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Wang M, Luo X, Xiao X, Zhang L, Wang Q, Wang S, Wang X, Xue H, Zhang L, Chen Y, Lei J, Štupnik T, Scarci M, Fiorelli A, Laisaar T, Fruscio R, Elkhayat H, Novoa NM, Davoli F, Waseda R, Estill J, Norris SL, Riley DS, Tian J. CARE-radiology statement explanation and elaboration: reporting guideline for radiological case reports. BMJ Evid Based Med 2024:bmjebm-2023-112695. [PMID: 38458654 DOI: 10.1136/bmjebm-2023-112695] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 02/12/2024] [Indexed: 03/10/2024]
Abstract
Despite the increasing number of radiological case reports, the majority lack a standardised methodology of writing and reporting. We therefore develop a reporting guideline for radiological case reports based on the CAse REport (CARE) statement. We established a multidisciplinary group of experts, comprising 40 radiologists, methodologists, journal editors and researchers, to develop a reporting guideline for radiological case reports according to the methodology recommended by the Enhancing the QUAlity and Transparency Of health Research network. The Delphi panel was requested to evaluate the significance of a list of elements for potential inclusion in a guideline for reporting mediation analyses. By reviewing the reporting guidelines and through discussion, we initially drafted 46 potential items. Following a Delphi survey and discussion, the final CARE-radiology checklist is comprised of 38 items in 16 domains. CARE-radiology is a comprehensive reporting guideline for radiological case reports developed using a rigorous methodology. We hope that compliance with CARE-radiology will help in the future to improve the completeness and quality of case reports in radiology.
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Affiliation(s)
- Mengshu Wang
- Evidence-Based Medicine Center, School of Basic Medical Sciences, Lanzhou University, Lanzhou, China
- Department of Radiology, The First Hospital of Lanzhou University, Lanzhou, China
| | - Xufei Luo
- Evidence-Based Medicine Center, School of Basic Medical Sciences, Lanzhou University, Lanzhou, China
- Research Unit of Evidence-Based Evaluation and Guidelines, Chinese Academy of Medical Sciences (2021RU017), School of Basic Medical Sciences, Lanzhou University, Lanzhou, China
| | - Xiaojuan Xiao
- Department of Radiology, The Eighth Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Shenzhen, China
| | - Linlin Zhang
- Editorial Office of Chinese Journal of Radiology, Beijing, China
| | - Qi Wang
- Health Research Methods, Evidence, and Impact (HEI), McMaster University, Hamilton, ON, Canada
- McMaster Health Forum, McMaster University, Hamilton, ON, Canada
| | - Shiyu Wang
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Ximing Wang
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China
| | - Huadan Xue
- Department of Radiology, Translational Medicine Center, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Longjiang Zhang
- Department of Radiology, Jinling Hospital, Medical School of Nanjing University, General Hospital of Eastern Theater Command, Nanjing, China
| | - Yaolong Chen
- Evidence-Based Medicine Center, School of Basic Medical Sciences, Lanzhou University, Lanzhou, China
- Research Unit of Evidence-Based Evaluation and Guidelines, Chinese Academy of Medical Sciences (2021RU017), School of Basic Medical Sciences, Lanzhou University, Lanzhou, China
- Key Laboratory of Evidence Based Medicine of Gansu Province, Lanzhou, China
- Institute of Health Data Science, Lanzhou University, Lanzhou, China
- World Health Organization Collaboration Center for Guideline Implementation and Knowledge Translation, Lanzhou, China
| | - Junqiang Lei
- Department of Radiology, The First Hospital of Lanzhou University, Lanzhou, China
- Intelligent Imaging Medical Engineering Research Center of Gansu Province, Lanzhou, China
- Accurate Image Collaborative Innovation International Science and Technology Cooperation Base of Gansu Province, Lanzhou, China
| | - Tomaž Štupnik
- Medical Faculty, University of Ljubljana, Ljubljana, Slovenia
| | - Marco Scarci
- Department of Cardiothoracic Surgery, Imperial College Healthcare NHS Trust, London, UK
| | - Alfonso Fiorelli
- Thoracic Surgery Unit, University of Campania "Luigi Vanvitelli", Naples, Italy
| | - Tanel Laisaar
- Department of Thoracic Surgery and Lung Transplantation, Lung Clinic, Tartu University Hospital, Tartu, Estonia
- Lung Clinic, Institute of Clinical Medicine, Medical Faculty, University of Tartu, Tartu, Estonia
| | - Robert Fruscio
- Clinic of Obstetrics and Gynecology, University of Milan-Bicocca, IRCCS San Gerardo, Monza, Italy
| | - Hussein Elkhayat
- Cardiothoracic Surgery Department, Faculty of Medicine, Assiut University, Assiut, Egypt
| | - Nuria M Novoa
- Thoracic Surgery, Puerta de Hierro University Hospital-Majadahonda, Madrid, Spain
- Biomedical Institute of Salamanca, Salamanca, Spain
| | - Fabio Davoli
- General & Thoracic Surgery Unit, IRCCS Istituto Ortopedico Rizzoli, Bologna, Italy
| | - Ryuichi Waseda
- Department of General Thoracic, Breast and Pediatric Surgery, Fukuoka University, Fukuoka, Japan
| | - Janne Estill
- Evidence-Based Medicine Center, School of Basic Medical Sciences, Lanzhou University, Lanzhou, China
- Institute of Global Health, University of Geneva, Geneva, Switzerland
| | - Susan L Norris
- Oregon Health & Science University, Portland, Oregon, USA
| | - David S Riley
- University of New Mexico Medical School, Santa Fe, New Mexico, USA
| | - Jinhui Tian
- Evidence-Based Medicine Center, School of Basic Medical Sciences, Lanzhou University, Lanzhou, China
- Key Laboratory of Evidence Based Medicine of Gansu Province, Lanzhou, China
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He J, Zhao Y, Fu Z, Chen L, Hu K, Lin X, Wang N, Huang W, Xu Q, He S, He Y, Song L, Xia Fang M, Zheng J, Chen B, Cai Q, Fu J, Su J. A novel tree shrew model of lipopolysaccharide-induced acute respiratory distress syndrome. J Adv Res 2024; 56:157-165. [PMID: 37037373 PMCID: PMC10834818 DOI: 10.1016/j.jare.2023.03.009] [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: 05/07/2022] [Revised: 12/20/2022] [Accepted: 03/25/2023] [Indexed: 04/12/2023] Open
Abstract
INTRODUCTION Acute respiratory distress syndrome (ARDS) is a leading cause of respiratory failure, with substantial attributable morbidity and mortality. The small animal models that are currently used for ARDS do not fully manifest all of the pathological hallmarks of human patients, which hampers both the studies of disease mechanism and drug development. OBJECTIVES To examine whether the phenotypic changes of primate-like tree shrews in response to a one-hit lipopolysaccharides (LPS) injury resemble human ARDS features. METHODS LPS was administered to tree shrews through intratracheal instillation; then, the animals underwent CT or PET/CT imaging to examine the changes in the structure and function of the whole lung. The lung histology was analyzed by H&E staining and immunohistochemical staining of inflammatory cells. RESULTS Results demonstrated that tree shrews exhibited an average survival time of 3-5 days after LPS insult, as well as an obvious symptom of dyspnea before death. The ratios of PaO2 to FiO2 (P/F ratio) were close to those of moderate ARDS in humans. CT imaging showed that the scope of the lung injury in tree shrews after LPS treatment were extensive. PET/CT imaging with 18F-FDG displayed an obvious inflammatory infiltration. Histological analysis detected the formation of a hyaline membrane, which is usually present in human ARDS. CONCLUSION This study established a lung injury model with a primate-like small animal model and confirmed that they have similar features to human ARDS, which might provide a valuable tool for translational research.
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Affiliation(s)
- Jun He
- Institute of Laboratory Animal Science, Jinan University, Guangzhou, China.
| | - Yue Zhao
- Institute of Laboratory Animal Science, Jinan University, Guangzhou, China
| | - Zhenli Fu
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Li Chen
- Department of Respiratory and Critical Care Medicine, the First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Kongzhen Hu
- Nanfang PET Center, Department of Nuclear Medicine, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Xiaoyan Lin
- Institute of Laboratory Animal Science, Jinan University, Guangzhou, China
| | - Ning Wang
- Institute of Laboratory Animal Science, Jinan University, Guangzhou, China
| | - Weijian Huang
- Institute of Laboratory Animal Science, Jinan University, Guangzhou, China
| | - Qi Xu
- Institute of Laboratory Animal Science, Jinan University, Guangzhou, China
| | - Shuhua He
- Institute of Laboratory Animal Science, Jinan University, Guangzhou, China
| | - Ying He
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Linliang Song
- Institute of Laboratory Animal Science, Jinan University, Guangzhou, China
| | - Mei Xia Fang
- Institute of Laboratory Animal Science, Jinan University, Guangzhou, China
| | - Jie Zheng
- Department of Food Science and Engineering, Jinan University, Guangzhou, China
| | - Biying Chen
- Radiology Department of the First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Qiuyan Cai
- Department of Respiratory and Critical Care Medicine, the First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Jiangnan Fu
- Institute of Laboratory Animal Science, Jinan University, Guangzhou, China
| | - Jin Su
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
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Bertolini M, Brambilla A, Dallasta S, Colombo G. High-quality chest CT segmentation to assess the impact of COVID-19 disease. Int J Comput Assist Radiol Surg 2021; 16:1737-1747. [PMID: 34357524 PMCID: PMC8343216 DOI: 10.1007/s11548-021-02466-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2021] [Accepted: 07/15/2021] [Indexed: 12/24/2022]
Abstract
Purpose COVID-19 has spread rapidly worldwide since its initial appearance, creating the need for faster diagnostic methods and tools. Due to the high rate of false-negative RT-PCR tests, the role of chest CT examination has been investigated as an auxiliary procedure. The main goal of this work is to establish a well-defined strategy for 3D segmentation of the airways and lungs of COVID-19 positive patients from CT scans, including detected abnormalities. Their identification and the volumetric quantification could allow an easier classification in terms of gravity, extent and progression of the infection. Moreover, these 3D reconstructions can provide a high-impact tool to enhance awareness of the severity of COVID-19 pneumonia. Methods Segmentation process was performed utilizing a proprietary software, starting from six different stacks of chest CT images of subjects with and without COVID-19. In this context, a comparison between manual and automatic segmentation methods of the respiratory system was conducted, to assess the potential value of both techniques, in terms of time consumption, required anatomical knowledge and branch detection, in healthy and pathological conditions. Results High-quality 3D models were obtained. They can be utilized to assess the impact of the pathology, by volumetrically quantifying the extension of the affected areas. Indeed, based on the obtained reconstructions, an attempted classification for each patient in terms of the severity of the COVID-19 infection has been outlined. Conclusions Automatic algorithms allowed for a substantial reduction in segmentation time. However, a great effort was required for the manual identification of COVID-19 CT manifestations. The developed automated procedure succeeded in obtaining sufficiently accurate models of the airways and the lungs of both healthy patients and subjects with confirmed COVID-19, in a reasonable time.
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Affiliation(s)
- Michele Bertolini
- Department of Mechanical Engineering, Politecnico di Milano, Milan, Italy.
| | - Alma Brambilla
- Department of Mechanical Engineering, Politecnico di Milano, Milan, Italy
| | - Samanta Dallasta
- Department of Mechanical Engineering, Politecnico di Milano, Milan, Italy
| | - Giorgio Colombo
- Department of Mechanical Engineering, Politecnico di Milano, Milan, Italy
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Guvener O, Eyidogan A, Oto C, Huri PY. Novel additive manufacturing applications for communicable disease prevention and control: focus on recent COVID-19 pandemic. EMERGENT MATERIALS 2021; 4:351-361. [PMID: 33585795 PMCID: PMC7874037 DOI: 10.1007/s42247-021-00172-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/31/2020] [Accepted: 01/24/2021] [Indexed: 05/02/2023]
Abstract
COVID-19 disease caused by the SARS-CoV-2 virus has had serious adverse effects globally in 2020 which are foreseen to extend in 2021, as well. The most important of these effects was exceeding the capacity of the healthcare infrastructures, and the related inability to meet the need for various medical equipment especially within the first months of the crisis following the emergence and rapid spreading of the virus. Urgent global demand for the previously unavailable personal protective equipment, sterile disposable medical supplies as well as the active molecules including vaccines and drugs fueled the need for the coordinated efforts of the scientific community. Amid all this confusion, the rapid prototyping technology, 3D printing, has demonstrated its competitive advantage by repositioning its capabilities to respond to the urgent need. Individual and corporate, amateur and professional all makers around the world with 3D printing capacity became united in effort to fill the gap in the supply chain until mass production is available especially for personal protective equipment and other medical supplies. Due to the unexpected, ever-changing nature of the COVID-19 pandemic-like all other potential communicable diseases-the need for rapid design and 3D production of parts and pieces as well as sterile disposable medical equipment and consumables is likely to continue to keep its importance in the upcoming years. This review article summarizes how additive manufacturing technology can contribute to such cases with special focus on the recent COVID-19 pandemic.
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Affiliation(s)
- Orcun Guvener
- Ankara University Medical Design Research and Application Center, MEDITAM, Ankara, Turkey
- Ankara University Faculty of Veterinary Medicine, Department of Anatomy, Ankara, Turkey
| | - Abdullah Eyidogan
- Ankara University Medical Design Research and Application Center, MEDITAM, Ankara, Turkey
- Ankara University Faculty of Engineering, Department of Biomedical Engineering, Ankara, Turkey
| | - Cagdas Oto
- Ankara University Medical Design Research and Application Center, MEDITAM, Ankara, Turkey
- Ankara University Faculty of Veterinary Medicine, Department of Anatomy, Ankara, Turkey
| | - Pinar Yilgor Huri
- Ankara University Medical Design Research and Application Center, MEDITAM, Ankara, Turkey
- Ankara University Faculty of Engineering, Department of Biomedical Engineering, Ankara, Turkey
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