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Sun HJ, Lu QB, Zhu XX, Ni ZR, Su JB, Fu X, Chen G, Zheng GL, Nie XW, Bian JS. Pharmacology of Hydrogen Sulfide and Its Donors in Cardiometabolic Diseases. Pharmacol Rev 2024; 76:846-895. [PMID: 38866561 DOI: 10.1124/pharmrev.123.000928] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Revised: 04/13/2024] [Accepted: 06/10/2024] [Indexed: 06/14/2024] Open
Abstract
Cardiometabolic diseases (CMDs) are major contributors to global mortality, emphasizing the critical need for novel therapeutic interventions. Hydrogen sulfide (H2S) has garnered enormous attention as a significant gasotransmitter with various physiological, pathophysiological, and pharmacological impacts within mammalian cardiometabolic systems. In addition to its roles in attenuating oxidative stress and inflammatory response, burgeoning research emphasizes the significance of H2S in regulating proteins via persulfidation, a well known modification intricately associated with the pathogenesis of CMDs. This review seeks to investigate recent updates on the physiological actions of endogenous H2S and the pharmacological roles of various H2S donors in addressing diverse aspects of CMDs across cellular, animal, and clinical studies. Of note, advanced methodologies, including multiomics, intestinal microflora analysis, organoid, and single-cell sequencing techniques, are gaining traction due to their ability to offer comprehensive insights into biomedical research. These emerging approaches hold promise in characterizing the pharmacological roles of H2S in health and diseases. We will critically assess the current literature to clarify the roles of H2S in diseases while also delineating the opportunities and challenges they present in H2S-based pharmacotherapy for CMDs. SIGNIFICANCE STATEMENT: This comprehensive review covers recent developments in H2S biology and pharmacology in cardiometabolic diseases CMDs. Endogenous H2S and its donors show great promise for the management of CMDs by regulating numerous proteins and signaling pathways. The emergence of new technologies will considerably advance the pharmacological research and clinical translation of H2S.
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Affiliation(s)
- Hai-Jian Sun
- Wuxi School of Medicine, Jiangnan University, Wuxi, Jiangsu, China (H.-J.S., X.-X.Z., Z.-R.N., J.-B.S., X.F., G.C., G.-L.Z.); Department of Endocrinology, Affiliated Hospital of Jiangnan University, Jiangnan University, Wuxi, Jiangsu, China (Q.-B.L.); Shenzhen Key Laboratory of Respiratory Diseases, Shenzhen People's Hospital, Shenzhen, Guangdong, China (X.-W.N.); and Department of Pharmacology, School of Medicine, Southern University of Science and Technology, Shenzhen, Guangdong, China (J.-S.B.)
| | - Qing-Bo Lu
- Wuxi School of Medicine, Jiangnan University, Wuxi, Jiangsu, China (H.-J.S., X.-X.Z., Z.-R.N., J.-B.S., X.F., G.C., G.-L.Z.); Department of Endocrinology, Affiliated Hospital of Jiangnan University, Jiangnan University, Wuxi, Jiangsu, China (Q.-B.L.); Shenzhen Key Laboratory of Respiratory Diseases, Shenzhen People's Hospital, Shenzhen, Guangdong, China (X.-W.N.); and Department of Pharmacology, School of Medicine, Southern University of Science and Technology, Shenzhen, Guangdong, China (J.-S.B.)
| | - Xue-Xue Zhu
- Wuxi School of Medicine, Jiangnan University, Wuxi, Jiangsu, China (H.-J.S., X.-X.Z., Z.-R.N., J.-B.S., X.F., G.C., G.-L.Z.); Department of Endocrinology, Affiliated Hospital of Jiangnan University, Jiangnan University, Wuxi, Jiangsu, China (Q.-B.L.); Shenzhen Key Laboratory of Respiratory Diseases, Shenzhen People's Hospital, Shenzhen, Guangdong, China (X.-W.N.); and Department of Pharmacology, School of Medicine, Southern University of Science and Technology, Shenzhen, Guangdong, China (J.-S.B.)
| | - Zhang-Rong Ni
- Wuxi School of Medicine, Jiangnan University, Wuxi, Jiangsu, China (H.-J.S., X.-X.Z., Z.-R.N., J.-B.S., X.F., G.C., G.-L.Z.); Department of Endocrinology, Affiliated Hospital of Jiangnan University, Jiangnan University, Wuxi, Jiangsu, China (Q.-B.L.); Shenzhen Key Laboratory of Respiratory Diseases, Shenzhen People's Hospital, Shenzhen, Guangdong, China (X.-W.N.); and Department of Pharmacology, School of Medicine, Southern University of Science and Technology, Shenzhen, Guangdong, China (J.-S.B.)
| | - Jia-Bao Su
- Wuxi School of Medicine, Jiangnan University, Wuxi, Jiangsu, China (H.-J.S., X.-X.Z., Z.-R.N., J.-B.S., X.F., G.C., G.-L.Z.); Department of Endocrinology, Affiliated Hospital of Jiangnan University, Jiangnan University, Wuxi, Jiangsu, China (Q.-B.L.); Shenzhen Key Laboratory of Respiratory Diseases, Shenzhen People's Hospital, Shenzhen, Guangdong, China (X.-W.N.); and Department of Pharmacology, School of Medicine, Southern University of Science and Technology, Shenzhen, Guangdong, China (J.-S.B.)
| | - Xiao Fu
- Wuxi School of Medicine, Jiangnan University, Wuxi, Jiangsu, China (H.-J.S., X.-X.Z., Z.-R.N., J.-B.S., X.F., G.C., G.-L.Z.); Department of Endocrinology, Affiliated Hospital of Jiangnan University, Jiangnan University, Wuxi, Jiangsu, China (Q.-B.L.); Shenzhen Key Laboratory of Respiratory Diseases, Shenzhen People's Hospital, Shenzhen, Guangdong, China (X.-W.N.); and Department of Pharmacology, School of Medicine, Southern University of Science and Technology, Shenzhen, Guangdong, China (J.-S.B.)
| | - Guo Chen
- Wuxi School of Medicine, Jiangnan University, Wuxi, Jiangsu, China (H.-J.S., X.-X.Z., Z.-R.N., J.-B.S., X.F., G.C., G.-L.Z.); Department of Endocrinology, Affiliated Hospital of Jiangnan University, Jiangnan University, Wuxi, Jiangsu, China (Q.-B.L.); Shenzhen Key Laboratory of Respiratory Diseases, Shenzhen People's Hospital, Shenzhen, Guangdong, China (X.-W.N.); and Department of Pharmacology, School of Medicine, Southern University of Science and Technology, Shenzhen, Guangdong, China (J.-S.B.)
| | - Guan-Li Zheng
- Wuxi School of Medicine, Jiangnan University, Wuxi, Jiangsu, China (H.-J.S., X.-X.Z., Z.-R.N., J.-B.S., X.F., G.C., G.-L.Z.); Department of Endocrinology, Affiliated Hospital of Jiangnan University, Jiangnan University, Wuxi, Jiangsu, China (Q.-B.L.); Shenzhen Key Laboratory of Respiratory Diseases, Shenzhen People's Hospital, Shenzhen, Guangdong, China (X.-W.N.); and Department of Pharmacology, School of Medicine, Southern University of Science and Technology, Shenzhen, Guangdong, China (J.-S.B.)
| | - Xiao-Wei Nie
- Wuxi School of Medicine, Jiangnan University, Wuxi, Jiangsu, China (H.-J.S., X.-X.Z., Z.-R.N., J.-B.S., X.F., G.C., G.-L.Z.); Department of Endocrinology, Affiliated Hospital of Jiangnan University, Jiangnan University, Wuxi, Jiangsu, China (Q.-B.L.); Shenzhen Key Laboratory of Respiratory Diseases, Shenzhen People's Hospital, Shenzhen, Guangdong, China (X.-W.N.); and Department of Pharmacology, School of Medicine, Southern University of Science and Technology, Shenzhen, Guangdong, China (J.-S.B.)
| | - Jin-Song Bian
- Wuxi School of Medicine, Jiangnan University, Wuxi, Jiangsu, China (H.-J.S., X.-X.Z., Z.-R.N., J.-B.S., X.F., G.C., G.-L.Z.); Department of Endocrinology, Affiliated Hospital of Jiangnan University, Jiangnan University, Wuxi, Jiangsu, China (Q.-B.L.); Shenzhen Key Laboratory of Respiratory Diseases, Shenzhen People's Hospital, Shenzhen, Guangdong, China (X.-W.N.); and Department of Pharmacology, School of Medicine, Southern University of Science and Technology, Shenzhen, Guangdong, China (J.-S.B.)
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2
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Chen CH, Tsai PJ, Chang WW, Chen CY, Chen CY, Guo YL. Subpleural curvilinear lines as an early indicator of silicosis in artificial stone workers. Pulmonology 2024; 30:174-176. [PMID: 37743171 DOI: 10.1016/j.pulmoe.2023.08.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Revised: 07/24/2023] [Accepted: 08/03/2023] [Indexed: 09/26/2023] Open
Affiliation(s)
- C H Chen
- Department of Environmental and Occupational Medicine, National Taiwan University (NTU) College of medicine and NTU Hospital, No. 7, Zhongshan S. Rd., Zhongzheng Dist., Taipei City, Taiwan
| | - P J Tsai
- Department of Environmental and Occupational Health, College of Medicine, National Cheng Kung University, 138 Sheng-Li Rd., North District, Tainan, 70403, Taiwan
| | - W W Chang
- Department of Environmental and Occupational Health, College of Medicine, National Cheng Kung University, 138 Sheng-Li Rd., North District, Tainan, 70403, Taiwan
| | - C Y Chen
- Division of Occupational Hazards Assessment, Institute of Labor, Occupational Safety and Health, Ministry of Labor, No. 99, Ln. 407, Hengke Rd., Xizhi Dist., New Taipei City, 221004 , Taiwan
| | - C Y Chen
- Division of Occupational Hazards Assessment, Institute of Labor, Occupational Safety and Health, Ministry of Labor, No. 99, Ln. 407, Hengke Rd., Xizhi Dist., New Taipei City, 221004 , Taiwan
| | - Y L Guo
- Department of Environmental and Occupational Medicine, National Taiwan University (NTU) College of medicine and NTU Hospital, No. 7, Zhongshan S. Rd., Zhongzheng Dist., Taipei City, Taiwan; Institute of Environmental and Occupational Health Sciences, National Taiwan University College of Public Health, No. 17, Xuzhou Rd., Zhongzheng Dist., Taipei City, Taiwan; National Institute of Environmental Sciences, National Health Research Institutes, No. 35, Keyan Rd., Zhunan Township, Miaoli County, Taiwan.
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3
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Guo K, Cheng J, Li K, Wang L, Lv Y, Cao D. Diagnosis and detection of pneumonia using weak-label based on X-ray images: a multi-center study. BMC Med Imaging 2023; 23:209. [PMID: 38087255 PMCID: PMC10717871 DOI: 10.1186/s12880-023-01174-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Accepted: 12/05/2023] [Indexed: 12/18/2023] Open
Abstract
PURPOSE Development and assessment the deep learning weakly supervised algorithm for the classification and detection pneumonia via X-ray. METHODS This retrospective study analyzed two publicly available dataset that contain X-ray images of pneumonia cases and normal cases. The first dataset from Guangzhou Women and Children's Medical Center. It contains a total of 5,856 X-ray images, which are divided into training, validation, and test sets with 8:1:1 ratio for algorithm training and testing. The deep learning algorithm ResNet34 was employed to build diagnostic model. And the second public dataset were collated by researchers from Qatar University and the University of Dhaka along with collaborators from Pakistan and Malaysia and some medical doctors. A total of 1,300 images of COVID-19 positive cases, 1,300 normal images and 1,300 images of viral pneumonia for external validation. Class activation map (CAM) were used to location the pneumonia lesions. RESULTS The ResNet34 model for pneumonia detection achieved an AUC of 0.9949 [0.9910-0.9981] (with an accuracy of 98.29% a sensitivity of 99.29% and a specificity of 95.57%) in the test dataset. And for external validation dataset, the model obtained an AUC of 0.9835[0.9806-0.9864] (with an accuracy of 94.62%, a sensitivity of 92.35% and a specificity of 99.15%). Moreover, the CAM can accurately locate the pneumonia area. CONCLUSION The deep learning algorithm can accurately detect pneumonia and locate the pneumonia area based on weak supervision information, which can provide potential value for helping radiologists to improve their accuracy of detection pneumonia patients through X-ray images.
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Affiliation(s)
- Kairou Guo
- Department of Biomedical Engineering, Chinese PLA General Hospital, Beijing, 100853, P.R. China
| | - Jiangbo Cheng
- Department of Biomedical Engineering, Chinese PLA General Hospital, Beijing, 100853, P.R. China
| | - Kaiyuan Li
- Department of Biomedical Engineering, Chinese PLA General Hospital, Beijing, 100853, P.R. China
| | - Lanhui Wang
- Department of Biomedical Engineering, Chinese PLA General Hospital, Beijing, 100853, P.R. China
| | - Yadong Lv
- Department of Biomedical Engineering, Chinese PLA General Hospital, Beijing, 100853, P.R. China
| | - Desen Cao
- Department of Biomedical Engineering, Chinese PLA General Hospital, Beijing, 100853, P.R. China.
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Meng F, Kottlors J, Shahzad R, Liu H, Fervers P, Jin Y, Rinneburger M, Le D, Weisthoff M, Liu W, Ni M, Sun Y, An L, Huai X, Móré D, Giannakis A, Kaltenborn I, Bucher A, Maintz D, Zhang L, Thiele F, Li M, Perkuhn M, Zhang H, Persigehl T. AI support for accurate and fast radiological diagnosis of COVID-19: an international multicenter, multivendor CT study. Eur Radiol 2023; 33:4280-4291. [PMID: 36525088 PMCID: PMC9755771 DOI: 10.1007/s00330-022-09335-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2022] [Revised: 11/03/2022] [Accepted: 11/29/2022] [Indexed: 12/23/2022]
Abstract
OBJECTIVES Differentiation between COVID-19 and community-acquired pneumonia (CAP) in computed tomography (CT) is a task that can be performed by human radiologists and artificial intelligence (AI). The present study aims to (1) develop an AI algorithm for differentiating COVID-19 from CAP and (2) evaluate its performance. (3) Evaluate the benefit of using the AI result as assistance for radiological diagnosis and the impact on relevant parameters such as accuracy of the diagnosis, diagnostic time, and confidence. METHODS We included n = 1591 multicenter, multivendor chest CT scans and divided them into AI training and validation datasets to develop an AI algorithm (n = 991 CT scans; n = 462 COVID-19, and n = 529 CAP) from three centers in China. An independent Chinese and German test dataset of n = 600 CT scans from six centers (COVID-19 / CAP; n = 300 each) was used to test the performance of eight blinded radiologists and the AI algorithm. A subtest dataset (180 CT scans; n = 90 each) was used to evaluate the radiologists' performance without and with AI assistance to quantify changes in diagnostic accuracy, reporting time, and diagnostic confidence. RESULTS The diagnostic accuracy of the AI algorithm in the Chinese-German test dataset was 76.5%. Without AI assistance, the eight radiologists' diagnostic accuracy was 79.1% and increased with AI assistance to 81.5%, going along with significantly shorter decision times and higher confidence scores. CONCLUSION This large multicenter study demonstrates that AI assistance in CT-based differentiation of COVID-19 and CAP increases radiological performance with higher accuracy and specificity, faster diagnostic time, and improved diagnostic confidence. KEY POINTS • AI can help radiologists to get higher diagnostic accuracy, make faster decisions, and improve diagnostic confidence. • The China-German multicenter study demonstrates the advantages of a human-machine interaction using AI in clinical radiology for diagnostic differentiation between COVID-19 and CAP in CT scans.
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Affiliation(s)
- Fanyang Meng
- Department of Radiology, The First Hospital of Ji Lin University, No. 1 Xinmin Street, Changchun, 130012, China
| | - Jonathan Kottlors
- Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Rahil Shahzad
- Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
- Innovative Technology, Philips Healthcare, Aachen, Germany
| | - Haifeng Liu
- Department of Radiology, Wuhan No. 1 Hospital, Wuhan, China
| | - Philipp Fervers
- Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Yinhua Jin
- Department of Radiology, Ningbo Hwamei Hospital, University of Chinese Academy of Sciences, Wuhan, China
| | - Miriam Rinneburger
- Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Dou Le
- Department of Radiology, The First Hospital of Ji Lin University, No. 1 Xinmin Street, Changchun, 130012, China
| | - Mathilda Weisthoff
- Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Wenyun Liu
- Department of Radiology, The First Hospital of Ji Lin University, No. 1 Xinmin Street, Changchun, 130012, China
| | - Mengzhe Ni
- Department of Radiology, The First Hospital of Ji Lin University, No. 1 Xinmin Street, Changchun, 130012, China
| | - Ye Sun
- Department of Radiology, The First Hospital of Ji Lin University, No. 1 Xinmin Street, Changchun, 130012, China
| | - Liying An
- Department of Radiology, The First Hospital of Ji Lin University, No. 1 Xinmin Street, Changchun, 130012, China
| | | | - Dorottya Móré
- Department of Diagnostic and Interventional Radiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Athanasios Giannakis
- Department of Diagnostic and Interventional Radiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Isabel Kaltenborn
- Institute for Diagnostic and Interventional Radiology, Frankfurt University Hospital, Frankfurt, Germany
| | - Andreas Bucher
- Institute for Diagnostic and Interventional Radiology, Frankfurt University Hospital, Frankfurt, Germany
| | - David Maintz
- Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Lei Zhang
- Department of Radiology, The First Hospital of Ji Lin University, No. 1 Xinmin Street, Changchun, 130012, China
| | - Frank Thiele
- Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
- Innovative Technology, Philips Healthcare, Aachen, Germany
| | - Mingyang Li
- Department of Radiology, The First Hospital of Ji Lin University, No. 1 Xinmin Street, Changchun, 130012, China
| | - Michael Perkuhn
- Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
- Innovative Technology, Philips Healthcare, Aachen, Germany
| | - Huimao Zhang
- Department of Radiology, The First Hospital of Ji Lin University, No. 1 Xinmin Street, Changchun, 130012, China.
| | - Thorsten Persigehl
- Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
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Grassi F, Granata V, Fusco R, De Muzio F, Cutolo C, Gabelloni M, Borgheresi A, Danti G, Picone C, Giovagnoni A, Miele V, Gandolfo N, Barile A, Nardone V, Grassi R. Radiation Recall Pneumonitis: The Open Challenge in Differential Diagnosis of Pneumonia Induced by Oncological Treatments. J Clin Med 2023; 12:jcm12041442. [PMID: 36835977 PMCID: PMC9964719 DOI: 10.3390/jcm12041442] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2023] [Revised: 02/06/2023] [Accepted: 02/07/2023] [Indexed: 02/17/2023] Open
Abstract
The treatment of primary and secondary lung neoplasms now sees the fundamental role of radiotherapy, associated with surgery and systemic therapies. The improvement in survival outcomes has also increased attention to the quality of life, treatment compliance and the management of side effects. The role of imaging is not only limited to recognizing the efficacy of treatment but also to identifying, as soon as possible, the uncommon effects, especially when more treatments, such as chemotherapy, immunotherapy and radiotherapy, are associated. Radiation recall pneumonitis is an uncommon treatment complication that should be correctly characterized, and it is essential to recognize the mechanisms of radiation recall pneumonitis pathogenesis and diagnostic features in order to promptly identify them and adopt the best therapeutic strategy, with the shortest possible withdrawal of the current oncological drug. In this setting, artificial intelligence could have a critical role, although a larger patient data set is required.
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Affiliation(s)
- Francesca Grassi
- Division of Radiology, Università Degli Studi Della Campania Luigi Vanvitelli, 80127 Naples, Italy
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, Via della Signora 2, 20122 Milan, Italy
| | - Vincenza Granata
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy
- Correspondence:
| | - Roberta Fusco
- Medical Oncology Division, Igea SpA, 80015 Naples, Italy
| | - Federica De Muzio
- Diagnostic Imaging Section, Department of Medical and Surgical Sciences & Neurosciences, University of Molise, 86100 Campobasso, Italy
| | - Carmen Cutolo
- Department of Medicine, Surgery and Dentistry, University of Salerno, 84084 Salerno, Italy
| | - Michela Gabelloni
- Department of Translational Research, Diagnostic and Interventional Radiology, University of Pisa, 56126 Pisa, Italy
| | - Alessandra Borgheresi
- Department of Clinical, Special and Dental Sciences, University Politecnica Delle Marche, Via Conca 71, 60126 Ancona, Italy
- Department of Radiology, University Hospital “Azienda Ospedaliera Universitaria delle Marche”, Via Conca 71, 60126 Ancona, Italy
| | - Ginevra Danti
- Department of Radiology, Careggi University Hospital, Largo Brambilla 3, 50134 Florence, Italy
| | - Carmine Picone
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy
| | - Andrea Giovagnoni
- Department of Clinical, Special and Dental Sciences, University Politecnica Delle Marche, Via Conca 71, 60126 Ancona, Italy
- Department of Radiology, University Hospital “Azienda Ospedaliera Universitaria delle Marche”, Via Conca 71, 60126 Ancona, Italy
| | - Vittorio Miele
- Department of Radiology, Careggi University Hospital, Largo Brambilla 3, 50134 Florence, Italy
| | - Nicoletta Gandolfo
- Diagnostic Imaging Department, Villa Scassi Hospital-ASL 3, Corso Scassi 1, 16149 Genoa, Italy
| | - Antonio Barile
- Department of Applied Clinical Sciences and Biotechnology, University of L’Aquila, Via Vetoio 1, 67100 L’Aquila, Italy
| | - Valerio Nardone
- Division of Radiology, Università Degli Studi Della Campania Luigi Vanvitelli, 80127 Naples, Italy
| | - Roberta Grassi
- Division of Radiology, Università Degli Studi Della Campania Luigi Vanvitelli, 80127 Naples, Italy
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Kottlors J, Fervers P, Geißen S, Gertz RJ, Bremm J, Rinneburger M, Weisthoff M, Shahzad R, Maintz D, Persigehl T. Morphological appearance of the B.1.1.7 mutation of the novel coronavirus 2 (SARS-CoV-2) in chest CT. Quant Imaging Med Surg 2023; 13:1058-1070. [PMID: 36819239 PMCID: PMC9929392 DOI: 10.21037/qims-22-718] [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: 07/07/2022] [Accepted: 12/01/2022] [Indexed: 01/15/2023]
Abstract
Background Diagnosing a coronavirus disease 2019 (COVID-19) infection with high specificity in chest computed tomography (CT) imaging is considered possible due to distinctive imaging features of COVID-19 pneumonia. Since other viral non-COVID pneumonia show mostly a different distribution pattern, it is reasonable to assume that the patterns observed caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are a consequence of its genetically encoded molecular properties when interacting with the respiratory tissue. As more mutations of the initial SARS-CoV-2 wild-type with varying aggressiveness have been detected in the course of 2021, it became obvious that its genome is in a state of transformation and therefore a potential modification of the specific morphological appearance in CT may occur. The aim of this study was to quantitatively analyze the morphological differences of the SARS-CoV-2-B.1.1.7 mutation and wildtype variant in CT scans of the thorax. Methods We analyzed a dataset of 140 patients, which was divided into pneumonias caused by n=40 wildtype variants, n=40 B.1.1.7 variants, n=20 bacterial pneumonias, n=20 viral (non-COVID) pneumonias, and a test group of n=20 unremarkable CT examinations of the thorax. Semiautomated 3D segmentation of the lung tissue was performed for quantification of lung pathologies. The extent, ratio, and specific distribution of inflammatory affected lung tissue in each group were compared in a multivariate group analysis. Results Lung segmentation revealed significant difference between the extent of ground glass opacities (GGO) or consolidation comparing SARS-CoV-2 wild-type and B.1.1.7 variant. Wildtype and B.1.1.7 variant showed both a symmetric distribution pattern of stage-dependent GGO and consolidation within matched COVID-19 stages. Viral non-COVID pneumonias had significantly fewer consolidations than the bacterial, but also than the COVID-19 B.1.1.7 variant groups. Conclusions CT based segmentation showed no significant difference between the morphological appearance of the COVID-19 wild-type variant and the SARS-CoV-2 B.1.1.7 mutation. However, our approach allowed a semiautomatic quantification of bacterial and viral lung pathologies. Quantitative CT image analyses, such as the one presented, appear to be an important component of pandemic preparedness considering an organism with ongoing genetic change, to describe a potential arising change in CT morphological appearance of possible new upcoming COVID-19 variants of concern.
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Affiliation(s)
- Jonathan Kottlors
- Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne (UOC), Cologne, Germany
| | - Philipp Fervers
- Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne (UOC), Cologne, Germany
| | - Simon Geißen
- Division of Cardiology, Pneumology, Angiology and Intensive Care, University of Cologne (UOC), Cologne, Germany
| | - Roman Johannes Gertz
- Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne (UOC), Cologne, Germany
| | - Johannes Bremm
- Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne (UOC), Cologne, Germany
| | - Miriam Rinneburger
- Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne (UOC), Cologne, Germany
| | - Mathilda Weisthoff
- Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne (UOC), Cologne, Germany
| | - Rahil Shahzad
- Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne (UOC), Cologne, Germany;,Innovative Technology, Philips Healthcare, Aachen, Germany
| | - David Maintz
- Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne (UOC), Cologne, Germany
| | - Thorsten Persigehl
- Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne (UOC), Cologne, Germany
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7
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Granata V, Fusco R, Villanacci A, Magliocchetti S, Urraro F, Tetaj N, Marchioni L, Albarello F, Campioni P, Cristofaro M, Di Stefano F, Fusco N, Petrone A, Schininà V, Grassi F, Girardi E, Ianniello S. Imaging Severity COVID-19 Assessment in Vaccinated and Unvaccinated Patients: Comparison of the Different Variants in a High Volume Italian Reference Center. J Pers Med 2022; 12:955. [PMID: 35743740 PMCID: PMC9224665 DOI: 10.3390/jpm12060955] [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: 04/28/2022] [Revised: 06/03/2022] [Accepted: 06/09/2022] [Indexed: 11/17/2022] Open
Abstract
Purpose: To analyze the vaccine effect by comparing five groups: unvaccinated patients with Alpha variant, unvaccinated patients with Delta variant, vaccinated patients with Delta variant, unvaccinated patients with Omicron variant, and vaccinated patients with Omicron variant, assessing the “gravity” of COVID-19 pulmonary involvement, based on CT findings in critically ill patients admitted to Intensive Care Unit (ICU). Methods: Patients were selected by ICU database considering the period from December 2021 to 23 March 2022, according to the following inclusion criteria: patients with proven Omicron variant COVID-19 infection with known COVID-19 vaccination with at least two doses and with chest Computed Tomography (CT) study during ICU hospitalization. Wee also evaluated the ICU database considering the period from March 2020 to December 2021, to select unvaccinated consecutive patients with Alpha variant, subjected to CT study, consecutive unvaccinated and vaccinated patients with Delta variant, subjected to CT study, and, consecutive unvaccinated patients with Omicron variant, subjected to CT study. CT images were evaluated qualitatively using a severity score scale of 5 levels (none involvement, mild: ≤25% of involvement, moderate: 26−50% of involvement, severe: 51−75% of involvement, and critical involvement: 76−100%) and quantitatively, using the Philips IntelliSpace Portal clinical application CT COPD computer tool. For each patient the lung volumetry was performed identifying the percentage value of aerated residual lung volume. Non-parametric tests for continuous and categorical variables were performed to assess statistically significant differences among groups. Results: The patient study group was composed of 13 vaccinated patients affected by the Omicron variant (Omicron V). As control groups we identified: 20 unvaccinated patients with Alpha variant (Alpha NV); 20 unvaccinated patients with Delta variant (Delta NV); 18 vaccinated patients with Delta variant (Delta V); and 20 unvaccinated patients affected by the Omicron variant (Omicron NV). No differences between the groups under examination were found (p value > 0.05 at Chi square test) in terms of risk factors (age, cardiovascular diseases, diabetes, immunosuppression, chronic kidney, cardiac, pulmonary, neurologic, and liver disease, etc.). A different median value of aerated residual lung volume was observed in the Delta variant groups: median value of aerated residual lung volume was 46.70% in unvaccinated patients compared to 67.10% in vaccinated patients. In addition, in patients with Delta variant every other extracted volume by automatic tool showed a statistically significant difference between vaccinated and unvaccinated group. Statistically significant differences were observed for each extracted volume by automatic tool between unvaccinated patients affected by Alpha variant and vaccinated patients affected by Delta variant of COVID-19. Good statistically significant correlations among volumes extracted by automatic tool for each lung lobe and overall radiological severity score were obtained (ICC range 0.71−0.86). GGO was the main sign of COVID-19 lesions on CT images found in 87 of the 91 (95.6%) patients. No statistically significant differences were observed in CT findings (ground glass opacities (GGO), consolidation or crazy paving sign) among patient groups. Conclusion: In our study, we showed that in critically ill patients no difference were observed in terms of severity of disease or exitus, between unvaccinated and vaccinated patients. The only statistically significant differences were observed, with regard to the severity of COVID-19 pulmonary parenchymal involvement, between unvaccinated patients affected by Alpha variant and vaccinated patients affected by Delta variant, and between unvaccinated patients with Delta variant and vaccinated patients with Delta variant.
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Affiliation(s)
- Vincenza Granata
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy;
| | - Roberta Fusco
- Medical Oncology Division, Igea SpA, 80013 Napoli, Italy
| | - Alberta Villanacci
- Diagnostic Imaging of Infectious Diseases, National Institute for Infectious Diseases Lazzaro Spallanzani IRCCS, 00149 Rome, Italy; (A.V.); (F.A.); (P.C.); (M.C.); (F.D.S.); (N.F.); (A.P.); (V.S.); (S.I.)
| | - Simona Magliocchetti
- Division of Radiology, Università degli Studi della Campania Luigi Vanvitelli, 80128 Naples, Italy; (S.M.); (F.U.); (F.G.)
| | - Fabrizio Urraro
- Division of Radiology, Università degli Studi della Campania Luigi Vanvitelli, 80128 Naples, Italy; (S.M.); (F.U.); (F.G.)
| | - Nardi Tetaj
- Intensive Care Unit, National Institute for Infectious Diseases Lazzaro Spallanzani IRCCS, 00149 Rome, Italy; (N.T.); (L.M.)
| | - Luisa Marchioni
- Intensive Care Unit, National Institute for Infectious Diseases Lazzaro Spallanzani IRCCS, 00149 Rome, Italy; (N.T.); (L.M.)
| | - Fabrizio Albarello
- Diagnostic Imaging of Infectious Diseases, National Institute for Infectious Diseases Lazzaro Spallanzani IRCCS, 00149 Rome, Italy; (A.V.); (F.A.); (P.C.); (M.C.); (F.D.S.); (N.F.); (A.P.); (V.S.); (S.I.)
| | - Paolo Campioni
- Diagnostic Imaging of Infectious Diseases, National Institute for Infectious Diseases Lazzaro Spallanzani IRCCS, 00149 Rome, Italy; (A.V.); (F.A.); (P.C.); (M.C.); (F.D.S.); (N.F.); (A.P.); (V.S.); (S.I.)
| | - Massimo Cristofaro
- Diagnostic Imaging of Infectious Diseases, National Institute for Infectious Diseases Lazzaro Spallanzani IRCCS, 00149 Rome, Italy; (A.V.); (F.A.); (P.C.); (M.C.); (F.D.S.); (N.F.); (A.P.); (V.S.); (S.I.)
| | - Federica Di Stefano
- Diagnostic Imaging of Infectious Diseases, National Institute for Infectious Diseases Lazzaro Spallanzani IRCCS, 00149 Rome, Italy; (A.V.); (F.A.); (P.C.); (M.C.); (F.D.S.); (N.F.); (A.P.); (V.S.); (S.I.)
| | - Nicoletta Fusco
- Diagnostic Imaging of Infectious Diseases, National Institute for Infectious Diseases Lazzaro Spallanzani IRCCS, 00149 Rome, Italy; (A.V.); (F.A.); (P.C.); (M.C.); (F.D.S.); (N.F.); (A.P.); (V.S.); (S.I.)
| | - Ada Petrone
- Diagnostic Imaging of Infectious Diseases, National Institute for Infectious Diseases Lazzaro Spallanzani IRCCS, 00149 Rome, Italy; (A.V.); (F.A.); (P.C.); (M.C.); (F.D.S.); (N.F.); (A.P.); (V.S.); (S.I.)
| | - Vincenzo Schininà
- Diagnostic Imaging of Infectious Diseases, National Institute for Infectious Diseases Lazzaro Spallanzani IRCCS, 00149 Rome, Italy; (A.V.); (F.A.); (P.C.); (M.C.); (F.D.S.); (N.F.); (A.P.); (V.S.); (S.I.)
| | - Francesca Grassi
- Division of Radiology, Università degli Studi della Campania Luigi Vanvitelli, 80128 Naples, Italy; (S.M.); (F.U.); (F.G.)
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, Via Della Signora 2, 20122 Milan, Italy
| | - Enrico Girardi
- Department of Epidemiology and Research, National Institute for Infectious Diseases Lazzaro Spallanzani IRCCS, 00149 Rome, Italy;
| | - Stefania Ianniello
- Diagnostic Imaging of Infectious Diseases, National Institute for Infectious Diseases Lazzaro Spallanzani IRCCS, 00149 Rome, Italy; (A.V.); (F.A.); (P.C.); (M.C.); (F.D.S.); (N.F.); (A.P.); (V.S.); (S.I.)
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8
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Fusco R, Simonetti I, Ianniello S, Villanacci A, Grassi F, Dell’Aversana F, Grassi R, Cozzi D, Bicci E, Palumbo P, Borgheresi A, Giovagnoni A, Miele V, Barile A, Granata V. Pulmonary Lymphangitis Poses a Major Challenge for Radiologists in an Oncological Setting during the COVID-19 Pandemic. J Pers Med 2022; 12:624. [PMID: 35455740 PMCID: PMC9024504 DOI: 10.3390/jpm12040624] [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: 03/13/2022] [Revised: 03/29/2022] [Accepted: 03/31/2022] [Indexed: 12/17/2022] Open
Abstract
Due to the increasing number of COVID-19-infected and vaccinated individuals, radiologists continue to see patients with COVID-19 pneumonitis and recall pneumonitis, which could result in additional workups and false-positive results. Moreover, cancer patients undergoing immunotherapy may show therapy-related pneumonitis during imaging management. This is otherwise known as immune checkpoint inhibitor-related pneumonitis. Following on from this background, radiologists should seek to know their patients' COVID-19 infection and vaccination history. Knowing the imaging features related to COVID-19 infection and vaccination is critical to avoiding misleading results and alarmism in patients and clinicians.
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Affiliation(s)
- Roberta Fusco
- Medical Oncology Division, Igea SpA, 80013 Napoli, Italy;
| | - Igino Simonetti
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy;
| | - Stefania Ianniello
- Diagnostica per Immagini nelle Malattie Infettive INMI Spallanzani IRCCS, 00161 Rome, Italy; (S.I.); (A.V.)
| | - Alberta Villanacci
- Diagnostica per Immagini nelle Malattie Infettive INMI Spallanzani IRCCS, 00161 Rome, Italy; (S.I.); (A.V.)
| | - Francesca Grassi
- Division of Radiology, Università degli Studi della Campania Luigi Vanvitelli, 80127 Naples, Italy; (F.G.); (F.D.); (R.G.)
| | - Federica Dell’Aversana
- Division of Radiology, Università degli Studi della Campania Luigi Vanvitelli, 80127 Naples, Italy; (F.G.); (F.D.); (R.G.)
| | - Roberta Grassi
- Division of Radiology, Università degli Studi della Campania Luigi Vanvitelli, 80127 Naples, Italy; (F.G.); (F.D.); (R.G.)
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, Via della Signora 2, 20122 Milan, Italy; (D.C.); (E.B.); (A.B.); (A.G.); (V.M.)
| | - Diletta Cozzi
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, Via della Signora 2, 20122 Milan, Italy; (D.C.); (E.B.); (A.B.); (A.G.); (V.M.)
- Department of Radiology, Azienda Ospedaliero-Universitaria Careggi, 50134 Florence, Italy
| | - Eleonora Bicci
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, Via della Signora 2, 20122 Milan, Italy; (D.C.); (E.B.); (A.B.); (A.G.); (V.M.)
- Department of Radiology, Azienda Ospedaliero-Universitaria Careggi, 50134 Florence, Italy
| | - Pierpaolo Palumbo
- Abruzzo Health Unit 1, Department of Diagnostic Imaging, Area of Cardiovascular and Interventional Imaging, 67100 L’Aquila, Italy;
| | - Alessandra Borgheresi
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, Via della Signora 2, 20122 Milan, Italy; (D.C.); (E.B.); (A.B.); (A.G.); (V.M.)
- Department of Clinical, Special and Dental Sciences, Marche Polytechnic University, 60126 Ancona, Italy
| | - Andrea Giovagnoni
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, Via della Signora 2, 20122 Milan, Italy; (D.C.); (E.B.); (A.B.); (A.G.); (V.M.)
- Department of Clinical, Special and Dental Sciences, Marche Polytechnic University, 60126 Ancona, Italy
| | - Vittorio Miele
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, Via della Signora 2, 20122 Milan, Italy; (D.C.); (E.B.); (A.B.); (A.G.); (V.M.)
- Department of Radiology, Azienda Ospedaliero-Universitaria Careggi, 50134 Florence, Italy
| | - Antonio Barile
- Department of Applied Clinical Science and Biotechnology, University of L’Aquila, Via Vetoio 1, 67100 L’Aquila, Italy;
| | - Vincenza Granata
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy;
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