. BMC Med Imaging 2022; 22:55. [PMID: 35346080 PMCID: PMC8960213 DOI: 10.1186/s12880-022-00780-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2021] [Accepted: 03/21/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND To identify effective factors and establish a model to distinguish COVID-19 patients from suspected cases. METHODS The clinical characteristics, laboratory results and initial chest CT findings of suspected COVID-19 patients in 3 institutions were retrospectively reviewed. Univariate and multivariate logistic regression were performed to identify significant features. A nomogram was constructed, with calibration validated internally and externally. RESULTS 239 patients from 2 institutions were enrolled in the primary cohort including 157 COVID-19 and 82 non-COVID-19 patients. 11 features were selected by LASSO selection, and 8 features were found significant using multivariate logistic regression analysis. We found that the COVID-19 group are more likely to have fever (OR 4.22), contact history (OR 284.73), lower WBC count (OR 0.63), left lower lobe involvement (OR 9.42), multifocal lesions (OR 8.98), pleural thickening (OR 5.59), peripheral distribution (OR 0.09), and less mediastinal lymphadenopathy (OR 0.037). The nomogram developed accordingly for clinical practice showed satisfactory internal and external validation. CONCLUSIONS In conclusion, fever, contact history, decreased WBC count, left lower lobe involvement, pleural thickening, multifocal lesions, peripheral distribution, and absence of mediastinal lymphadenopathy are able to distinguish COVID-19 patients from other suspected patients. The corresponding nomogram is a useful tool in clinical practice.
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Affiliation(s)
- Xuanxuan Li
- Department of Radiology, Huashan Hospital, Fudan University, 12, Middle Wulumuqi Rd., Jing’an District, Shanghai, 200040 China
| | - Yajing Zhao
- Department of Radiology, Huashan Hospital, Fudan University, 12, Middle Wulumuqi Rd., Jing’an District, Shanghai, 200040 China
| | - Yiping Lu
- Department of Radiology, Huashan Hospital, Fudan University, 12, Middle Wulumuqi Rd., Jing’an District, Shanghai, 200040 China
| | - Yingyan Zheng
- Department of Radiology, Huashan Hospital, Fudan University, 12, Middle Wulumuqi Rd., Jing’an District, Shanghai, 200040 China
| | - Nan Mei
- Department of Radiology, Huashan Hospital, Fudan University, 12, Middle Wulumuqi Rd., Jing’an District, Shanghai, 200040 China
| | - Qiuyue Han
- Department of Radiology, Huashan Hospital, Fudan University, 12, Middle Wulumuqi Rd., Jing’an District, Shanghai, 200040 China
| | - Zhuoying Ruan
- Department of Radiology, Huashan Hospital, Fudan University, 12, Middle Wulumuqi Rd., Jing’an District, Shanghai, 200040 China
| | - Anling Xiao
- Department of Radiology, Fu Yang No. 2 People’s Hospital, 450 Linquan Road, Fuyang, Anhui Province China
| | - Xiaohui Qiu
- Department of Radiology, Bozhou People’s Hospital, 616, Duzhong Road, Bozhou, Anhui Province China
| | - Dongdong Wang
- Department of Radiology, Huashan Hospital, Fudan University, 12, Middle Wulumuqi Rd., Jing’an District, Shanghai, 200040 China
| | - Bo Yin
- Department of Radiology, Huashan Hospital, Fudan University, 12, Middle Wulumuqi Rd., Jing’an District, Shanghai, 200040 China
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Air trapping in COVID-19 patients following hospital discharge: retrospective evaluation with paired inspiratory/expiratory thin-section CT. Eur Radiol 2022; 32:4427-4436. [PMID: 35226158 PMCID: PMC8884095 DOI: 10.1007/s00330-022-08580-2] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Revised: 01/14/2022] [Accepted: 01/15/2022] [Indexed: 12/13/2022]
Abstract
Objectives The study reports our experience with paired inspiration/expiration thin-section computed tomographic (CT) scans in the follow-up of COVID-19 patients with persistent respiratory symptoms. Methods From August 13, 2020, to May 31, 2021, 48 long-COVID patients with respiratory symptoms (27 men and 21 women; median age, 62.0 years; interquartile range: 54.0–69.0 years) underwent follow-up paired inspiration-expiration thin-section CT scans. Patient demographics, length of hospital stay, intensive care unit admission rate, and clinical and laboratory features of acute infection were also included. The scans were obtained on a median of 72.5 days after onset of symptoms (interquartile range: 58.5–86.5) and at least 30 days after hospital discharge. Thin-section CT findings included ground-glass opacity, mosaic attenuation pattern, consolidation, traction bronchiectasis, reticulation, parenchymal bands, bronchial wall thickening, and air trapping. We used a quantitative score to determine the degree of air trapping in the expiratory scans. Results Parenchymal abnormality was found in 50% (24/48) of patients and included air trapping (37/48, 77%), ground-glass opacities (19/48, 40%), reticulation (18/48, 38%), parenchymal bands (15/48, 31%), traction bronchiectasis (9/48, 19%), mosaic attenuation pattern (9/48, 19%), bronchial wall thickening (6/48, 13%), and consolidation (2/48, 4%). The absence of air trapping was observed in 11/48 (23%), mild air trapping in 20/48 (42%), moderate in 13/48 (27%), and severe in 4/48 (8%). Independent predictors of air trapping were, in decreasing order of importance, gender (p = 0.0085), and age (p = 0.0182). Conclusions Our results, in a limited number of patients, suggest that follow-up with paired inspiratory/expiratory CT in long-COVID patients with persistent respiratory symptoms commonly displays air trapping. Key Points • Our experience indicates that paired inspiratory/expiratory CT in long-COVID patients with persistent respiratory symptoms commonly displays air trapping. • Iterative reconstruction and dose-reduction options are recommended for demonstrating air trapping in long-COVID patients.
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Abumossalam AM, Abdelgawad TT, Ibrahim MA, Mohamad MD, Ahmed DA, Elhalaby HA. COVID Tarnish Lung: Residual Radiological Lung Consequences of Infection with COVID-19. CURRENT RESPIRATORY MEDICINE REVIEWS 2022. [DOI: 10.2174/1573398x18666220218101742] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Background:
COVID-19 has still expressed as a mysterious viral infection with dramatic pulmonary consequences.
Objectives:
This article aims to study the radiological pulmonary consequences of respiratory covid-19 infection at 6 months and their relevance to the clinical stage, laboratory markers, and management modalities.
Patients and methods:
This study was implemented on two hundred and fifty (250) confirmed positive cases for COVID-19 infections. One hundred and ninety-seven cases (197) who completed the study displayed residual radiological lung shadowing (RRLS) on follow-up computed tomography (CT) of the chest. They were categorized by Simple clinical classification of COVID-19 into groups A, B and C.
Results:
GGO as well as reticulations were statistically significantly higher in group A than other two groups; however, bronchiectasis changes, parenchymal scarring, nodules as well as pleural tractions were statistically significantly higher in group C than the other two groups.
Conclusion:
Respiratory covid-19 infection might be linked to residual radiological lung shadowing. Ground glass opacities GGO, reticulations pervaded in mild involvement with lower inflammatory markers level, unlike, severe changes that expressed scarring, nodules and bronchiectasis changes accompanied by increased levels of inflammatory markers.
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Affiliation(s)
- Ahmed Mohammed Abumossalam
- Department of Pulmonary and Critical Care Medicine- Faculty of Medicine – Mansoura University- Mansoura- Egypt
| | - Taha Taha Abdelgawad
- Department of Thoracic Medicine, Faculty of Medicine, Mansoura University, Egypt
| | | | | | - Dalia Abdelsattar Ahmed
- Department of Radio-diagnosis Dekrnis general hospital Hospital, Dakhlia, Ministry of health, Egypt
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Clinical Differences between Community-Acquired Mycoplasma pneumoniae Pneumonia and COVID-19 Pneumonia. J Clin Med 2022; 11:jcm11040964. [PMID: 35207236 PMCID: PMC8878343 DOI: 10.3390/jcm11040964] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2022] [Revised: 02/07/2022] [Accepted: 02/09/2022] [Indexed: 01/27/2023] Open
Abstract
Mycoplasmapneumoniae is one of the major causative pathogens of community-acquired pneumonia (CAP). M. pneumoniae CAP is clinically and radiologically distinct from bacterial CAPs. One feature of the Japanese Respiratory Society (JRS) guidelines is a trial to be carried out to differentiate between M. pneumoniae pneumonia and bacterial pneumonia for the selection of antibiotics. The purpose of the present study was to clarify the clinical and radiological differences of the M. pneumoniae CAP and coronavirus disease 2019 (COVID-19) CAP. This study was conducted at 5 institutions and assessed a total of 210 patients with M. pneumoniae CAP and 956 patients with COVID-19 CAP. The median age was significantly younger in patients with M. pneumoniae CAP than COVID-19 CAP. Among the clinical symptoms, cough and sputum were observed more frequently in patients with M. pneumoniae CAP than those with COVID-19 CAP. However, the diagnostic specificity of these findings was low. In contrast, loss of taste and anosmia were observed in patients with COVID-19 CAP but not observed in those with M. pneumoniae CAP. Bronchial wall thickening and nodules (tree-in-bud and centrilobular), which are chest computed tomography (CT) features of M. pneumoniae CAP, were rarely observed in patients with COVID-19 CAP. Our results demonstrated that there were two specific differences between M. pneumoniae CAP and COVID-19 CAP: (1) the presence of loss of taste and/or anosmia and (2) chest CT findings.
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Subesinghe M, Bhuva S, Dunn JT, Hammers A, Cook GJ, Barrington SF, Fischer BM. A case-control evaluation of pulmonary and extrapulmonary findings of incidental asymptomatic COVID-19 infection on FDG PET-CT. Br J Radiol 2022; 95:20211079. [PMID: 34930037 PMCID: PMC8822569 DOI: 10.1259/bjr.20211079] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2021] [Revised: 12/02/2021] [Accepted: 12/13/2021] [Indexed: 11/05/2022] Open
Abstract
OBJECTIVES To describe the findings of incidental asymptomatic COVID-19 infection on FDG PET-CT using a case-control design. METHODS Incidental pulmonary findings suspicious of asymptomatic COVID-19 infection on FDG PET-CT were classified as a confirmed (positive RT-PCR test) or suspected case (no/negative RT-PCR test). Control cases were identified using a 4:1 control:case ratio. Pulmonary findings were re-categorised by two reporters using the BSTI classification. SUV metrics in ground glass opacification (GGO)/consolidation (where present), background lung, intrathoracic nodes, liver, spleen and bone marrow were measured. RESULTS 7/9 confirmed and 11/15 suspected cases (COVID-19 group) were re-categorised as BSTI 1 (classic/probable COVID-19) or BSTI 2 (indeterminate COVID-19); 0/96 control cases were categorised as BSTI 1. Agreement between two reporters using the BSTI classification was almost perfect (weighted κ = 0.94). SUVmax GGO/consolidation (5.1 vs 2.2; p < 0.0001) and target-to-background ratio, normalised to liver SUVmean (2.4 vs 1.0; p < 0.0001) were higher in the BSTI 1 & 2 group vs BSTI 3 (non-COVID-19) cases. SUVmax GGO/consolidation discriminated between the BSTI 1 & 2 group vs BSTI 3 (non-COVID-19) cases with high accuracy (AUC = 0.93). SUV metrics were higher (p < 0.05) in the COVID-19 group vs control cases in the lungs, intrathoracic nodes and spleen. CONCLUSION Asymptomatic COVID-19 infection on FDG PET-CT is characterised by bilateral areas of FDG avid (intensity > x2 liver SUVmean) GGO/consolidation and can be identified with high interobserver agreement using the BSTI classification. There is generalised background inflammation within the lungs, intrathoracic nodes and spleen. ADVANCES IN KNOWLEDGE Incidental asymptomatic COVID-19 infection on FDG PET-CT, characterised by bilateral areas of ground glass opacification and consolidation, can be identified with high reproducibility using the BSTI classification. The intensity of associated FDG uptake (>x2 liver SUVmean) provides high discriminative ability in differentiating such cases from pulmonary findings in a non-COVID-19 pattern. Asymptomatic COVID-19 infection causes a generalised background inflammation within the mid-lower zones of the lungs, hilar and central mediastinal nodal stations, and spleen on FDG PET-CT.
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Miyashita N, Nakamori Y, Ogata M, Fukuda N, Yamura A, Ishiura Y, Nomura S. Early identification of novel coronavirus (COVID-19) pneumonia using clinical and radiographic findings. J Infect Chemother 2022; 28:718-721. [PMID: 35190258 PMCID: PMC8828417 DOI: 10.1016/j.jiac.2022.02.005] [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: 10/13/2021] [Revised: 12/16/2021] [Accepted: 02/07/2022] [Indexed: 11/15/2022]
Abstract
Introduction The Japanese Respiratory Society (JRS) scoring system is a useful tool for identifying Mycoplasma pneumoniae pneumonia. Most COVID-19 pneumonia in non-elderly patients (aged <60 years) are classified as atypical pneumonia using the JRS scoring system. We evaluated whether physicians could distinguish between COVID-19 pneumonia and M. pneumoniae pneumonia using chest computed tomography (CT) findings. In addition, we investigated chest CT findings if there is a difference between the variant and non-variant strain. Methods This study was conducted at five institutions and assessed a total of 823 patients with COVID-19 pneumonia (335 had lineage B.1.1.7.) and 100 patients with M. pneumoniae pneumonia. Results In COVID-19 pneumonia, at the first CT examination, peripheral, bilateral ground-glass opacity (GGO) with or without consolidation or crazy-paving pattern was observed frequently. GGO frequently had a round morphology (39.2%). No differences were observed in the radiological findings between the non-B.1.1.7 groups and B.1.1.7 groups. The frequency of pleural effusion, lymphadenopathy, bronchial wall thickening and nodules (tree-in-bud and centrilobular) was low. In contrast to COVID-19 pneumonia, bronchial wall thickening (84%) was observed most frequently, followed by nodules (81%) in M. pneumoniae pneumonia. These findings were significantly higher in M. pneumoniae pneumonia than COVID-19 pneumonia. Conclusions Our results demonstrated that a combination of the JRS scoring system and chest CT findings is useful for the rapid presumptive diagnosis of COVID-19 pneumonia in patients aged <60 years. However, this clinical and radiographic diagnosis is not adapted to elderly people.
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Kalenchic TI, Kabak SL, Primak SV, Melnichenko YM, Kudelich OA. Bilateral parapneumonic pleural effusion with pneumothorax in a patient with covid 19 pneumonia: case report. Radiol Case Rep 2022; 17:869-874. [PMID: 35035651 PMCID: PMC8743860 DOI: 10.1016/j.radcr.2021.12.039] [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] [Received: 12/14/2021] [Accepted: 12/19/2021] [Indexed: 11/21/2022] Open
Abstract
Recurrent pyogenic effusion combined with bilateral pneumothorax is a rare complication associated with the COVID−19 infection. Current article presents the case report of a 68-year-old male with the severe community-acquired bilateral polysegmental viral COVID−19 pneumonia. Chest radiography on the 15th day after admission to the hospital showed the presence of air and pleural effusion in the right pleural cavity with collapse of the right lung. Thoracentesis and thoracostomy in the sixth intercostal space on the mid-axillary line were performed. About 1400 ml of a yellowish opaque liquid were evacuated from the pleural cavity. Pleural fluid analysis confirmed an exudative lymphocytic-rich effusion with no growth of acid-fast bacteria (AFB). In the pleural fluid such gram-negative bacteria as Acinetobacter baumannii and Pseudomonas aeruginosa were cultured. Chest computed tomography obtained on the third day after thoracentesis showed radiological sings of bilateral hydropneumothorax. Needle thoracocentesis and new pleural drainage in the second intercostal space on the right midclavicular line were established. Five days later after the second drainage of the pleural space was initiated the patient was diagnosed with pleural empyema and transferred to the Surgical Clinic. This case report highlights that in patients with COVID-19 recurrent pyogenic effusion combined with bilateral pneumothorax may occur.
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Affiliation(s)
- Tamara I Kalenchic
- Department of Medical Rehabilitation and Physiotherapy, Belarusian State Medical University, Dzerzhinsky Ave., 83, Minsk, Belarus. 220116
| | - Sergey L Kabak
- Head of the Human Morphology Department, Belarusian State Medical University, Dzerzhinsky Ave., 83, Minsk, Belarus. 220116
| | - Sergey V Primak
- Pulmonologist of the Pulmonary department no.1 of the 6th City Clinical Hospital, Uralskaya Str. 5, Minsk, Belarus. 220037
| | - Yuliya M Melnichenko
- Human Morphology Department, Belarusian State Medical University, Dzerzhinsky Ave., 83, Minsk, Belarus. 220116
| | - O A Kudelich
- Department of Surgical Diseases, Belarusian State Medical University, Dzerzhinsky Ave., 83, Minsk, Belarus. 220116
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Al-Jassas HK, Al-Hakeim HK, Maes M. Intersections between pneumonia, lowered oxygen saturation percentage and immune activation mediate depression, anxiety, and chronic fatigue syndrome-like symptoms due to COVID-19: A nomothetic network approach. J Affect Disord 2022; 297:233-245. [PMID: 34699853 PMCID: PMC8541833 DOI: 10.1016/j.jad.2021.10.039] [Citation(s) in RCA: 32] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/11/2021] [Revised: 09/28/2021] [Accepted: 10/20/2021] [Indexed: 02/08/2023]
Abstract
BACKGROUND COVID-19 is associated with neuropsychiatric symptoms including increased depressive, anxiety and chronic fatigue-syndrome (CFS)-like and physiosomatic symptoms. AIMS To delineate the associations between affective and CFS-like symptoms in COVID-19 and chest computed tomography scan anomalies (CCTAs), oxygen saturation (SpO2), interleukin (IL)-6, IL-10, C-Reactive Protein (CRP), albumin, calcium, magnesium, soluble angiotensin converting enzyme (ACE2) and soluble advanced glycation products (sRAGEs). METHOD The above biomarkers were assessed in 60 COVID-19 patients and 30 healthy controls who had measurements of the Hamilton Depression (HDRS) and Anxiety (HAM-A) and the Fibromyalgia and Chronic Fatigue (FF) Rating Scales. RESULTS Partial Least Squares-SEM analysis showed that reliable latent vectors could be extracted from a) key depressive and anxiety and physiosomatic symptoms (the physio-affective or PA-core), b) IL-6, IL-10, CRP, albumin, calcium, and sRAGEs (the immune response core); and c) different CCTAs (including ground glass opacities, consolidation, and crazy paving) and lowered SpO2% (lung lesions). PLS showed that 70.0% of the variance in the PA-core was explained by the regression on the immune response and lung lesions latent vectors. One common "infection-immune-inflammatory (III) core" underpins pneumonia-associated CCTAs, lowered SpO2 and immune activation, and this III core explains 70% of the variance in the PA core, and a relevant part of the variance in melancholia, insomnia, and neurocognitive symptoms. DISCUSSION Acute SARS-CoV-2 infection is accompanied by lung lesions and lowered SpO2 which may cause activated immune-inflammatory pathways, which mediate the effects of the former on the PA-core and other neuropsychiatric symptoms due to SARS-CoV-2 infection.
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Affiliation(s)
| | | | - Michael Maes
- School of Medicine, IMPACT-the Institute for Mental and Physical Health and Clinical Translation, Deakin University, Barwon Health, Geelong, Australia; Department of Psychiatry, Medical University of Plovdiv, Plovdiv, Bulgaria; Department of Psychiatry, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand.
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Bellini MI, Fresilli D, Lauro A, Mennini G, Rossi M, Catalano C, D'Andrea V, Cantisani V. Liver Transplant Imaging prior to and during the COVID-19 Pandemic. BIOMED RESEARCH INTERNATIONAL 2022; 2022:7768383. [PMID: 35036437 PMCID: PMC8753253 DOI: 10.1155/2022/7768383] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 06/04/2021] [Accepted: 12/23/2021] [Indexed: 02/08/2023]
Abstract
BACKGROUND The suspension of the surgical activity, the burden of the infection in immunosuppressed patients, and the comorbidities underlying end-stage organ disease have impacted transplant programs significantly, even life-saving procedures, such as liver transplantation. METHODS A review of the literature was conducted to explore the challenges faced by transplant programs and the adopted strategies to overcome them, with a focus on indications for imaging in liver transplant candidates. RESULTS Liver transplantation relies on an appropriate imaging method for its success. During the Coronavirus Disease 2019 (COVID-19) pandemic, chest CT showed an additional value to detect early signs of SARS-CoV-2 infection and other screening modalities are less accurate than radiology. CONCLUSION There is an emerging recognition of the chest CT value to recommend its use and help COVID-19 detection in patients. This examination appears highly sensitive for liver transplant candidates and recipients, who otherwise would have not undergone it, particularly when asymptomatic.
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Affiliation(s)
| | - Daniele Fresilli
- Department of Radiological, Oncological, Anatomo-Pathological Sciences, Sapienza University of Rome, Rome, Italy
| | - Augusto Lauro
- Department of Surgical Sciences, Sapienza University of Rome, Rome, Italy
| | - Gianluca Mennini
- Department of Hepato-Bilopancreatic and Transplant Surgery, Sapienza University of Rome, Rome, Italy
| | - Massimo Rossi
- Department of Hepato-Bilopancreatic and Transplant Surgery, Sapienza University of Rome, Rome, Italy
| | - Carlo Catalano
- Department of Radiological, Oncological, Anatomo-Pathological Sciences, Sapienza University of Rome, Rome, Italy
| | - Vito D'Andrea
- Department of Surgical Sciences, Sapienza University of Rome, Rome, Italy
| | - Vito Cantisani
- Department of Radiological, Oncological, Anatomo-Pathological Sciences, Sapienza University of Rome, Rome, Italy
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Filippenko Y, Zagurovskaya M, Abdrakhmanova A, Kassenova S, Zhakenova Z, Aimakhanova A, Zholdybay Z. Impact of COPD on clinical and CT characteristics of COVID-19-associated pneumonia: single tertiary center experience. THE EGYPTIAN JOURNAL OF RADIOLOGY AND NUCLEAR MEDICINE 2022; 53:245. [PMCID: PMC9707087 DOI: 10.1186/s43055-022-00932-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/02/2022] Open
Abstract
Background The severe acute respiratory syndrome-related coronavirus 2 pandemic continues to this day worldwide. Individuals with COPD are at increased risk of contracting SARS-CoV-2. Most of the conducted studies are based on the clinical assessment of COVID-19 infection with different comorbidities. The specific contribution of COPD to the severity of the disease and outcome still remains the point of investigation. The main goals of our study are to assess COPD’s influence on the severity of clinical and CT characteristics of COVID-19 pneumonia and associated in-hospital mortality. Results This is a retrospective study on 281 patients with RT-PCR-confirmed COVID-19 infection and CT spectrum of COVID-19 pneumonia. Fifty patients have COPD based on CT criteria. No significant difference was observed in the mean hospital length of stay, arterial oxygen saturation on admission or in-hospital mortality between COPD and non-COPD groups. Patients with COPD were two times less likely to have fever less than 37.9 °C (RR = 2.037; 95% CI 1.114–3.724, p = 0.016), but higher absolute neutrophil count (p = 0.033) and median level of neutrophil/lymphocyte ratio (p = 0.029). The COPD group was presented with milder CT severity score (especially CT1, less than 25% of lung involvement) (p = 0.022), less likely to have bilateral (RR = 2.601; 95% CI: 1.341–5.044, p = 0.023) or central (RR = 1.754; 95% CI 1.024–3.003, p = 0.046) distribution of ground-glass opacities, right lower lobe (RR = 2.887; 95% CI 1.574–5.293, p = 0.008) or left lung (RR = 2.627; 95% CI 1.448–4.765, p = 0.009) involvement, and “crazy-paving” pattern (RR = 2.208; 95% CI 1.292–3.774, p = 0.003). Both moderate positive and negative relationship was observed between CT1, CT4, hypoxia and in-hospital mortality in the COPD group (r = − 0.383, p = 0.033; r = 0.486, p = 0.007; r = − 0,354, p = 0,022, respectively). Conclusion The presence of COPD by imaging criteria in the settings of COVID-19-associated pneumonia did not significantly influence the clinical or imaging performance of the patients, nor was it linked to the increased in-hospital mortality.
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Affiliation(s)
- Yevgeniya Filippenko
- grid.443453.10000 0004 0387 8740Department of Diagnostic Radiology, Kazakh National Medical University Named After S.D. Asfendiyarov, Tole bi St. 94, 050000 Almaty, Kazakhstan
| | - Marianna Zagurovskaya
- grid.257413.60000 0001 2287 3919Department of Radiology and Imaging Sciences, Indiana University, Indianapolis, USA
| | | | - Saule Kassenova
- Department of Internal Medicine, Scientific Research Institute of Cardiology and Internal Diseases, Almaty, Kazakhstan
| | - Zhanar Zhakenova
- grid.443453.10000 0004 0387 8740Department of Diagnostic Radiology, Kazakh National Medical University Named After S.D. Asfendiyarov, Tole bi St. 94, 050000 Almaty, Kazakhstan
| | - Aizat Aimakhanova
- grid.443453.10000 0004 0387 8740Department of Biostatistics and Basis of Scientific Analysis, Kazakh National Medical University Named After S.D. Asfendiyarov, Almaty, Kazakhstan
| | - Zhamilya Zholdybay
- grid.443453.10000 0004 0387 8740Department of Diagnostic Radiology, Kazakh National Medical University Named After S.D. Asfendiyarov, Tole bi St. 94, 050000 Almaty, Kazakhstan
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