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1
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Zhang Q, Tao X, Zhao S, Li N, Wang S, Wu N. Association of Clinical and Radiological Features with Disease Severity of Symptomatic Immune Checkpoint Inhibitor-Related Pneumonitis. Diagnostics (Basel) 2023; 13:691. [PMID: 36832178 PMCID: PMC9955572 DOI: 10.3390/diagnostics13040691] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2023] [Revised: 02/07/2023] [Accepted: 02/08/2023] [Indexed: 02/16/2023] Open
Abstract
OBJECTIVES To investigate the predictive ability of clinical and chest computed tomography (CT) features to predict the severity of symptomatic immune checkpoint inhibitor-related pneumonitis (CIP). METHODS This study included 34 patients diagnosed with symptomatic CIP (grades 2-5) and divided into mild (grade 2) and severe CIP (grades 3-5) groups. The groups' clinical and chest CT features were analyzed. Three manual scores (extent, image finding, and clinical symptom scores) were conducted to evaluate the diagnostic performance alone and in combination. RESULTS There were 20 cases of mild CIP and 14 cases of severe CIP. More severe CIP occurred within 3 months than after 3 months (11 vs. 3 cases, p = 0.038). Severe CIP was significantly associated with fever (p < 0.001) and the acute interstitial pneumonia/acute respiratory distress syndrome pattern (p = 0.001). The diagnostic performance of chest CT scores (extent score and image finding score) was better than that of clinical symptom score. The combination of the three scores demonstrated the best diagnostic value, with an area under the receiver operating characteristic curve of 0.948. CONCLUSIONS The clinical and chest CT features have important application value in assessing the disease severity of symptomatic CIP. We recommend the routine use of chest CT in a comprehensive clinical evaluation.
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Affiliation(s)
- Qian Zhang
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Xiuli Tao
- Department of Nuclear Medicine (PET-CT Center), National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Shijun Zhao
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Ning Li
- Department of Clinical Trial Center, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Shuhang Wang
- Department of Clinical Trial Center, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Ning Wu
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
- Department of Nuclear Medicine (PET-CT Center), National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Hebei Cancer Hospital, Chinese Academy of Medical Sciences, Langfang 065001, China
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Kathamuthu ND, Subramaniam S, Le QH, Muthusamy S, Panchal H, Sundararajan SCM, Alrubaie AJ, Zahra MMA. A deep transfer learning-based convolution neural network model for COVID-19 detection using computed tomography scan images for medical applications. ADVANCES IN ENGINEERING SOFTWARE (BARKING, LONDON, ENGLAND : 1992) 2023; 175:103317. [PMID: 36311489 PMCID: PMC9595382 DOI: 10.1016/j.advengsoft.2022.103317] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Revised: 10/09/2022] [Accepted: 10/19/2022] [Indexed: 05/26/2023]
Abstract
The Coronavirus (COVID-19) has become a critical and extreme epidemic because of its international dissemination. COVID-19 is the world's most serious health, economic, and survival danger. This disease affects not only a single country but the entire planet due to this infectious disease. Illnesses of Covid-19 spread at a much faster rate than usual influenza cases. Because of its high transmissibility and early diagnosis, it isn't easy to manage COVID-19. The popularly used RT-PCR method for COVID-19 disease diagnosis may provide false negatives. COVID-19 can be detected non-invasively using medical imaging procedures such as chest CT and chest x-ray. Deep learning is the most effective machine learning approach for examining a considerable quantity of chest computed tomography (CT) pictures that can significantly affect Covid-19 screening. Convolutional neural network (CNN) is one of the most popular deep learning techniques right now, and its gaining traction due to its potential to transform several spheres of human life. This research aims to develop conceptual transfer learning enhanced CNN framework models for detecting COVID-19 with CT scan images. Though with minimal datasets, these techniques were demonstrated to be effective in detecting the presence of COVID-19. This proposed research looks into several deep transfer learning-based CNN approaches for detecting the presence of COVID-19 in chest CT images.VGG16, VGG19, Densenet121, InceptionV3, Xception, and Resnet50 are the foundation models used in this work. Each model's performance was evaluated using a confusion matrix and various performance measures such as accuracy, recall, precision, f1-score, loss, and ROC. The VGG16 model performed much better than the other models in this study (98.00 % accuracy). Promising outcomes from experiments have revealed the merits of the proposed model for detecting and monitoring COVID-19 patients. This could help practitioners and academics create a tool to help minimal health professionals decide on the best course of therapy.
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Affiliation(s)
- Nirmala Devi Kathamuthu
- Department of Computer Science and Engineering, Kongu Engineering College (Autonomous), Perundurai, Erode, Tamil Nadu, India
| | - Shanthi Subramaniam
- Department of Computer Science and Engineering, Kongu Engineering College (Autonomous), Perundurai, Erode, Tamil Nadu, India
| | - Quynh Hoang Le
- Institute of Research and Development, Duy Tan University, Da Nang, Vietnam
- School of Medicine and Pharmacy, Duy Tan University, Da Nang, Vietnam
| | - Suresh Muthusamy
- Department of Electronics and Communication Engineering, Kongu Engineering College (Autonomous), Perundurai, Erode, Tamil Nadu, India
| | - Hitesh Panchal
- Department of Mechanical Engineering, Government Engineering College, Patan, Gujarat, India
| | | | - Ali Jawad Alrubaie
- Department of Medical Instrumentation Techniques Engineering, Al- Mustaqbal University College, 51001, Hilla, Iraq
| | - Musaddak Maher Abdul Zahra
- Computer Techniques Engineering Department, Al-Mustaqbal University College, Hillah 51001, Iraq
- Electrical Engineering Department, College of Engineering, University of Babylon, Hilla, Babil, Iraq
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Özdemir Ö, Sönmez EB. Attention mechanism and mixup data augmentation for classification of COVID-19 Computed Tomography images. JOURNAL OF KING SAUD UNIVERSITY. COMPUTER AND INFORMATION SCIENCES 2022; 34:6199-6207. [PMID: 38620953 PMCID: PMC8280602 DOI: 10.1016/j.jksuci.2021.07.005] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/22/2021] [Revised: 07/01/2021] [Accepted: 07/07/2021] [Indexed: 12/21/2022]
Abstract
The Coronavirus disease is quickly spreading all over the world and the emergency situation is still out of control. Latest achievements of deep learning algorithms suggest the use of deep Convolutional Neural Network to implement a computer-aided diagnostic system for automatic classification of COVID-19 CT images. In this paper, we propose to employ a feature-wise attention layer in order to enhance the discriminative features obtained by convolutional networks. Moreover, the original performance of the network has been improved using the mixup data augmentation technique. This work compares the proposed attention-based model against the stacked attention networks, and traditional versus mixup data augmentation approaches. We deduced that feature-wise attention extension, while outperforming the stacked attention variants, achieves remarkable improvements over the baseline convolutional neural networks. That is, ResNet50 architecture extended with a feature-wise attention layer obtained 95.57% accuracy score, which, to best of our knowledge, fixes the state-of-the-art in the challenging COVID-CT dataset.
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Affiliation(s)
- Özgür Özdemir
- Computer Engineering Department, Istanbul Bilgi University, Turkey
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4
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He JW, Su Y, Qiu ZS, Wu JJ, Chen J, Luo Z, Zhang Y. Steroids Therapy in Patients With Severe COVID-19: Association With Decreasing of Pneumonia Fibrotic Tissue Volume. Front Med (Lausanne) 2022; 9:907727. [PMID: 35911397 PMCID: PMC9329540 DOI: 10.3389/fmed.2022.907727] [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: 04/01/2022] [Accepted: 06/07/2022] [Indexed: 11/13/2022] Open
Abstract
Background We use longitudinal chest CT images to explore the effect of steroids therapy in COVID-19 pneumonia which caused pulmonary lesion progression. Materials and Methods We retrospectively enrolled 78 patients with severe to critical COVID-19 pneumonia, among which 25 patients (32.1%) who received steroid therapy. Patients were further divided into two groups with severe and significant-severe illness based on clinical symptoms. Serial longitudinal chest CT scans were performed for each patient. Lung tissue was segmented into the five lung lobes and mapped into the five pulmonary tissue type categories based on Hounsfield unit value. The volume changes of normal tissue and pneumonia fibrotic tissue in the entire lung and each five lung lobes were the primary outcomes. In addition, this study calculated the changing percentage of tissue volume relative to baseline value to directly demonstrate the disease progress. Results Steroid therapy was associated with the decrease of pneumonia fibrotic tissue (PFT) volume proportion. For example, after four CT cycles of treatment, the volume reduction percentage of PFT in the entire lung was −59.79[±12.4]% for the steroid-treated patients with severe illness, and its p-value was 0.000 compared to that (−27.54[±85.81]%) in non-steroid-treated ones. However, for the patient with a significant-severe illness, PFT reduction in steroid-treated patients was −41.92[±52.26]%, showing a 0.275 p-value compared to −37.18[±76.49]% in non-steroid-treated ones. The PFT evolution analysis in different lung lobes indicated consistent findings as well. Conclusion Steroid therapy showed a positive effect on the COVID-19 recovery, and its effect was related to the disease severity.
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Affiliation(s)
- Jin-wei He
- School of Information Science and Technology, ShanghaiTech University, Shanghai, China
| | - Ying Su
- Department of Critical Care Medicine, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Ze-song Qiu
- School of Information Science and Technology, ShanghaiTech University, Shanghai, China
| | - Jiang-jie Wu
- School of Information Science and Technology, ShanghaiTech University, Shanghai, China
| | - Jun Chen
- Department of Radiology, Renmin Hospital of Wuhan University, Wuhan, China
| | - Zhe Luo
- Department of Critical Care Medicine, Zhongshan Hospital, Fudan University, Shanghai, China
- *Correspondence: Zhe Luo,
| | - Yuyao Zhang
- School of Information Science and Technology, ShanghaiTech University, Shanghai, China
- iHuman Institute, ShanghaiTech University, Shanghai, China
- Yuyao Zhang,
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5
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Ghashghaei S, Wood DA, Sadatshojaei E, Jalilpoor M. Grayscale image statistics of COVID‐19 patient CT scans characterize lung condition with machine and deep learning. Chronic Dis Transl Med 2022; 8:191-206. [PMID: 35942198 PMCID: PMC9347876 DOI: 10.1002/cdt3.27] [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: 02/08/2022] [Revised: 03/28/2022] [Accepted: 04/08/2022] [Indexed: 11/30/2022] Open
Abstract
Background Grayscale image attributes of computed tomography (CT) of pulmonary scans contain valuable information relating to patients with respiratory ailments. These attributes are used to evaluate the severity of lung conditions of patients confirmed to be with and without COVID‐19. Method Five hundred thirteen CT images relating to 57 patients (49 with COVID‐19; 8 free of COVID‐19) were collected at Namazi Medical Centre (Shiraz, Iran) in 2020 and 2021. Five visual scores (VS: 0, 1, 2, 3, or 4) are clinically assigned to these images with the score increasing with the severity of COVID‐19‐related lung conditions. Eleven deep learning and machine learning techniques (DL/ML) are used to distinguish the VS class based on 12 grayscale image attributes. Results The convolutional neural network achieves 96.49% VS accuracy (18 errors from 513 images) successfully distinguishing VS Classes 0 and 1, outperforming clinicians’ visual inspections. An algorithmic score (AS), involving just five grayscale image attributes, is developed independently of clinicians’ assessments (99.81% AS accuracy; 1 error from 513 images). Conclusion Grayscale CT image attributes can be successfully used to distinguish the severity of COVID‐19 lung damage. The AS technique developed provides a suitable basis for an automated system using ML/DL methods and 12 image attributes.
Grayscale image statistics of CT scans can effectively classify lung abnormalities Graphical trends of grayscale statistics distinguish visual assessments COVID‐19 classes Machine/deep learning algorithms predict severity from image grayscale attributes Algorithmic class systems can be established using just five grayscale attributes Confusion matrices provide detailed insight to algorithm prediction capabilities
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Affiliation(s)
- Sara Ghashghaei
- Medical School Shiraz University of Medical Sciences Shiraz Iran
| | - David A. Wood
- Department of Research DWA Energy Limited Lincoln LN5 9JP UK
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Al-Jahdhami I, Al-Mawali A, Bennji SM. Respiratory Complications after COVID-19. Oman Med J 2022; 37:e343. [PMID: 35282425 PMCID: PMC8907756 DOI: 10.5001/omj.2022.52] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2021] [Accepted: 10/10/2021] [Indexed: 11/05/2022] Open
Abstract
COVID-19 pandemic has been associated with high short-term morbidity and mortality. Lungs are the main organs affected by SARS-CoV-2 infection. In the long-term, the pulmonary sequelae related to COVID-19 are expected to rise significantly leading to an extended impact on community health and health care facilities. A wide variety of long-term respiratory complications secondary to COVID-19 have been described ranging from persistent symptoms and radiologically observable changes to impaired respiratory physiology, vascular complications, and pulmonary fibrosis. Even after two-years, respiratory sequalae related to post-acute SARS-CoV-2 infection have not been fully explored and understood. The main treatment for most COVID-19 respiratory complications is still symptomatic and supportive-care oriented. In this review article, we shed light on current knowledge of the post-COVID-19 complications, focusing on pulmonary fibrosis, treatment directions, and recommendations to physicians.
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Affiliation(s)
| | - Khalid Al-naamani1
- Department of Medicine, Armed Forced Hospital, Muscat, Oman
- Centre of Studies and Research, Ministry of Health, Muscat, Oman
- Strategic Research Program for Non-Communicable Diseases, Ministry of Higher Education, Scientific Research and Innovation, Muscat, Oman
- Thoracic Oncology Unit, Sultan Qaboos Comprehensive Cancer Care and Research Center, Muscat, Oman
| | - Adhra Al-Mawali
- Centre of Studies and Research, Ministry of Health, Muscat, Oman
- Strategic Research Program for Non-Communicable Diseases, Ministry of Higher Education, Scientific Research and Innovation, Muscat, Oman
| | - Sami M. Bennji
- Thoracic Oncology Unit, Sultan Qaboos Comprehensive Cancer Care and Research Center, Muscat, Oman
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Andrew D, Shyam K, Cicilet S, Johny J. Assessment of interobserver reliability and predictive values of CT semiquantitative and severity scores in COVID lung disease. THE EGYPTIAN JOURNAL OF RADIOLOGY AND NUCLEAR MEDICINE 2021. [PMCID: PMC8211928 DOI: 10.1186/s43055-021-00523-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
Abstract
Background The coronavirus disease (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), and first reported in December 2019 at Wuhan, China, has since then progressed into an ongoing global pandemic. The primary organ targeted by the virus is the pulmonary system, leading to interstitial pneumonia and subsequent oxygen dependency and morbidity. Computed tomography (CT) has been used by various centers as an imaging modality for the assessment of severity of lung involvement in individuals. Two popular systems of scoring lung involvement on CT are CT semiquantitative score (SQ) and CT severity score (CT-SS), both of which assess extent of pulmonary involvement by interstitial pneumonia and are partly based upon subjective evaluation. Our cross-sectional observational study aims to assess the interobserver reliability of these scores, as well as to assess the statistical correlation between the respective CT scores to severity of clinical outcome. Results Both the SQ and CT-SS scores showed an excellent interobserver reliability (ICC 0.91 and 0.93, respectively, p < 0.05). The CT-SS was marginally more sensitive (99.2%) in detecting severe COVID pneumonia than SQ (86.5%). The positive predictive value of SQ (98.3%) is more than CT-SS (78%) for detecting severe disease. The similarity of interobserver reliability obtained for both scores reiterates the respective cutoff CT scores proposed by the above systems, as 18 for SQ and 19.5 for CT-SS. Conclusion Both the SQ and CT-SS scores display excellent interobserver reliability. The CT-SS was more sensitive in detecting severe COVID pneumonia and may thus be preferred over the SQ as an initial radiological tool in predicting severity of infection.
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Arora V, Ng EYK, Leekha RS, Darshan M, Singh A. Transfer learning-based approach for detecting COVID-19 ailment in lung CT scan. Comput Biol Med 2021; 135:104575. [PMID: 34153789 PMCID: PMC8196483 DOI: 10.1016/j.compbiomed.2021.104575] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2021] [Revised: 06/08/2021] [Accepted: 06/09/2021] [Indexed: 12/29/2022]
Abstract
This research work aims to identify COVID-19 through deep learning models using lung CT-SCAN images. In order to enhance lung CT scan efficiency, a super-residual dense neural network was applied. The experimentation has been carried out using benchmark datasets like SARS-COV-2 CT-Scan and Covid-CT Scan. To mark COVID-19 as positive or negative for the improved CT scan, existing pre-trained models such as XceptionNet, MobileNet, InceptionV3, DenseNet, ResNet50, and VGG (Visual Geometry Group)16 have been used. Taking CT scans with super resolution using a residual dense neural network in the pre-processing step resulted in improving the accuracy, F1 score, precision, and recall of the proposed model. On the dataset Covid-CT Scan and SARS-COV-2 CT-Scan, the MobileNet model provided a precision of 94.12% and 100% respectively.
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Affiliation(s)
- Vinay Arora
- Computer Science & Engineering Department, Thapar Institute of Engineering and Technology, Patiala, Punjab, India.
| | - Eddie Yin-Kwee Ng
- School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore.
| | | | - Medhavi Darshan
- Department of Mathematics, Kamala Nehru College, University of Delhi, Delhi, India.
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Verma HK. Radiological and clinical spectrum of COVID-19: A major concern for public health. World J Radiol 2021; 13:53-63. [PMID: 33815683 PMCID: PMC8006056 DOI: 10.4329/wjr.v13.i3.53] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/03/2020] [Revised: 12/07/2020] [Accepted: 03/12/2021] [Indexed: 02/06/2023] Open
Abstract
The pandemic of novel coronavirus disease 2019 (COVID-19) is an infectious disease caused by +ve strand RNA virus (SARS-CoV-2, severe acute respiratory syndrome coronavirus 2) that belongs to the corona viridae family. In March, the World Health Organization declared the outbreak of novel coronavirus for the public health emergency. Although SARS-CoV-2 infection presents with respiratory symptoms, it affects other organs such as the kidneys, liver, heart and brain. Early-stage laboratory disease testing shows many false positive or negative outcomes such as less white blood cell count and a low number of lymphocyte count. However, radiological examination and diagnosis are among the main components of the diagnosis and treatment of COVID-19. In particular, for COVID-19, chest computed tomography developed vigorous initial diagnosis and disease progression assessment. However, the accuracy is limited. Although real-time reverse transcription-polymerase chain reaction is the gold standard method for the diagnosis of COVID-19, sometimes it may give false-negative results. Due to the consequences of the missing diagnosis. This resulted in a discrepancy between the two means of examination. Conversely, based on currently available evidence, we summarized the possible understanding of the various patho-physiology, radio diagnostic methods in severe COVID-19 patients. As the information on COVID-19 evolves rapidly, this review will provide vital information for scientists and clinicians to consider novel perceptions for the comprehensive knowledge of the diagnostic approaches based on current experience.
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Affiliation(s)
- Henu Kumar Verma
- Developmental and Stem Cell Biology Lab, Institute of Experimental Endocrinology and Oncology CNR, Naples 80131, Campania, Italy
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10
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Ileri C, Dogan Z, Ozben B, Karaoglu C, Gunay N, Tigen K, Basat S, Uyan C. Evaluation of the relation between cardiac biomarkers and thorax computed tomography findings in COVID-19 patients. Biomark Med 2021; 15:285-293. [PMID: 33501850 PMCID: PMC7863677 DOI: 10.2217/bmm-2020-0388] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2020] [Accepted: 12/22/2020] [Indexed: 01/08/2023] Open
Abstract
Background: Troponin levels may be elevated in COVID-19 infection. The aim of this study was to the explore relation between troponin levels and COVID-19 severity. Materials, methods & Results: One hundred and forty consecutive patients with COVID-19 pneumonia were included. Diagnosis of COVID-19 pneumonia was based on positive chest computed tomography (CT) findings. Quantitative PCR test was performed in all patients. Only 74 patients were quantitative PCR-positive. Twenty four patients had severe CT findings and 27 patients had progressive disease. These patients had significantly lower albumin and higher ferritin, D-dimer, lactate dehydrogenase, C-reactive protein, and high-sensitivity cardiac troponin I (hs-cTnI). Conclusion: COVID-19 patients with severe CT findings and progressive disease had higher hs-cTnI levels suggesting the use of hs-cTnI in risk stratification.
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Affiliation(s)
- Cigdem Ileri
- Department of Cardiology, Umraniye Research & Training Hospital, University of Health Sciences, Istanbul, Turkey
| | - Zekeriya Dogan
- Department of Cardiology, Marmara University School of Medicine, Istanbul, Turkey
| | - Beste Ozben
- Department of Cardiology, Marmara University School of Medicine, Istanbul, Turkey
| | - Cagla Karaoglu
- Department of Internal Medicine, Umraniye Research & Training Hospital, University of Health Sciences, Istanbul, Turkey
| | - Nuran Gunay
- Department of Cardiology, Umraniye Research & Training Hospital, University of Health Sciences, Istanbul, Turkey
| | - Kursat Tigen
- Department of Cardiology, Marmara University School of Medicine, Istanbul, Turkey
| | - Sema Basat
- Department of Internal Medicine, Umraniye Research & Training Hospital, University of Health Sciences, Istanbul, Turkey
| | - Cihangir Uyan
- Department of Cardiology, Umraniye Research & Training Hospital, University of Health Sciences, Istanbul, Turkey
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Ratre YK, Kahar N, Bhaskar LVKS, Bhattacharya A, Verma HK. Molecular mechanism, diagnosis, and potential treatment for novel coronavirus (COVID-19): a current literature review and perspective. 3 Biotech 2021; 11:94. [PMID: 33520580 PMCID: PMC7832422 DOI: 10.1007/s13205-021-02657-3] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2020] [Accepted: 01/12/2021] [Indexed: 12/12/2022] Open
Abstract
Novel coronavirus disease 2019 (COVID-19) is a positive-sense single-stranded RNA virus which belongs to the Coronaviridae family. COVID-19 outbreak became evident after the severe acute respiratory syndrome coronavirus and the Middle East respiratory syndrome coronavirus in the twenty-first century as the start of the third deadly coronavirus. Currently, research is at an early stage, and the exact etiological dimensions of COVID-19 are unknown. Several candidate drugs and plasma therapy have been considered and evaluated for the treatment of severe COVID-19 patients. These include clinically available drugs such as chloroquine, hydroxychloroquine, and lopinavir/ritonavir. However, understanding the pathogenic mechanisms of this virus is critical for predicting interaction with humans. Based on recent evidence, we have summarized the current virus biology in terms of the possible understanding of the various pathophysiologies, molecular mechanisms, recent efficient diagnostics, and therapeutic approaches to control the disease. In addition, we briefly reviewed the biochemistry of leading candidates for novel therapies and their current status in clinical trials. As information from COVID-19 is evolving rapidly, this review will help the researcher to consider new insights and potential therapeutic approaches based on up-to-date knowledge. Finally, this review illustrates a list of alternative therapeutic solutions for a viral infection.
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Affiliation(s)
| | - Namrata Kahar
- Department of Zoology, Guru Ghasidas Vishwavidyalaya, Bilaspur, India
| | | | - Antaripa Bhattacharya
- Department of Molecular Medicine and Medical Biotechnology, University of Naples “Federico II”, Naples, Italy
| | - Henu Kumar Verma
- Developmental and Stem Cell Biology Lab, Institute of Experimental Endocrinology and Oncology CNR, Naples, Italy
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Rajaraman S, Sornapudi S, Alderson PO, Folio LR, Antani SK. Analyzing inter-reader variability affecting deep ensemble learning for COVID-19 detection in chest radiographs. PLoS One 2020; 15:e0242301. [PMID: 33180877 PMCID: PMC7660555 DOI: 10.1371/journal.pone.0242301] [Citation(s) in RCA: 35] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2020] [Accepted: 11/01/2020] [Indexed: 01/17/2023] Open
Abstract
Data-driven deep learning (DL) methods using convolutional neural networks (CNNs) demonstrate promising performance in natural image computer vision tasks. However, their use in medical computer vision tasks faces several limitations, viz., (i) adapting to visual characteristics that are unlike natural images; (ii) modeling random noise during training due to stochastic optimization and backpropagation-based learning strategy; (iii) challenges in explaining DL black-box behavior to support clinical decision-making; and (iv) inter-reader variability in the ground truth (GT) annotations affecting learning and evaluation. This study proposes a systematic approach to address these limitations through application to the pandemic-caused need for Coronavirus disease 2019 (COVID-19) detection using chest X-rays (CXRs). Specifically, our contribution highlights significant benefits obtained through (i) pretraining specific to CXRs in transferring and fine-tuning the learned knowledge toward improving COVID-19 detection performance; (ii) using ensembles of the fine-tuned models to further improve performance over individual constituent models; (iii) performing statistical analyses at various learning stages for validating results; (iv) interpreting learned individual and ensemble model behavior through class-selective relevance mapping (CRM)-based region of interest (ROI) localization; and, (v) analyzing inter-reader variability and ensemble localization performance using Simultaneous Truth and Performance Level Estimation (STAPLE) methods. We find that ensemble approaches markedly improved classification and localization performance, and that inter-reader variability and performance level assessment helps guide algorithm design and parameter optimization. To the best of our knowledge, this is the first study to construct ensembles, perform ensemble-based disease ROI localization, and analyze inter-reader variability and algorithm performance for COVID-19 detection in CXRs.
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Affiliation(s)
- Sivaramakrishnan Rajaraman
- Lister Hill National Center for Biomedical Communications, National Library of Medicine, Bethesda, Maryland, United States of America
| | - Sudhir Sornapudi
- Department of Electrical and Computer Engineering, Missouri University of Science and Technology, Rolla, Missouri, United States of America
| | - Philip O. Alderson
- School of Medicine, Saint Louis University, St. Louis, Missouri, United States of America
| | - Les R. Folio
- Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Sameer K. Antani
- Lister Hill National Center for Biomedical Communications, National Library of Medicine, Bethesda, Maryland, United States of America
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Huang J, Li J, Zou Z, Kandathil A, Liu J, Qiu S, Oz OK. Clinical Characteristics of 3 Patients Infected with COVID-19: Age, Interleukin 6 (IL-6), Lymphopenia, and Variations in Chest Computed Tomography (CT). Am J Case Rep 2020; 21:e924905. [PMID: 33052896 PMCID: PMC7571279 DOI: 10.12659/ajcr.924905] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022] Open
Abstract
Case series Patients: Female, 69-year-old • Female, 38-year-old • Male, 37-year-old Final Diagnosis: COVID-19 Symptoms: Fever Medication: — Clinical Procedure: — Specialty: Infectious Diseases
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Affiliation(s)
- Jing Huang
- Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China (mainland)
| | - Jie Li
- Department of Immunology, Cancer Biology PhD Program, University of South Florida, Tampa, FL, USA
| | - Zhenwei Zou
- Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China (mainland)
| | - Asha Kandathil
- Department of Radiology, UT Southwestern Medical Center, Dallas, TX, USA
| | - Jun Liu
- Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China (mainland)
| | - Shaohong Qiu
- The People's Hospital of Shishou, Shishou, Hubei, China (mainland)
| | - Orhan K Oz
- Department of Radiology, UT Southwestern Medical Center, Dallas, TX, USA
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Freund Y, Drogrey M, Miró Ò, Marra A, Féral‐Pierssens A, Penaloza A, Hernandez BAL, Beaune S, Gorlicki J, Vaittinada Ayar P, Truchot J, Pena B, Aguirre A, Fémy F, Javaud N, Chauvin A, Chouihed T, Montassier E, Claret P, Occelli C, Roussel M, Brigant F, Ellouze S, Le Borgne P, Laribi S, Simon T, Lucidarme O, Cachanado M, Bloom B. Association Between Pulmonary Embolism and COVID-19 in Emergency Department Patients Undergoing Computed Tomography Pulmonary Angiogram: The PEPCOV International Retrospective Study. Acad Emerg Med 2020; 27:811-820. [PMID: 32734624 DOI: 10.1111/acem.14096] [Citation(s) in RCA: 46] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2020] [Revised: 06/18/2020] [Accepted: 06/18/2020] [Indexed: 01/08/2023]
Abstract
BACKGROUND There have been reports of procoagulant activity in patients with COVID-19. Whether there is an association between pulmonary embolism (PE) and COVID-19 in the emergency department (ED) is unknown. The aim of this study was to assess whether COVID-19 is associated with PE in ED patients who underwent a computed tomographic pulmonary angiogram (CTPA). METHODS A retrospective study in 26 EDs from six countries. ED patients in whom a CTPA was performed for suspected PE during a 2-month period covering the pandemic peak. The primary endpoint was the occurrence of a PE on CTPA. COVID-19 was diagnosed in the ED either on CT or reverse transcriptase-polymerase chain reaction. A multivariable binary logistic regression was built to adjust with other variables known to be associated with PE. A sensitivity analysis was performed in patients included during the pandemic period. RESULTS A total of 3,358 patients were included, of whom 105 were excluded because COVID-19 status was unknown, leaving 3,253 for analysis. Among them, 974 (30%) were diagnosed with COVID-19. Mean (±SD) age was 61 (±19) years and 52% were women. A PE was diagnosed on CTPA in 500 patients (15%). The risk of PE was similar between COVID-19 patients and others (15% in both groups). In the multivariable binary logistic regression model, COVID-19 was not associated with higher risk of PE (adjusted odds ratio = 0.98, 95% confidence interval = 0.76 to 1.26). There was no association when limited to patients in the pandemic period. CONCLUSION In ED patients who underwent CTPA for suspected PE, COVID-19 was not associated with an increased probability of PE diagnosis. These results were also valid when limited to the pandemic period. However, these results may not apply to patients with suspected COVID-19 in general.
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Affiliation(s)
- Yonathan Freund
- From Sorbonne Université Paris France
- the Emergency Department Hôpital Pitié‐Salpêtrière Assistance Publique–Hôpitaux de Paris (APHP), APHP.SU Paris France
| | - Marie Drogrey
- the Emergency Department Hôpital Pitié‐Salpêtrière Assistance Publique–Hôpitaux de Paris (APHP), APHP.SU Paris France
| | - Òscar Miró
- the Emergency Departement Hospital Clínic Barcelona Catalonia Spain
| | - Alessio Marra
- the Emergency Department Centro EAS–Emergenza Alta Specializzazione ASST Papa Giovanni XXIII Hospital Bergamo Italy
| | - Anne‐Laure Féral‐Pierssens
- the Charles Lemoyne–Saguenay Lac Saint‐Jean Research Center on Health Innovations (CR CSIS) Sherbrooke University Longueuil Québec Canada
- the Emergency Department European Georges Pompidou hospital APHP Paris France
| | - Andrea Penaloza
- the Service des Urgences Cliniques Universitaires Saint‐Luc Université Catholique de Louvain Louvain‐la‐Neuve Belgium
| | | | - Sebastien Beaune
- the Emergency Department Hôpital Ambroise‐Paré, APHP, Boulogne, INSERM UMR 1144 Université Paris Centre Paris France
| | - Judith Gorlicki
- the Emergency Department SAMU 93 Avicenne University Hospital, APHP.HUPSSD Bobigny France
- INSERM UMR‐S 942 Sorbonne Paris Nord University Bobigny France
| | - Prabakar Vaittinada Ayar
- the Emergency Department University Hospital of Beaujon, APHP Clichy France
- UMR‐S 942 INSERM MASCOT Paris France
- University Paris France
| | - Jennifer Truchot
- the Emergency Department Cochin Hospital Hôpitaux Universitaire Paris Centre APHP Paris France
| | - Barbara Pena
- the Emergency Department Hospital General Universitario de Alicante Alicante Spain
| | - Alfons Aguirre
- the Emergency Department Hospital del Mar Barcelona Catalonia Spain
| | - Florent Fémy
- the Emergency Department Georges Pompidou European Hospital APHP, Université de Paris Paris France
- the Toxicology and Chemical Risks Department French Armed Forces Biomedical Institute Bretigny‐Sur‐Orges France
| | - Nicolas Javaud
- the Emergency Department Louis Mourier Hospital University of Paris, APHP.North Paris France
| | - Anthony Chauvin
- the Emergency Department Hopital Lariboisière Assistance Publique‐Hôpitaux de Paris Paris France
- the Faculté de Médecine Université de Paris Paris France
| | - Tahar Chouihed
- the Emergency Department Université de Lorraine, University Hospital of Nancy, Centre d'Investigations Cliniques‐1433, and INSERM UMR_S 1116 Nancy France
- the F‐CRIN INI‐CRCT Nancy France
| | - Emmanuel Montassier
- the Department of Emergency Medicine CHU Nantes Nantes France
- the MiHAR Laboratory Université de Nantes Nantes France
| | - Pierre‐Géraud Claret
- the Department of Anesthesia Resuscitation Pain Emergency Medicine Nîmes University Hospital Nîmes France
| | - Céline Occelli
- the Emergency Department CHU Pasteur 2 Nice France
- the University Nice Côte d'Azur Nice France
| | - Mélanie Roussel
- the Emergency Department Rouen University Hospital Rouen France
| | - Fabien Brigant
- the Emergency Department Hôpital Saint‐Antoine, APHP.SU Paris France
| | - Sami Ellouze
- the Emergency Department Hôpital Saint‐Louis, APHP Paris France
| | - Pierrick Le Borgne
- the Emergency Department Hôpitaux Universitaires de Strasbourg Strasbourg France
- the INSERM (French National Institute of Health and Medical Research), UMR 1260 Regenerative NanoMedicine (RNM) Fédération de Médecine Translationnelle (FMTS) University of Strasbourg Strasbourg France
| | - Said Laribi
- the Emergency Department School of Medicine and CHU Tours Tours University Tours France
| | - Tabassome Simon
- From Sorbonne Université Paris France
- the Clinical Research Platform (URC‐CRC‐CRB) AP‐HP Hôpital Saint‐Antoine Paris France
| | - Olivier Lucidarme
- From Sorbonne Université Paris France
- the APHP‐Sorbonne Universités Pitié Salpêtrière Hospital, Radiology Department and UPMC Univ Paris 06 CNRS, INSERM, Laboratoire d'Imagerie Biomédicale Paris France
| | - Marine Cachanado
- the Emergency Department Royal London Hospital Barts Health NHS Trust London UK
| | - Ben Bloom
- and the Emergency Department European Georges Pompidou Hospital, APHP Paris France
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