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Zhuo LY, Hao JW, Song ZJ, Meng H, Wang TD, Yang LL, Yang ZM, Ma JM, Shen D, Cui JJ, Chen WJ, Yang W, Zang LL, Wang JN, Yin XP. Predicting the severity of mycoplasma pneumoniae pneumonia in pediatric and adult patients: a multicenter study. Sci Rep 2024; 14:22978. [PMID: 39362944 DOI: 10.1038/s41598-024-74251-5] [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: 05/04/2024] [Accepted: 09/24/2024] [Indexed: 10/05/2024] Open
Abstract
The purpose of this study is to develop a nomogram model for early prediction of the severe mycoplasma pneumoniae pneumonia (SMPP) in Pediatric and Adult Patients. A retrospective analysis was conducted on patients with MPP, classifying them into SMPP and non-severe MPP (NSMPP) groups. A total of 550 patients (NSMPP 374 and SMPP 176) were enrolled in the study and allocated to training, validation cohorts. 278 patients (NSMPP 224 and SMPP 54) were retrospectively collected from two institutions and allocated to testing cohort. The risk factors for SMPP were identified using univariate analysis. For radiomic feature selection, Spearman's correlation and the least absolute shrinkage and selection operator (LASSO) were utilized. Logistic regression was used to build different models, including clinical, imaging, radiomics, and integrated models (combining clinical, imaging, and radiomics features selected). The model's discrimination was evaluated using a receiver operating characteristic curve, its calibration with a calibration curve, and the results were visualized using the Hosmer-Lemeshow goodness-of-fit test. Thirteen clinical features and fourteen imaging features were selected for constructing the clinical and imaging models. Simultaneously, a set of twenty-five radiomics features were utilized to build the radiomics model. The integrated model demonstrated good calibration and discrimination in the training cohorts (AUC, 0.922; 95% CI: 0.900, 0.942), validation cohorts (AUC, 0.879; 95% CI: 0.806, 0.920), and testing cohorts (AUC, 0.877; 95% CI: 0.836, 0.916). The discriminatory and predictive efficacy of the clinical model in testing cohorts increased further after clinical and radiological features were incorporated (AUC, 0.849 vs. 0.922, P = 0.002). The model demonstrated exemplary predictive efficacy for SMPP by leveraging a comprehensive set of inputs, encompassing clinical data, quantitative and qualitative radiological features, along with radiomics features. The integration of these three aspects in the predictive model further enhanced the performance of the clinical model, indicating the potential for extensive clinical applications.
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Affiliation(s)
- Li-Yong Zhuo
- Department of Radiology and Hebei Key Laboratory of Precise Imaging of Inflammation Related Tumors, The Affiliated Hospital of Hebei University, 212 Eastern Yuhua Road, Baoding, 071000, Hebei Province, China
| | - Jia-Wei Hao
- Department of Radiology and Hebei Key Laboratory of Precise Imaging of Inflammation Related Tumors, The Affiliated Hospital of Hebei University, 212 Eastern Yuhua Road, Baoding, 071000, Hebei Province, China
| | - Zi-Jun Song
- Department of Critical Care Medicine, Baoding First Central Hospital, Lianchi District, No. 320, Changcheng North Street (Qianwei Road), Baoding, 071000, China
| | - Huan Meng
- Department of Radiology and Hebei Key Laboratory of Precise Imaging of Inflammation Related Tumors, The Affiliated Hospital of Hebei University, 212 Eastern Yuhua Road, Baoding, 071000, Hebei Province, China
| | - Tian-Da Wang
- Department of Radiology and Hebei Key Laboratory of Precise Imaging of Inflammation Related Tumors, The Affiliated Hospital of Hebei University, 212 Eastern Yuhua Road, Baoding, 071000, Hebei Province, China
| | - Lu-Lu Yang
- Department of Radiology and Hebei Key Laboratory of Precise Imaging of Inflammation Related Tumors, The Affiliated Hospital of Hebei University, 212 Eastern Yuhua Road, Baoding, 071000, Hebei Province, China
| | - Zi-Mei Yang
- Department of Radiology and Hebei Key Laboratory of Precise Imaging of Inflammation Related Tumors, The Affiliated Hospital of Hebei University, 212 Eastern Yuhua Road, Baoding, 071000, Hebei Province, China
| | - Jia-Mei Ma
- Department of Radiology and Hebei Key Laboratory of Precise Imaging of Inflammation Related Tumors, The Affiliated Hospital of Hebei University, 212 Eastern Yuhua Road, Baoding, 071000, Hebei Province, China
| | - Dan Shen
- Department of Urology, the Affiliated Hospital of Hebei University, 212 Eastern Yuhua Road, Baoding City, 071000, Hebei Province, China
| | - Jing-Jing Cui
- Department of Research and Development, United Imaging Intelligence (Beijing) Co., Ltd.Yongteng North Road, Haidian District, Beijing, 100094, China
| | - Wen-Jing Chen
- Department of Research and Development, United Imaging Intelligence (Beijing) Co., Ltd.Yongteng North Road, Haidian District, Beijing, 100094, China
| | - Wei Yang
- Department of Pulmonary and Critical Care Medicine, Baoding First Central Hospital, Lianchi District, No. 320, Changcheng North Street (Qianwei Road), Baoding, 071000, China
| | - Li-Li Zang
- Department of Radiology, Baoding Children's Hospital, No. 103, East Baihua Road, Baoding, 071000, China
| | - Jia-Ning Wang
- Department of Radiology and Hebei Key Laboratory of Precise Imaging of Inflammation Related Tumors, The Affiliated Hospital of Hebei University, 212 Eastern Yuhua Road, Baoding, 071000, Hebei Province, China.
| | - Xiao-Ping Yin
- Department of Radiology and Hebei Key Laboratory of Precise Imaging of Inflammation Related Tumors, The Affiliated Hospital of Hebei University, 212 Eastern Yuhua Road, Baoding, 071000, Hebei Province, China.
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Wu J, Zhou Z, Huang Y, Deng X, Zheng S, He S, Huang G, Hu B, Shi M, Liao W, Huang N. Radiofrequency ablation: mechanisms and clinical applications. MedComm (Beijing) 2024; 5:e746. [PMID: 39359691 PMCID: PMC11445673 DOI: 10.1002/mco2.746] [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: 03/10/2024] [Revised: 08/31/2024] [Accepted: 09/02/2024] [Indexed: 10/04/2024] Open
Abstract
Radiofrequency ablation (RFA), a form of thermal ablation, employs localized heat to induce protein denaturation in tissue cells, resulting in cell death. It has emerged as a viable treatment option for patients who are ineligible for surgery in various diseases, particularly liver cancer and other tumor-related conditions. In addition to directly eliminating tumor cells, RFA also induces alterations in the infiltrating cells within the tumor microenvironment (TME), which can significantly impact treatment outcomes. Moreover, incomplete RFA (iRFA) may lead to tumor recurrence and metastasis. The current challenge is to enhance the efficacy of RFA by elucidating its underlying mechanisms. This review discusses the clinical applications of RFA in treating various diseases and the mechanisms that contribute to the survival and invasion of tumor cells following iRFA, including the roles of heat shock proteins, hypoxia, and autophagy. Additionally, we analyze the changes occurring in infiltrating cells within the TME after iRFA. Finally, we provide a comprehensive summary of clinical trials involving RFA in conjunction with other treatment modalities in the field of cancer therapy, aiming to offer novel insights and references for improving the effectiveness of RFA.
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Affiliation(s)
- Jianhua Wu
- Department of Oncology, Nanfang HospitalSouthern Medical UniversityGuangzhouGuangdongChina
| | - Zhiyuan Zhou
- Department of Oncology, Nanfang HospitalSouthern Medical UniversityGuangzhouGuangdongChina
| | - Yuanwen Huang
- Department of Oncology, Nanfang HospitalSouthern Medical UniversityGuangzhouGuangdongChina
| | - Xinyue Deng
- Department of Oncology, Nanfang HospitalSouthern Medical UniversityGuangzhouGuangdongChina
| | - Siting Zheng
- Department of Oncology, Nanfang HospitalSouthern Medical UniversityGuangzhouGuangdongChina
| | - Shangwen He
- Department of Respiratory and Critical Care MedicineChronic Airways Diseases Laboratory, Nanfang Hospital, Southern Medical UniversityGuangzhouGuangdongChina
| | - Genjie Huang
- Department of Oncology, Nanfang HospitalSouthern Medical UniversityGuangzhouGuangdongChina
| | - Binghui Hu
- Department of Oncology, Nanfang HospitalSouthern Medical UniversityGuangzhouGuangdongChina
| | - Min Shi
- Department of Oncology, Nanfang HospitalSouthern Medical UniversityGuangzhouGuangdongChina
| | - Wangjun Liao
- Department of Oncology, Nanfang HospitalSouthern Medical UniversityGuangzhouGuangdongChina
| | - Na Huang
- Department of Oncology, Nanfang HospitalSouthern Medical UniversityGuangzhouGuangdongChina
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Khanam SJ, Rana MS, Islam MM, Khan MN. COVID-19 vaccine uptake in individuals with functional difficulty, disability, and comorbid conditions: insights from a national survey in Bangladesh. BMC Public Health 2024; 24:2531. [PMID: 39289678 PMCID: PMC11409621 DOI: 10.1186/s12889-024-20096-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2024] [Accepted: 09/16/2024] [Indexed: 09/19/2024] Open
Abstract
BACKGROUND COVID-19 vaccine uptake among individuals with disabilities is crucial for safeguarding their health and well-being. However, the extent of vaccine uptake among this group remains largely unknown in low- and middle-income countries. This study aims to assess the COVID-19 vaccine uptake among persons with functional difficulty, disability and/or comorbidity in Bangladesh and their associated factors. METHODS Data from 9,370 respondents extracted from the 2021 National Household Survey on Persons with Disability were analysed. The outcome variable was the uptake of at least one dose of the COVID-19 vaccine (yes, no). Key explanatory variables included the presence of disability (yes, no), comorbidity (yes, no), and both comorbidity and disability (yes, no) among persons with functional difficulty. The relationship between the outcome and explanatory variables was determined using mixed-effects multilevel logistic regressions adjusted for covariates. RESULTS The overall uptake of at least one dose of the COVID-19 vaccine among persons with functional difficulty was 57.37%, among persons with functional difficulty and disability was 48.63% and among persons with functional difficulty and single (57.85%) or multi-comorbidity (60.37%). Compared to the respondents with functional difficulty only, the adjusted odds ratio (aOR) of not receiving any dose of the COVID-19 vaccine for individuals with both functional difficulty and disability was 1.37 (95% CI, 1.22-1.53), and for individuals with functional difficulty, disability and one or more comorbid conditions was 1.30 (95% CI, 1.15-1.47). The aOR of receiving at least one dose of the COVID-19 vaccine among individuals with functional difficulty and one or more comorbid conditions was significantly higher than among those with functional difficulty only. CONCLUSION In Bangladesh, COVID-19 vaccine uptake was relatively low among individuals with disabilities. The existing COVID-19 vaccine rollout programs and similar future programs should prioritise individuals with disabilities and include targeted strategies to reach them.
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Affiliation(s)
- Shimlin Jahan Khanam
- Department of Population Science, Jatiya Kabi Kazi Nazrul Islam University, Trishal, Mymensingh, 2220, Bangladesh
| | - Md Shohel Rana
- Department of Population Science, Jatiya Kabi Kazi Nazrul Islam University, Trishal, Mymensingh, 2220, Bangladesh
| | - M Mofizul Islam
- Department of Public Health, La Trobe University, Melbourne, 3086, Australia
| | - Md Nuruzzaman Khan
- Department of Population Science, Jatiya Kabi Kazi Nazrul Islam University, Trishal, Mymensingh, 2220, Bangladesh.
- Nossal Institute for Global Health, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Australia.
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Vásquez-Venegas C, Sotomayor CG, Ramos B, Castañeda V, Pereira G, Cabrera-Vives G, Härtel S. Human-in-the-Loop-A Deep Learning Strategy in Combination with a Patient-Specific Gaussian Mixture Model Leads to the Fast Characterization of Volumetric Ground-Glass Opacity and Consolidation in the Computed Tomography Scans of COVID-19 Patients. J Clin Med 2024; 13:5231. [PMID: 39274444 PMCID: PMC11396404 DOI: 10.3390/jcm13175231] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2024] [Revised: 08/02/2024] [Accepted: 09/02/2024] [Indexed: 09/16/2024] Open
Abstract
Background/Objectives: The accurate quantification of ground-glass opacities (GGOs) and consolidation volumes has prognostic value in COVID-19 patients. Nevertheless, the accurate manual quantification of the corresponding volumes remains a time-consuming task. Deep learning (DL) has demonstrated good performance in the segmentation of normal lung parenchyma and COVID-19 pneumonia. We introduce a Human-in-the-Loop (HITL) strategy for the segmentation of normal lung parenchyma and COVID-19 pneumonia that is both time efficient and quality effective. Furthermore, we propose a Gaussian Mixture Model (GMM) to classify GGO and consolidation based on a probabilistic characterization and case-sensitive thresholds. Methods: A total of 65 Computed Tomography (CT) scans from 64 patients, acquired between March 2020 and June 2021, were randomly selected. We pretrained a 3D-UNet with an international dataset and implemented a HITL strategy to refine the local dataset with delineations by teams of medical interns, radiology residents, and radiologists. Following each HITL cycle, 3D-UNet was re-trained until the Dice Similarity Coefficients (DSCs) reached the quality criteria set by radiologists (DSC = 0.95/0.8 for the normal lung parenchyma/COVID-19 pneumonia). For the probabilistic characterization, a Gaussian Mixture Model (GMM) was fitted to the Hounsfield Units (HUs) of voxels from the CT scans of patients with COVID-19 pneumonia on the assumption that two distinct populations were superimposed: one for GGO and one for consolidation. Results: Manual delineation of the normal lung parenchyma and COVID-19 pneumonia was performed by seven teams on 65 CT scans from 64 patients (56 ± 16 years old (μ ± σ), 46 males, 62 with reported symptoms). Automated lung/COVID-19 pneumonia segmentation with a DSC > 0.96/0.81 was achieved after three HITL cycles. The HITL strategy improved the DSC by 0.2 and 0.5 for the normal lung parenchyma and COVID-19 pneumonia segmentation, respectively. The distribution of the patient-specific thresholds derived from the GMM yielded a mean of -528.4 ± 99.5 HU (μ ± σ), which is below most of the reported fixed HU thresholds. Conclusions: The HITL strategy allowed for fast and effective annotations, thereby enhancing the quality of segmentation for a local CT dataset. Probabilistic characterization of COVID-19 pneumonia by the GMM enabled patient-specific segmentation of GGO and consolidation. The combination of both approaches is essential to gain confidence in DL approaches in our local environment. The patient-specific probabilistic approach, when combined with the automatic quantification of COVID-19 imaging findings, enhances the understanding of GGO and consolidation during the course of the disease, with the potential to improve the accuracy of clinical predictions.
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Affiliation(s)
- Constanza Vásquez-Venegas
- Department of Computer Science, Faculty of Engineering, University of Concepción, Concepción 4030000, Chile
- Laboratory for Scientific Image Analysis SCIAN-Lab, Integrative Biology Program, Institute of Biomedical Sciences, Faculty of Medicine, University of Chile, Santiago 8380453, Chile
| | - Camilo G Sotomayor
- Laboratory for Scientific Image Analysis SCIAN-Lab, Integrative Biology Program, Institute of Biomedical Sciences, Faculty of Medicine, University of Chile, Santiago 8380453, Chile
- Radiology Department, University of Chile Clinical Hospital, University of Chile, Santiago 8380420, Chile
| | - Baltasar Ramos
- School of Medicine, Faculty of Medicine, University of Chile, Santiago 8380453, Chile
| | - Víctor Castañeda
- Center of Medical Informatics and Telemedicine & National Center of Health Information Systems, Faculty of Medicine, University of Chile, Santiago 8380453, Chile
- Department of Medical Technology, Faculty of Medicine, University of Chile, Santiago 8380453, Chile
| | - Gonzalo Pereira
- Radiology Department, University of Chile Clinical Hospital, University of Chile, Santiago 8380420, Chile
| | - Guillermo Cabrera-Vives
- Department of Computer Science, Faculty of Engineering, University of Concepción, Concepción 4030000, Chile
| | - Steffen Härtel
- Laboratory for Scientific Image Analysis SCIAN-Lab, Integrative Biology Program, Institute of Biomedical Sciences, Faculty of Medicine, University of Chile, Santiago 8380453, Chile
- Center of Medical Informatics and Telemedicine & National Center of Health Information Systems, Faculty of Medicine, University of Chile, Santiago 8380453, Chile
- Biomedical Neuroscience Institute, Faculty of Medicine, University of Chile, Santiago 8380453, Chile
- National Center for Health Information Systems, Santiago 8380453, Chile
- Center of Mathematical Modelling, University of Chile, Santiago 8380453, Chile
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Gao Y, Dong Y, Bu Q, Gong Z, Wang W, Zhou Z, Gao Y, Liu L, Wu M, Zhang J, Liang L, Li H, Jiang M, Luo Z, Ma Y, Zhang X, Hu Z. Antiviral Effectiveness, Clinical Outcomes, and Artificial Intelligence Imaging Analysis for Hospitalized COVID-19 Patients Receiving Antivirals. Influenza Other Respir Viruses 2024; 18:e70006. [PMID: 39284764 PMCID: PMC11405122 DOI: 10.1111/irv.70006] [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: 04/23/2024] [Revised: 07/14/2024] [Accepted: 08/29/2024] [Indexed: 09/22/2024] Open
Abstract
INTRODUCTION There is still a lack of clinical evidence comprehensively evaluating the effectiveness of antiviral treatments for COVID-19 hospitalized patients. METHODS A retrospective cohort study was conducted at Beijing You'An Hospital, focusing on patients treated with nirmatrelvir/ritonavir or azvudine. The study employed a tripartite analysis-viral dynamics, survival curve analysis, and AI-based radiological analysis of pulmonary CT images-aiming to assess the severity of pneumonia. RESULTS Of 370 patients treated with either nirmatrelvir/ritonavir or azvudine as monotherapy, those in the nirmatrelvir/ritonavir group experienced faster viral clearance than those treated with azvudine (5.4 days vs. 8.4 days, p < 0.001). No significant differences were observed in the survival curves between the two drug groups. AI-based radiological analysis revealed that patients in the nirmatrelvir group had more severe pneumonia conditions (infection ratio is 11.1 vs. 5.35, p = 0.007). Patients with an infection ratio higher than 9.2 had nearly three times the mortality rate compared to those with an infection ratio lower than 9.2. CONCLUSIONS Our study suggests that in real-world studies regarding hospitalized patients with COVID-19 pneumonia, the antiviral effect of nirmatrelvir/ritonavir is significantly superior to azvudine, but the choice of antiviral agents is not necessarily linked to clinical outcomes; the severity of pneumonia at admission is the most important factor to determine prognosis. Additionally, our findings indicate that pulmonary AI imaging analysis can be a powerful tool for predicting patient prognosis and guiding clinical decision-making.
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Affiliation(s)
- Yuan Gao
- Fourth Department of Liver Disease Center, Beijing You'An Hospital, Capital Medical University, Beijing, China
| | - Yixi Dong
- School of Management, University of Science and Technology of China, Hefei, China
| | - Qiushi Bu
- Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China
| | - Zhijie Gong
- School of Management, University of Science and Technology of China, Hefei, China
| | - Wei Wang
- Department of Radiology, Beijing Youan Hospital, Capital Medical University, Beijing, China
| | - Zhongkai Zhou
- Department of Radiology, Beijing Youan Hospital, Capital Medical University, Beijing, China
| | - Yunyi Gao
- School of Basic Medicine, Qingdao University, Qingdao, China
| | - Liwei Liu
- Fourth Department of Liver Disease Center, Beijing You'An Hospital, Capital Medical University, Beijing, China
| | - Menghua Wu
- Department of Urology, Beijing You'An Hospital, Capital Medical University, Beijing, China
| | - Jiaying Zhang
- Department of Infectious Diseases, Beijing You'An Hospital, Capital Medical University, Beijing, China
| | - Lianchun Liang
- Department of Infectious Diseases, Beijing You'An Hospital, Capital Medical University, Beijing, China
| | - Hongjun Li
- Department of Radiology, Beijing Youan Hospital, Capital Medical University, Beijing, China
| | - Mengxi Jiang
- Department of Pharmacology, School of Basic Medical Sciences, Capital Medical University, Beijing, China
| | - Zujin Luo
- Department of Respiratory and Critical Care Medicine, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China
| | - Yingmin Ma
- Department of Respiratory and Critical Care Medicine, Beijing You'An Hospital, Capital Medical University, Beijing, China
| | - Xinyu Zhang
- School of Management, University of Science and Technology of China, Hefei, China
- Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China
| | - Zhongjie Hu
- Liver Disease Center, Beijing You'An Hospital, Capital Medical University, Beijing, China
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Xing H, Gu S, Li Z, Wei XE, He L, Liu Q, Feng H, Wang N, Huang H, Fan Y. Incorporation of Chest Computed Tomography Quantification to Predict Outcomes for Patients on Hemodialysis with COVID-19. KIDNEY DISEASES (BASEL, SWITZERLAND) 2024; 10:284-294. [PMID: 39131882 PMCID: PMC11309758 DOI: 10.1159/000539568] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/13/2024] [Accepted: 05/26/2024] [Indexed: 08/13/2024]
Abstract
Introduction Patients undergoing maintenance hemodialysis are vulnerable to coronavirus disease 2019 (COVID-19), exhibiting a high risk of hospitalization and mortality. Thus, early identification and intervention are important to prevent disease progression in these patients. Methods This was a two-center retrospective observational study of patients on hemodialysis diagnosed with COVID-19 at the Lingang and Xuhui campuses of Shanghai Sixth People's Hospital. Patients were randomized into the training (130) and validation cohorts (54), while 59 additional patients served as an independent external validation cohort. Artificial intelligence-based parameters of chest computed tomography (CT) were quantified, and a nomogram for patient outcomes at 14 and 28 days was created by screening quantitative CT measures, clinical data, and laboratory examination items, using univariate and multivariate Cox regression models. Results The median dialysis duration was 48 (interquartile range, 24-96) months. Age, diabetes mellitus, serum phosphorus level, lymphocyte count, and chest CT score were identified as independent prognostic indicators and included in the nomogram. The concordance index values were 0.865, 0.914, and 0.885 in the training, internal validation, and external validation cohorts, respectively. Calibration plots showed good agreement between the expected and actual outcomes. Conclusion This is the first study in which a reliable nomogram was developed to predict short-term outcomes and survival probabilities in patients with COVID-19 on hemodialysis. This model may be helpful to clinicians in treating COVID-19, managing serum phosphorus, and adjusting the dialysis strategies for these vulnerable patients to prevent disease progression in the context of COVID-19 and continuous emergence of novel viruses.
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Affiliation(s)
- Haifan Xing
- Department of Nephrology, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Sijie Gu
- Department of Nephrology, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Ze Li
- Department of Nephrology, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xiao-er Wei
- Institute of Diagnostic and Interventional Radiology, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Li He
- Department of Nephrology, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Qiye Liu
- Department of Nephrology, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Haoran Feng
- Department of Nephrology, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Niansong Wang
- Department of Nephrology, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Hengye Huang
- School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Ying Fan
- Department of Nephrology, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
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Nardi C, Magnini A, Rastrelli V, Zantonelli G, Calistri L, Lorini C, Luzzi V, Gori L, Ciani L, Morecchiato F, Simonetti V, Peired AJ, Landini N, Cavigli E, Yang G, Guiot J, Tomassetti S, Colagrande S. Laboratory data and broncho-alveolar lavage on Covid-19 patients with no intensive care unit admission: Correlation with chest CT features and clinical outcomes. Medicine (Baltimore) 2024; 103:e39028. [PMID: 39029011 PMCID: PMC11398758 DOI: 10.1097/md.0000000000039028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 07/21/2024] Open
Abstract
Broncho-alveolar lavage (BAL) is indicated in cases of uncertain diagnosis but high suspicion of Sars-Cov-2 infection allowing to collect material for microbiological culture to define the presence of coinfection or super-infection. This prospective study investigated the correlation between chest computed tomography (CT) findings, Covid-19 Reporting and Data System score, and clinical outcomes in Coronavirus disease 2019 (Covid-19) patients who underwent BAL with the aim of predicting outcomes such as lung coinfection, respiratory failure, and hospitalization length based on chest CT abnormalities. Study population included 34 patients (range 38-90 years old; 20 males, 14 females) with a positive nucleic acid amplification test for Covid-19 infection, suitable BAL examination, and good quality chest CT scan in the absence of lung cancer history. Pulmonary coinfections were found in 20.6% of patients, predominantly caused by bacteria. Specific correlations were found between right middle lobe involvement and pulmonary co-infections. Severe lung injury (PaO2/FiO2 ratio of 100-200) was associated with substantial involvement of right middle, right upper, and left lower lobes. No significant correlation was found between chest CT findings and inflammatory markers (C-reactive protein, procalcitonin) or hospitalization length of stay. Specific chest CT patterns, especially in right middle lobe, could serve as indicators for the presence of co-infections and disease severity in noncritically ill Covid-19 patients, aiding clinicians in timely interventions and personalized treatment strategies.
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Affiliation(s)
- Cosimo Nardi
- Department of Experimental and Clinical Biomedical Sciences, Radiodiagnostic Unit n. 2, University of Florence - Azienda Ospedaliero-Universitaria Careggi, Florence, Italy
| | - Andrea Magnini
- Department of Experimental and Clinical Biomedical Sciences, Radiodiagnostic Unit n. 2, University of Florence - Azienda Ospedaliero-Universitaria Careggi, Florence, Italy
| | - Vieri Rastrelli
- Department of Experimental and Clinical Biomedical Sciences, Radiodiagnostic Unit n. 2, University of Florence - Azienda Ospedaliero-Universitaria Careggi, Florence, Italy
| | - Giulia Zantonelli
- Department of Experimental and Clinical Biomedical Sciences, Radiodiagnostic Unit n. 2, University of Florence - Azienda Ospedaliero-Universitaria Careggi, Florence, Italy
| | - Linda Calistri
- Department of Experimental and Clinical Biomedical Sciences, Radiodiagnostic Unit n. 2, University of Florence - Azienda Ospedaliero-Universitaria Careggi, Florence, Italy
| | - Chiara Lorini
- Department of Health Science, University of Florence, Florence, Italy
| | - Valentina Luzzi
- Department of Clinical and Experimental Medicine, Interventional Pulmonology Unit, Careggi University Hospital, Florence, Italy
| | - Leonardo Gori
- Department of Clinical and Experimental Medicine, Interventional Pulmonology Unit, Careggi University Hospital, Florence, Italy
| | - Luca Ciani
- Department of Clinical and Experimental Medicine, Interventional Pulmonology Unit, Careggi University Hospital, Florence, Italy
| | - Fabio Morecchiato
- Department of Experimental and Clinical Medicine, University of Florence, Florence, Italy
- Clinical Microbiology and Virology Unit, Florence Careggi University Hospital, Florence, Italy
| | | | - Anna Julie Peired
- Department of Experimental and Clinical Biomedical Sciences "Mario Serio", University of Florence, Florence, Italy
| | - Nicholas Landini
- Department of Radiological, Oncological and Pathological Sciences, Policlinico Umberto I Hospital, "Sapienza" Rome University, Rome, Italy
| | - Edoardo Cavigli
- Department of Radiology, Careggi University Hospital, Florence, Italy
| | - Guang Yang
- Bioengineering Department and Imperial-X, Imperial College London, London, UK
| | - Julien Guiot
- Department of Respiratory Medicine, University Hospital of Liège, Liège, Belgium
| | - Sara Tomassetti
- Department of Clinical and Experimental Medicine, Interventional Pulmonology Unit, Careggi University Hospital, Florence, Italy
| | - Stefano Colagrande
- Department of Experimental and Clinical Biomedical Sciences, Radiodiagnostic Unit n. 2, University of Florence - Azienda Ospedaliero-Universitaria Careggi, Florence, Italy
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Siragam V, Maltseva M, Castonguay N, Galipeau Y, Srinivasan MM, Soto JH, Dankar S, Langlois MA. Seasonal human coronaviruses OC43, 229E, and NL63 induce cell surface modulation of entry receptors and display host cell-specific viral replication kinetics. Microbiol Spectr 2024; 12:e0422023. [PMID: 38864599 PMCID: PMC11218498 DOI: 10.1128/spectrum.04220-23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2024] [Accepted: 04/25/2024] [Indexed: 06/13/2024] Open
Abstract
The emergence of the COVID-19 pandemic prompted an increased interest in seasonal human coronaviruses. OC43, 229E, NL63, and HKU1 are endemic seasonal coronaviruses that cause the common cold and are associated with generally mild respiratory symptoms. In this study, we identified cell lines that exhibited cytopathic effects (CPE) upon infection by three of these coronaviruses and characterized their viral replication kinetics and the effect of infection on host surface receptor expression. We found that NL63 produced CPE in LLC-MK2 cells, while OC43 produced CPE in MRC-5, HCT-8, and WI-38 cell lines, while 229E produced CPE in MRC-5 and WI-38 by day 3 post-infection. We observed a sharp increase in nucleocapsid and spike viral RNA (vRNA) from day 3 to day 5 post-infection for all viruses; however, the abundance and the proportion of vRNA copies measured in the supernatants and cell lysates of infected cells varied considerably depending on the virus-host cell pair. Importantly, we observed modulation of coronavirus entry and attachment receptors upon infection. Infection with 229E and OC43 led to a downregulation of CD13 and GD3, respectively. In contrast, infection with NL63 and OC43 leads to an increase in ACE2 expression. Attempts to block entry of NL63 using either soluble ACE2 or anti-ACE2 monoclonal antibodies demonstrated the potential of these strategies to greatly reduce infection. Overall, our results enable a better understanding of seasonal coronaviruses infection kinetics in permissive cell lines and reveal entry receptor modulation that may have implications in facilitating co-infections with multiple coronaviruses in humans.IMPORTANCESeasonal human coronavirus is an important cause of the common cold associated with generally mild upper respiratory tract infections that can result in respiratory complications for some individuals. There are no vaccines available for these viruses, with only limited antiviral therapeutic options to treat the most severe cases. A better understanding of how these viruses interact with host cells is essential to identify new strategies to prevent infection-related complications. By analyzing viral replication kinetics in different permissive cell lines, we find that cell-dependent host factors influence how viral genes are expressed and virus particles released. We also analyzed entry receptor expression on infected cells and found that these can be up- or down-modulated depending on the infecting coronavirus. Our findings raise concerns over the possibility of infection enhancement upon co-infection by some coronaviruses, which may facilitate genetic recombination and the emergence of new variants and strains.
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MESH Headings
- Humans
- Virus Replication
- Coronavirus NL63, Human/physiology
- Coronavirus NL63, Human/genetics
- Coronavirus 229E, Human/physiology
- Coronavirus 229E, Human/genetics
- Coronavirus OC43, Human/physiology
- Coronavirus OC43, Human/genetics
- Cell Line
- Virus Internalization
- Seasons
- Kinetics
- Receptors, Virus/metabolism
- Receptors, Virus/genetics
- Common Cold/virology
- Common Cold/metabolism
- SARS-CoV-2/physiology
- SARS-CoV-2/genetics
- SARS-CoV-2/metabolism
- RNA, Viral/metabolism
- RNA, Viral/genetics
- Animals
- COVID-19/virology
- COVID-19/metabolism
- Coronavirus/physiology
- Coronavirus/genetics
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Affiliation(s)
- Vinayakumar Siragam
- Department of Biochemistry, Microbiology and Immunology, Faculty of Medicine, University of Ottawa, Ottawa, Canada
| | - Mariam Maltseva
- Department of Biochemistry, Microbiology and Immunology, Faculty of Medicine, University of Ottawa, Ottawa, Canada
| | - Nicolas Castonguay
- Department of Biochemistry, Microbiology and Immunology, Faculty of Medicine, University of Ottawa, Ottawa, Canada
| | - Yannick Galipeau
- Department of Biochemistry, Microbiology and Immunology, Faculty of Medicine, University of Ottawa, Ottawa, Canada
| | - Mrudhula Madapuji Srinivasan
- Department of Biochemistry, Microbiology and Immunology, Faculty of Medicine, University of Ottawa, Ottawa, Canada
| | - Justino Hernandez Soto
- Department of Biochemistry, Microbiology and Immunology, Faculty of Medicine, University of Ottawa, Ottawa, Canada
| | - Samar Dankar
- Department of Biochemistry, Microbiology and Immunology, Faculty of Medicine, University of Ottawa, Ottawa, Canada
| | - Marc-André Langlois
- Department of Biochemistry, Microbiology and Immunology, Faculty of Medicine, University of Ottawa, Ottawa, Canada
- The Center for Infection, Immunity, and Inflammation (CI3), University of Ottawa, Ottawa, Canada
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9
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Lu F, Zhang Z, Zhao S, Lin X, Zhang Z, Jin B, Gu W, Chen J, Wu X. CMM: A CNN-MLP Model for COVID-19 Lesion Segmentation and Severity Grading. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2024; 21:789-802. [PMID: 37028373 DOI: 10.1109/tcbb.2023.3253901] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
In this paper, a CNN-MLP model (CMM) is proposed for COVID-19 lesion segmentation and severity grading in CT images. The CMM starts by lung segmentation using UNet, and then segmenting the lesion from the lung region using a multi-scale deep supervised UNet (MDS-UNet), finally implementing the severity grading by a multi-layer preceptor (MLP). In MDS-UNet, shape prior information is fused with the input CT image to reduce the searching space of the potential segmentation outputs. The multi-scale input compensates for the loss of edge contour information in convolution operations. In order to enhance the learning of multiscale features, the multi-scale deep supervision extracts supervision signals from different upsampling points on the network. In addition, it is empirical that the lesion which has a whiter and denser appearance tends to be more severe in the COVID-19 CT image. So, the weighted mean gray-scale value (WMG) is proposed to depict this appearance, and together with the lung and lesion area to serve as input features for the severity grading in MLP. To improve the precision of lesion segmentation, a label refinement method based on the Frangi vessel filter is also proposed. Comparative experiments on COVID-19 public datasets show that our proposed CMM achieves high accuracy on COVID-19 lesion segmentation and severity grading.
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10
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Meng X, Sun K, Xu J, He X, Shen D. Multi-Modal Modality-Masked Diffusion Network for Brain MRI Synthesis With Random Modality Missing. IEEE TRANSACTIONS ON MEDICAL IMAGING 2024; 43:2587-2598. [PMID: 38393846 DOI: 10.1109/tmi.2024.3368664] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/25/2024]
Abstract
Synthesis of unavailable imaging modalities from available ones can generate modality-specific complementary information and enable multi-modality based medical images diagnosis or treatment. Existing generative methods for medical image synthesis are usually based on cross-modal translation between acquired and missing modalities. These methods are usually dedicated to specific missing modality and perform synthesis in one shot, which cannot deal with varying number of missing modalities flexibly and construct the mapping across modalities effectively. To address the above issues, in this paper, we propose a unified Multi-modal Modality-masked Diffusion Network (M2DN), tackling multi-modal synthesis from the perspective of "progressive whole-modality inpainting", instead of "cross-modal translation". Specifically, our M2DN considers the missing modalities as random noise and takes all the modalities as a unity in each reverse diffusion step. The proposed joint synthesis scheme performs synthesis for the missing modalities and self-reconstruction for the available ones, which not only enables synthesis for arbitrary missing scenarios, but also facilitates the construction of common latent space and enhances the model representation ability. Besides, we introduce a modality-mask scheme to encode availability status of each incoming modality explicitly in a binary mask, which is adopted as condition for the diffusion model to further enhance the synthesis performance of our M2DN for arbitrary missing scenarios. We carry out experiments on two public brain MRI datasets for synthesis and downstream segmentation tasks. Experimental results demonstrate that our M2DN outperforms the state-of-the-art models significantly and shows great generalizability for arbitrary missing modalities.
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11
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Liu X, Sun Y, Song H, Zhang W, Liu T, Chu Z, Gu X, Ma Z, Jin W. Nanomaterials-based electrochemical biosensors for diagnosis of COVID-19. Talanta 2024; 274:125994. [PMID: 38547841 DOI: 10.1016/j.talanta.2024.125994] [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: 01/01/2024] [Revised: 03/15/2024] [Accepted: 03/24/2024] [Indexed: 05/04/2024]
Abstract
Since the outbreak of corona virus disease 2019 (COVID-19), this pandemic has caused severe death and infection worldwide. Owing to its strong infectivity, long incubation period, and nonspecific symptoms, the early diagnosis is essential to reduce risk of the severe illness. The electrochemical biosensor, as a fast and sensitive technique for quantitative analysis of body fluids, has been widely studied to diagnose different biomarkers caused at different infective stages of COVID-19 virus (SARS-CoV-2). Recently, many reports have proved that nanomaterials with special architectures and size effects can effectively promote the biosensing performance on the COVID-19 diagnosis, there are few comprehensive summary reports yet. Therefore, in this review, we will pay efforts on recent progress of advanced nanomaterials-facilitated electrochemical biosensors for the COVID-19 detections. The process of SARS-CoV-2 infection in humans will be briefly described, as well as summarizing the types of sensors that should be designed for different infection processes. Emphasis will be supplied to various functional nanomaterials which dominate the biosensing performance for comparison, expecting to provide a rational guidance on the material selection of biosensor construction for people. Finally, we will conclude the perspective on the design of superior nanomaterials-based biosensors facing the unknown virus in future.
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Affiliation(s)
- Xinxin Liu
- State Key Laboratory of Materials-Oriented Chemical Engineering, College of Chemical Engineering, Nanjing Tech University, NO.30 Puzhu Road(S), Nanjing, 211816, PR China
| | - Yifan Sun
- State Key Laboratory of Materials-Oriented Chemical Engineering, College of Chemical Engineering, Nanjing Tech University, NO.30 Puzhu Road(S), Nanjing, 211816, PR China
| | - Huaiyu Song
- State Key Laboratory of Materials-Oriented Chemical Engineering, College of Chemical Engineering, Nanjing Tech University, NO.30 Puzhu Road(S), Nanjing, 211816, PR China
| | - Wei Zhang
- Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, 210008, PR China
| | - Tao Liu
- State Key Laboratory of Materials-Oriented Chemical Engineering, College of Chemical Engineering, Nanjing Tech University, NO.30 Puzhu Road(S), Nanjing, 211816, PR China.
| | - Zhenyu Chu
- State Key Laboratory of Materials-Oriented Chemical Engineering, College of Chemical Engineering, Nanjing Tech University, NO.30 Puzhu Road(S), Nanjing, 211816, PR China
| | - Xiaoping Gu
- Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, 210008, PR China.
| | - Zhengliang Ma
- Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, 210008, PR China
| | - Wanqin Jin
- State Key Laboratory of Materials-Oriented Chemical Engineering, College of Chemical Engineering, Nanjing Tech University, NO.30 Puzhu Road(S), Nanjing, 211816, PR China.
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12
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Mitchell BI, Yazel Eiser IE, Kallianpur KJ, Gangcuangco LM, Chow DC, Ndhlovu LC, Paul R, Shikuma CM. Dynamics of peripheral T cell exhaustion and monocyte subpopulations in neurocognitive impairment and brain atrophy in chronic HIV infection. J Neurovirol 2024:10.1007/s13365-024-01223-w. [PMID: 38949728 DOI: 10.1007/s13365-024-01223-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2024] [Revised: 06/06/2024] [Accepted: 06/12/2024] [Indexed: 07/02/2024]
Abstract
BACKGROUND HIV-associated neurocognitive disorders (HAND) is hypothesized to be a result of myeloid cell-induced neuro-inflammation in the central nervous system that may be initiated in the periphery, but the contribution of peripheral T cells in HAND pathogenesis remains poorly understood. METHODS We assessed markers of T cell activation (HLA-DR + CD38+), immunosenescence (CD57 + CD28-), and immune-exhaustion (TIM-3, PD-1 and TIGIT) as well as monocyte subsets (classical, intermediate, and non-classical) by flow cytometry in peripheral blood derived from individuals with HIV on long-term stable anti-retroviral therapy (ART). Additionally, normalized neuropsychological (NP) composite test z-scores were obtained and regional brain volumes were assessed by magnetic resonance imaging (MRI). Relationships between proportions of immune phenotypes (of T-cells and monocytes), NP z-scores, and brain volumes were analyzed using Pearson correlations and multiple linear regression models. RESULTS Of N = 51 participants, 84.3% were male, 86.3% had undetectable HIV RNA < 50 copies/ml, median age was 52 [47, 57] years and median CD4 T cell count was 479 [376, 717] cells/uL. Higher CD4 T cells expressing PD-1 + and/or TIM-3 + were associated with lower executive function and working memory and higher CD8 T cells expressing PD-1+ and/or TIM-3+ were associated with reduced brain volumes in multiple regions (putamen, nucleus accumbens, cerebellar cortex, and subcortical gray matter). Furthermore, higher single or dual frequencies of PD-1 + and TIM-3 + expressing CD4 and CD8 T-cells correlated with higher CD16 + monocyte numbers. CONCLUSIONS This study reinforces evidence that T cells, particularly those with immune exhaustion phenotypes, are associated with neurocognitive impairment and brain atrophy in people living with HIV on ART. Relationships revealed between T-cell immune exhaustion and inflammatory in CD16+ monocytes uncover interrelated cellular processes likely involved in the immunopathogenesis of HAND.
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Affiliation(s)
- Brooks I Mitchell
- Hawaii Center for AIDS, John A. Burns School of Medicine, University of Hawaii at Manoa, 651 Ilalo St., Biomedical Sciences Building 231, Honolulu, HI, 96813, USA
- Department of Tropical Medicine, John A Burns School of Medicine, University of Hawaii, Honolulu, HI, USA
| | - Isabelle E Yazel Eiser
- Hawaii Center for AIDS, John A. Burns School of Medicine, University of Hawaii at Manoa, 651 Ilalo St., Biomedical Sciences Building 231, Honolulu, HI, 96813, USA
- Department of Tropical Medicine, John A Burns School of Medicine, University of Hawaii, Honolulu, HI, USA
| | - Kalpana J Kallianpur
- Hawaii Center for AIDS, John A. Burns School of Medicine, University of Hawaii at Manoa, 651 Ilalo St., Biomedical Sciences Building 231, Honolulu, HI, 96813, USA
- Department of Tropical Medicine, John A Burns School of Medicine, University of Hawaii, Honolulu, HI, USA
- Kamehameha Schools- Kapālama, Honolulu, HI, USA
| | - Louie Mar Gangcuangco
- Hawaii Center for AIDS, John A. Burns School of Medicine, University of Hawaii at Manoa, 651 Ilalo St., Biomedical Sciences Building 231, Honolulu, HI, 96813, USA
- Department of Medicine, John A Burns School of Medicine, University of Hawaii, Honolulu, HI, USA
| | - Dominic C Chow
- Hawaii Center for AIDS, John A. Burns School of Medicine, University of Hawaii at Manoa, 651 Ilalo St., Biomedical Sciences Building 231, Honolulu, HI, 96813, USA
- Department of Medicine, John A Burns School of Medicine, University of Hawaii, Honolulu, HI, USA
| | - Lishomwa C Ndhlovu
- Department of Tropical Medicine, John A Burns School of Medicine, University of Hawaii, Honolulu, HI, USA
- Division of Infectious Diseases, Department of Medicine, Weill Cornell Medicine New York, New York, USA
| | - Robert Paul
- Department of Psychological Sciences, Missouri Institute of Mental Health, University of Missouri-St. Louis, St. Louis, MO, USA
| | - Cecilia M Shikuma
- Hawaii Center for AIDS, John A. Burns School of Medicine, University of Hawaii at Manoa, 651 Ilalo St., Biomedical Sciences Building 231, Honolulu, HI, 96813, USA.
- Department of Tropical Medicine, John A Burns School of Medicine, University of Hawaii, Honolulu, HI, USA.
- Department of Medicine, John A Burns School of Medicine, University of Hawaii, Honolulu, HI, USA.
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13
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Al-Momani H. A Literature Review on the Relative Diagnostic Accuracy of Chest CT Scans versus RT-PCR Testing for COVID-19 Diagnosis. Tomography 2024; 10:935-948. [PMID: 38921948 PMCID: PMC11209112 DOI: 10.3390/tomography10060071] [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: 04/04/2024] [Revised: 06/09/2024] [Accepted: 06/11/2024] [Indexed: 06/27/2024] Open
Abstract
BACKGROUND Reverse transcription polymerase chain reaction (RT-PCR) is the main technique used to identify COVID-19 from respiratory samples. It has been suggested in several articles that chest CTs could offer a possible alternate diagnostic tool for COVID-19; however, no professional medical body recommends using chest CTs as an early COVID-19 detection modality. This literature review examines the use of CT scans as a diagnostic tool for COVID-19. METHOD A comprehensive search of research works published in peer-reviewed journals was carried out utilizing precisely stated criteria. The search was limited to English-language publications, and studies of COVID-19-positive patients diagnosed using both chest CT scans and RT-PCR tests were sought. For this review, four databases were consulted: these were the Cochrane and ScienceDirect catalogs, and the CINAHL and Medline databases made available by EBSCOhost. FINDINGS In total, 285 possibly pertinent studies were found during an initial search. After applying inclusion and exclusion criteria, six studies remained for analysis. According to the included studies, chest CT scans were shown to have a 44 to 98% sensitivity and 25 to 96% specificity in terms of COVID-19 diagnosis. However, methodological limitations were identified in all studies included in this review. CONCLUSION RT-PCR is still the suggested first-line diagnostic technique for COVID-19; while chest CT is adequate for use in symptomatic patients, it is not a sufficiently robust diagnostic tool for the primary screening of COVID-19.
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Affiliation(s)
- Hafez Al-Momani
- Department of Microbiology, Pathology and Forensic Medicine, Faculty of Medicine, The Hashemite University, Zarqa 1133, Jordan
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14
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Nimer NA, Nimer SN. Immunization against Medically Important Human Coronaviruses of Public Health Concern. THE CANADIAN JOURNAL OF INFECTIOUS DISEASES & MEDICAL MICROBIOLOGY = JOURNAL CANADIEN DES MALADIES INFECTIEUSES ET DE LA MICROBIOLOGIE MEDICALE 2024; 2024:9952803. [PMID: 38938549 PMCID: PMC11208815 DOI: 10.1155/2024/9952803] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/04/2023] [Revised: 01/14/2024] [Accepted: 05/20/2024] [Indexed: 06/29/2024]
Abstract
SARS-CoV-2 is a virus that affects the human immune system. It was observed to be on the rise since the beginning of 2020 and turned into a life-threatening pandemic. Scientists have tried to develop a possible preventive and therapeutic drug against severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) and other related coronaviruses by assessing COVID-19-recovered persons' immunity. This study aims to review immunization against SARS-CoV-2, along with exploring the interventions that have been developed for the prevention of SARS-CoV-2. This study also highlighted the role of phototherapy in treating SARS-CoV infection. The study adopted a review approach to gathering the information available and the progress that has been made in the treatment and prevention of COVID-19. Various vaccinations, including nucleotide, subunit, and vector-based vaccines, as well as attenuated and inactivated forms that have already been shown to have prophylactic efficacy against the Middle East respiratory syndrome coronavirus (MERS-CoV) and SARS-CoV, have been summarized. Neutralizing and non-neutralizing antibodies are all associated with viral infections. Because there is no specific antiviral vaccine or therapies for coronaviruses, the main treatment strategy is supportive care, which is reinforced by combining broad-spectrum antivirals, convalescent plasma, and corticosteroids. COVID-19 has been a challenge to keep reconsidering the usual approaches to regulatory evaluation as a result of getting mixed and complicated findings on the vaccines, as well as licensing procedures. However, it is observed that medicinal herbs also play an important role in treating infection of the upper respiratory tract, the principal symptom of SARS-CoV due to their natural bioactive composite. However, some Traditional Chinese Medicines contain mutagens and nephrotoxins and the toxicological properties of the majority of Chinese herbal remedies are unknown. Therefore, to treat the COVID-19 infection along with conventional treatment, it is recommended that herb-drug interaction be examined thoroughly.
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Affiliation(s)
- Nabil A. Nimer
- Faculty of Pharmacy, Philadelphia University, Amman, Jordan
| | - Seema N. Nimer
- School of Medicine, The University of Jordan, Amman, Jordan
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15
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Wang N, Dong G, Qiao R, Yin X, Lin S. Bringing Artificial Intelligence (AI) into Environmental Toxicology Studies: A Perspective of AI-Enabled Zebrafish High-Throughput Screening. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2024; 58:9487-9499. [PMID: 38691763 DOI: 10.1021/acs.est.4c00480] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/03/2024]
Abstract
The booming development of artificial intelligence (AI) has brought excitement to many research fields that could benefit from its big data analysis capability for causative relationship establishment and knowledge generation. In toxicology studies using zebrafish, the microscopic images and videos that illustrate the developmental stages, phenotypic morphologies, and animal behaviors possess great potential to facilitate rapid hazard assessment and dissection of the toxicity mechanism of environmental pollutants. However, the traditional manual observation approach is both labor-intensive and time-consuming. In this Perspective, we aim to summarize the current AI-enabled image and video analysis tools to realize the full potential of AI. For image analysis, AI-based tools allow fast and objective determination of morphological features and extraction of quantitative information from images of various sorts. The advantages of providing accurate and reproducible results while avoiding human intervention play a critical role in speeding up the screening process. For video analysis, AI-based tools enable the tracking of dynamic changes in both microscopic cellular events and macroscopic animal behaviors. The subtle changes revealed by video analysis could serve as sensitive indicators of adverse outcomes. With AI-based toxicity analysis in its infancy, exciting developments and applications are expected to appear in the years to come.
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Affiliation(s)
- Nan Wang
- College of Environmental Science and Engineering, Biomedical Multidisciplinary Innovation Research Institute, Shanghai East Hospital, Tongji University, Shanghai 200092, People's Republic of China
- Key Laboratory of Yangtze River Water Environment, Ministry of Education; Shanghai Institute of Pollution Control and Ecological Security, Shanghai 200092, People's Republic of China
| | - Gongqing Dong
- College of Environmental Science and Engineering, Biomedical Multidisciplinary Innovation Research Institute, Shanghai East Hospital, Tongji University, Shanghai 200092, People's Republic of China
- Key Laboratory of Yangtze River Water Environment, Ministry of Education; Shanghai Institute of Pollution Control and Ecological Security, Shanghai 200092, People's Republic of China
| | - Ruxia Qiao
- College of Environmental Science and Engineering, Biomedical Multidisciplinary Innovation Research Institute, Shanghai East Hospital, Tongji University, Shanghai 200092, People's Republic of China
- Key Laboratory of Yangtze River Water Environment, Ministry of Education; Shanghai Institute of Pollution Control and Ecological Security, Shanghai 200092, People's Republic of China
| | - Xiang Yin
- College of Environmental Science and Engineering, Biomedical Multidisciplinary Innovation Research Institute, Shanghai East Hospital, Tongji University, Shanghai 200092, People's Republic of China
- Key Laboratory of Yangtze River Water Environment, Ministry of Education; Shanghai Institute of Pollution Control and Ecological Security, Shanghai 200092, People's Republic of China
| | - Sijie Lin
- College of Environmental Science and Engineering, Biomedical Multidisciplinary Innovation Research Institute, Shanghai East Hospital, Tongji University, Shanghai 200092, People's Republic of China
- Key Laboratory of Yangtze River Water Environment, Ministry of Education; Shanghai Institute of Pollution Control and Ecological Security, Shanghai 200092, People's Republic of China
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Meng H, Wang TD, Zhuo LY, Hao JW, Sui LY, Yang W, Zang LL, Cui JJ, Wang JN, Yin XP. Quantitative radiomics analysis of imaging features in adults and children Mycoplasma pneumonia. Front Med (Lausanne) 2024; 11:1409477. [PMID: 38831994 PMCID: PMC11146305 DOI: 10.3389/fmed.2024.1409477] [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: 03/30/2024] [Accepted: 04/30/2024] [Indexed: 06/05/2024] Open
Abstract
Purpose This study aims to explore the value of clinical features, CT imaging signs, and radiomics features in differentiating between adults and children with Mycoplasma pneumonia and seeking quantitative radiomic representations of CT imaging signs. Materials and methods In a retrospective analysis of 981 cases of mycoplasmal pneumonia patients from November 2021 to December 2023, 590 internal data (adults:450, children: 140) randomly divided into a training set and a validation set with an 8:2 ratio and 391 external test data (adults:121; children:270) were included. Using univariate analysis, CT imaging signs and clinical features with significant differences (p < 0.05) were selected. After segmenting the lesion area on the CT image as the region of interest, 1,904 radiomic features were extracted. Then, Pearson correlation analysis (PCC) and the least absolute shrinkage and selection operator (LASSO) were used to select the radiomic features. Based on the selected features, multivariable logistic regression analysis was used to establish the clinical model, CT image model, radiomic model, and combined model. The predictive performance of each model was evaluated using ROC curves, AUC, sensitivity, specificity, accuracy, and precision. The AUC between each model was compared using the Delong test. Importantly, the radiomics features and quantitative and qualitative CT image features were analyzed using Pearson correlation analysis and analysis of variance, respectively. Results For the individual model, the radiomics model, which was built using 45 selected features, achieved the highest AUCs in the training set, validation set, and external test set, which were 0.995 (0.992, 0.998), 0.952 (0.921, 0.978), and 0.969 (0.953, 0.982), respectively. In all models, the combined model achieved the highest AUCs, which were 0.996 (0.993, 0.998), 0.972 (0.942, 0.995), and 0.986 (0.976, 0.993) in the training set, validation set, and test set, respectively. In addition, we selected 11 radiomics features and CT image features with a correlation coefficient r greater than 0.35. Conclusion The combined model has good diagnostic performance for differentiating between adults and children with mycoplasmal pneumonia, and different CT imaging signs are quantitatively represented by radiomics.
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Affiliation(s)
- Huan Meng
- Clinical Medicine School of Hebei University, Baoding, China
- Department of Radiology, Affiliated Hospital of Hebei University, Baoding, China
- Hebei Key Laboratory of Precise Imaging of Inflammation Related Tumors, Baoding, China
| | - Tian-Da Wang
- Clinical Medicine School of Hebei University, Baoding, China
- Department of Radiology, Affiliated Hospital of Hebei University, Baoding, China
- Hebei Key Laboratory of Precise Imaging of Inflammation Related Tumors, Baoding, China
| | - Li-Yong Zhuo
- Clinical Medicine School of Hebei University, Baoding, China
- Department of Radiology, Affiliated Hospital of Hebei University, Baoding, China
- Hebei Key Laboratory of Precise Imaging of Inflammation Related Tumors, Baoding, China
| | - Jia-Wei Hao
- Clinical Medicine School of Hebei University, Baoding, China
- Department of Radiology, Affiliated Hospital of Hebei University, Baoding, China
- Hebei Key Laboratory of Precise Imaging of Inflammation Related Tumors, Baoding, China
| | - Lian-yu Sui
- Clinical Medicine School of Hebei University, Baoding, China
- Department of Radiology, Affiliated Hospital of Hebei University, Baoding, China
- Hebei Key Laboratory of Precise Imaging of Inflammation Related Tumors, Baoding, China
| | - Wei Yang
- Department of Radiology, Baoding First Central Hospital, Baoding, China
| | - Li-Li Zang
- Department of Radiology, Baoding Children's Hospital, Baoding, China
| | - Jing-Jing Cui
- Department of Research and Development, United Imaging Intelligence (Beijing) Co., Beijing, China
| | - Jia-Ning Wang
- Clinical Medicine School of Hebei University, Baoding, China
- Department of Radiology, Affiliated Hospital of Hebei University, Baoding, China
- Hebei Key Laboratory of Precise Imaging of Inflammation Related Tumors, Baoding, China
| | - Xiao-Ping Yin
- Clinical Medicine School of Hebei University, Baoding, China
- Department of Radiology, Affiliated Hospital of Hebei University, Baoding, China
- Hebei Key Laboratory of Precise Imaging of Inflammation Related Tumors, Baoding, China
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17
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Ye J, Huang Y, Chu C, Li J, Liu G, Li W, Gao C. Association Between Artificial Intelligence Based Chest Computed Tomography and Clinical/Laboratory Characteristics with Severity and Mortality in COVID-19 Hospitalized Patients. J Inflamm Res 2024; 17:2977-2989. [PMID: 38764494 PMCID: PMC11102184 DOI: 10.2147/jir.s456440] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2023] [Accepted: 04/23/2024] [Indexed: 05/21/2024] Open
Abstract
Background Some patients with COVID-19 rapidly develop respiratory failure or mortality, underscoring the necessity for early identification of those prone to severe illness. Numerous studies focus on clinical and lab traits, but only few attend to chest computed tomography. The current study seeks to numerically quantify pulmonary lesions using early-phase CT scans calculated through artificial intelligence algorithms in conjunction with clinical and laboratory helps clinicians to early identify the development of severe illness and death in a group of COVID-19 patients. Methods From December 15, 2022, to January 30, 2023, 191 confirmed COVID-19 patients admitted to Xinhua Hospital Affiliated with Shanghai Jiao Tong University School of Medicine were consecutively enrolled. All patients underwent chest CT scans and serum tests within 48 hours prior to admission. Variables significantly linked to critical illness or mortality in univariate analysis were subjected to multivariate logistic regression models post collinearity assessment. Adjusted odds ratio, 95% confidence intervals, sensitivity, specificity, Youden index, receiver-operator-characteristics (ROC) curves, and area under the curve (AUC) were computed for predicting severity and in-hospital mortality. Results Multivariate logistic analysis revealed that myoglobin (OR = 1.003, 95% CI 1.001-1.005), APACHE II score (OR = 1.387, 95% CI 1.216-1.583), and the infected CT region percentage (OR = 113.897, 95% CI 4.939-2626.496) independently correlated with in-hospital COVID-19 mortality. Prealbumin stood as an independent safeguarding factor (OR = 0.965, 95% CI 0.947-0.984). Neutrophil counts (OR = 1.529, 95% CI 1.131-2.068), urea nitrogen (OR = 1.587, 95% CI 1.222-2.062), SOFA score(OR = 3.333, 95% CI 1.476-7.522), qSOFA score(OR = 15.197, 95% CI 3.281-70.384), PSI score(OR = 1.053, 95% CI 1.018-1.090), and the infected CT region percentage (OR = 548.221, 95% CI 2.615-114,953.586) independently linked to COVID-19 patient severity.
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Affiliation(s)
- Jiawei Ye
- Department of Emergency Medicine, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200092, People’s Republic of China
| | - Yingying Huang
- Dementia Research Centre, Faculty of Medicine, Health and Human Sciences, Macquarie UniversitySydney, Australia
| | - Caiting Chu
- Department of Radiology, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200092, People’s Republic of China
| | - Juan Li
- Department of Emergency Medicine, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200092, People’s Republic of China
| | - Guoxiang Liu
- Department of Emergency Medicine, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200092, People’s Republic of China
| | - Wenjie Li
- Department of Emergency Medicine, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200092, People’s Republic of China
| | - Chengjin Gao
- Department of Emergency Medicine, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200092, People’s Republic of China
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Chen L, Li M, Wu Z, Liu S, Huang Y. A nomogram to predict severe COVID-19 patients with increased pulmonary lesions in early days. Front Med (Lausanne) 2024; 11:1343661. [PMID: 38737763 PMCID: PMC11082326 DOI: 10.3389/fmed.2024.1343661] [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: 11/24/2023] [Accepted: 03/25/2024] [Indexed: 05/14/2024] Open
Abstract
Objectives This study aimed to predict severe coronavirus disease 2019 (COVID-19) progression in patients with increased pneumonia lesions in the early days. A simplified nomogram was developed utilizing artificial intelligence (AI)-based quantified computed tomography (CT). Methods From 17 December 2019 to 20 February 2020, a total of 246 patients were confirmed COVID-19 infected in Jingzhou Central Hospital, Hubei Province, China. Of these patients, 93 were mildly ill and had follow-up examinations in 7 days, and 61 of them had enlarged lesions on CT scans. We collected the neutrophil-to-lymphocyte ratio (NLR) and three quantitative CT features from two examinations within 7 days. The three quantitative CT features of pneumonia lesions, including ground-glass opacity volume (GV), semi-consolidation volume (SV), and consolidation volume (CV), were automatically calculated using AI. Additionally, the variation volumes of the lesions were also computed. Finally, a nomogram was developed using a multivariable logistic regression model. To simplify the model, we classified all the lesion volumes based on quartiles and curve fitting results. Results Among the 93 patients, 61 patients showed enlarged lesions on CT within 7 days, of whom 19 (31.1%) developed any severe illness. The multivariable logistic regression model included age, NLR on the second time, an increase in lesion volume, and changes in SV and CV in 7 days. The personalized prediction nomogram demonstrated strong discrimination in the sample, with an area under curve (AUC) and the receiver operating characteristic curve (ROC) of 0.961 and a 95% confidence interval (CI) of 0.917-1.000. Decision curve analysis illustrated that a nomogram based on quantitative AI was clinically useful. Conclusion The integration of CT quantitative changes, NLR, and age in this model exhibits promising performance in predicting the progression to severe illness in COVID-19 patients with early-stage pneumonia lesions. This comprehensive approach holds the potential to assist clinical decision-making.
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Affiliation(s)
- Lina Chen
- Department of Radiology, Jingzhou Hospital Affiliated to Yangtze University, Jingzhou, Hubei Province, China
| | - Min Li
- Department of Radiology, Jingzhou Hospital of Traditional Chinese Medicine, Jingzhou, Hubei Province, China
| | - Zhenghong Wu
- Department of Radiology, Jingzhou Hospital Affiliated to Yangtze University, Jingzhou, Hubei Province, China
| | - Sibin Liu
- Department of Radiology, Jingzhou Hospital Affiliated to Yangtze University, Jingzhou, Hubei Province, China
| | - Yuanyi Huang
- Department of Radiology, Jingzhou Hospital Affiliated to Yangtze University, Jingzhou, Hubei Province, China
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Xie K, Cui C, Li X, Yuan Y, Wang Z, Zeng L. MRI-Based Clinical-Imaging-Radiomics Nomogram Model for Discriminating Between Benign and Malignant Solid Pulmonary Nodules or Masses. Acad Radiol 2024:S1076-6332(24)00207-1. [PMID: 38644089 DOI: 10.1016/j.acra.2024.03.042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2024] [Revised: 03/29/2024] [Accepted: 03/30/2024] [Indexed: 04/23/2024]
Abstract
RATIONALE AND OBJECTIVES Pulmonary nodules or masses are highly prevalent worldwide, and differential diagnosis of benign and malignant lesions remains difficult. Magnetic resonance imaging (MRI) can provide functional and metabolic information of pulmonary lesions. This study aimed to establish a nomogram model based on clinical features, imaging features, and multi-sequence MRI radiomics to identify benign and malignant solid pulmonary nodules or masses. MATERIALS AND METHODS A total of 145 eligible patients (76 male; mean age, 58.4 years ± 13.7 [SD]) with solid pulmonary nodules or masses were retrospectively analyzed. The patients were randomized into two groups (training cohort, n = 102; validation cohort, n = 43). The nomogram was used for predicting malignant pulmonary lesions. The diagnostic performance of different models was evaluated by receiver operating characteristic (ROC) curve analysis. RESULTS Of these patients, 95 patients were diagnosed with benign lesions and 50 with malignant lesions. Multivariate analysis showed that age, DWI value, LSR value, and ADC value were independent predictors of malignant lesions. Among the radiomics models, the multi-sequence MRI-based model (T1WI+T2WI+ADC) achieved the best diagnosis performance with AUCs of 0.858 (95%CI: 0.775, 0.919) and 0.774 (95%CI: 0.621, 0.887) for the training and validation cohorts, respectively. Combining multi-sequence radiomics, clinical and imaging features, the predictive efficacy of the clinical-imaging-radiomics model was significantly better than the clinical model, imaging model and radiomics model (all P < 0.05). CONCLUSION The MRI-based clinical-imaging-radiomics model is helpful to differentiate benign and malignant solid pulmonary nodules or masses, and may be useful for precision medicine of pulmonary diseases.
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Affiliation(s)
- Kexin Xie
- Department of Radiology, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, Jiangsu 210002, China
| | - Can Cui
- Department of Radiology, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, Jiangsu 210002, China
| | - Xiaoqing Li
- Department of Radiology, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, Jiangsu 210002, China
| | - Yongfeng Yuan
- Department of Radiology, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, Jiangsu 210002, China
| | - Zhongqiu Wang
- Department of Radiology, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, Jiangsu 210002, China
| | - Liang Zeng
- Department of Radiology, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, Jiangsu 210002, China.
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Malmberg MA, Odéen H, Hofstetter LW, Hadley JR, Parker DL. Validation of single reference variable flip angle (SR-VFA) dynamic T 1 mapping with T 2 * correction using a novel rotating phantom. Magn Reson Med 2024; 91:1419-1433. [PMID: 38115639 PMCID: PMC10872756 DOI: 10.1002/mrm.29944] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Revised: 10/12/2023] [Accepted: 11/09/2023] [Indexed: 12/21/2023]
Abstract
PURPOSE To validate single reference variable flip angle (SR-VFA) dynamic T1 mapping with and without T2 * correction against inversion recovery (IR) T1 measurements. METHODS A custom cylindrical phantom with three concentric compartments was filled with variably doped agar to produce a smooth spatial gradient of the T1 relaxation rate as a function of angle across each compartment. IR T1 , VFA T1 , and B1 + measurements were made on the phantom before rotation, and multi-echo stack-of-radial dynamic images were acquired during rotation via an MRI-compatible motor. B1 + -corrected SR-VFA and SR-VFA-T2 * T1 maps were computed from the sliding window reconstructed images and compared against rotationally registered IR and VFA T1 maps to determine the percentage error. RESULTS Both VFA and SR-VFA-T2 * T1 maps fell within 10% of IR T1 measurements for a low rotational speed, with a mean accuracy of 2.3% ± 2.6% and 2.8% ± 2.6%, respectively. Increasing rotational speed was found to decrease the accuracy due to increasing temporal smoothing over ranges where the T1 change had a nonconstant slope. SR-VFA T1 mapping was found to have similar accuracy as the SR-VFA-T2 * and VFA methods at low TEs (˜<2 ms), whereas accuracy degraded strongly with later TEs. T2 * correction of the SR-VFA T1 maps was found to consistently improve accuracy and precision, especially at later TEs. CONCLUSION SR-VFA-T2 * dynamic T1 mapping was found to be accurate against reference IR T1 measurements within 10% in an agar phantom. Further validation is needed in mixed fat-water phantoms and in vivo.
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Affiliation(s)
- Michael A. Malmberg
- Department of Bioengineering, University of Utah, Salt Lake City, UT, USA
- Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, UT, USA
| | - Henrik Odéen
- Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, UT, USA
| | | | - J. Rock Hadley
- Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, UT, USA
| | - Dennis L. Parker
- Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, UT, USA
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Mathkor DM, Mathkor N, Bassfar Z, Bantun F, Slama P, Ahmad F, Haque S. Multirole of the internet of medical things (IoMT) in biomedical systems for managing smart healthcare systems: An overview of current and future innovative trends. J Infect Public Health 2024; 17:559-572. [PMID: 38367570 DOI: 10.1016/j.jiph.2024.01.013] [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: 04/06/2023] [Revised: 01/16/2024] [Accepted: 01/18/2024] [Indexed: 02/19/2024] Open
Abstract
Internet of Medical Things (IoMT) is an emerging subset of Internet of Things (IoT), often called as IoT in healthcare, refers to medical devices and applications with internet connectivity, is exponentially gaining researchers' attention due to its wide-ranging applicability in biomedical systems for Smart Healthcare systems. IoMT facilitates remote health biomedical system and plays a crucial role within the healthcare industry to enhance precision, reliability, consistency and productivity of electronic devices used for various healthcare purposes. It comprises a conceptualized architecture for providing information retrieval strategies to extract the data from patient records using sensors for biomedical analysis and diagnostics against manifold diseases to provide cost-effective medical solutions, quick hospital treatments, and personalized healthcare. This article provides a comprehensive overview of IoMT with special emphasis on its current and future trends used in biomedical systems, such as deep learning, machine learning, blockchains, artificial intelligence, radio frequency identification, and industry 5.0.
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Affiliation(s)
- Darin Mansor Mathkor
- Research and Scientific Studies Unit, Department of Nursing, College of Nursing and Health Sciences, Jazan University, Jazan 45142, Saudi Arabia
| | - Noof Mathkor
- Department of Pathology, Ministry of National Guard Health Affairs (MNGHA), Riyadh, Saudi Arabia
| | - Zaid Bassfar
- Department of Information Technology, Faculty of Computers and Information Technology, University of Tabuk, Tabuk, Saudi Arabia
| | - Farkad Bantun
- Department of Microbiology, Faculty of Medicine, Umm Al-Qura University, Makkah, Saudi Arabia
| | - Petr Slama
- Laboratory of Animal Immunology and Biotechnology, Department of Animal Morphology, Physiology and Genetics, Mendel University in Brno, 61300 Brno, Czech Republic
| | - Faraz Ahmad
- Department of Biotechnology, School of Bio Sciences and Technology, Vellore Institute of Technology, Vellore 632014, India
| | - Shafiul Haque
- Research and Scientific Studies Unit, Department of Nursing, College of Nursing and Health Sciences, Jazan University, Jazan 45142, Saudi Arabia; Gilbert and Rose-Marie Chagoury School of Medicine, Lebanese American University, Beirut, Lebanon; Centre of Medical and Bio-Allied Health Sciences Research, Ajman University, Ajman, United Arab Emirates.
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22
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Cheng FC, Li YH, Wei YF, Chen CJ, Chen MH, Chiang CP. The usage of dental cone-beam computed tomography during the COVID-19 pandemic (from 2020 to 2022): A survey of a regional hospital in the northern Taiwan. J Dent Sci 2024; 19:795-803. [PMID: 38618131 PMCID: PMC11010694 DOI: 10.1016/j.jds.2023.10.032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Indexed: 04/16/2024] Open
Abstract
Background/purpose In Taiwan, cone-beam computed tomography (CBCT) has already widely used in dentistry. This study explored preliminarily the usage of dental CBCT during the COVID-19 pandemic (from 2020 to 2022) through a survey of a regional hospital in the northern Taiwan. Materials and methods This study used purposeful sampling to select a regional hospital in the northern Taiwan to survey its usage of dental CBCT during the COVID-19 pandemic. Results In the surveyed hospital, the number of patients' visits for the usage of dental CBCT increased from 355 in 2020 to 449 in 2021 and further to 488 in 2022 with a growth rate of 37.46 %, while the growth rates compared to the previous year were 26.48 % in 2021 and 8.69 % in 2022, respectively. There were a total of 1292 patients' visits for the dental CBCT. The ages of the 1292 patients (573 males and 719 females) ranged from 4 to 89 years. The 50-59-year age group had the highest number of patients' visits (371, 28.72 %), followed in a descending order by the 60-69-year (293, 22.68 %) and 40-49-year (206, 15.94 %) age groups. The dental CBCT was used mainly for the assessment of dental implants, accounting for 1148 (78.85 %) of the total 1456 irradiations. Conclusion During the COVID-19 pandemic, the medical services for dental care and treatments in Taiwan are still maintained normally, and the dental CBCT is also used widely and popularly by the dental patients of all ages, various dental procedures, and various dental specialties.
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Affiliation(s)
- Feng-Chou Cheng
- Chia-Te Dental Clinic, New Taipei City, Taiwan
- School of Life Science, College of Science, National Taiwan Normal University, Taipei, Taiwan
- Science Education Center, National Taiwan Normal University, Taipei, Taiwan
| | - Yu-Hung Li
- Department of Radiology Technology, Lotung Poh-Ai Hospital, Yilan, Taiwan
| | - Yuh-Fen Wei
- Department of Medical Imaging, National Taiwan University Hospital Hsin-Chu Branch, Hsinchu County, Taiwan
| | - Chien-Jung Chen
- Department of Nuclear Medicine, National Taiwan University Hospital, College of Medicine, National Taiwan University, Taipei, Taiwan
| | - Mu-Hsiung Chen
- Department of Dentistry, National Taiwan University Hospital, College of Medicine, National Taiwan University, Taipei, Taiwan
| | - Chun-Pin Chiang
- Department of Dentistry, National Taiwan University Hospital, College of Medicine, National Taiwan University, Taipei, Taiwan
- Graduate Institute of Clinical Dentistry, School of Dentistry, National Taiwan University, Taipei, Taiwan
- Graduate Institute of Oral Biology, School of Dentistry, National Taiwan University, Taipei, Taiwan
- Department of Dentistry, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Hualien, Taiwan
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Crombé A, Lecomte JC, Seux M, Banaste N, Gorincour G. Using the Textual Content of Radiological Reports to Detect Emerging Diseases: A Proof-of-Concept Study of COVID-19. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024; 37:620-632. [PMID: 38343242 PMCID: PMC11031522 DOI: 10.1007/s10278-023-00949-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Revised: 10/02/2023] [Accepted: 10/04/2023] [Indexed: 04/20/2024]
Abstract
Changes in the content of radiological reports at population level could detect emerging diseases. Herein, we developed a method to quantify similarities in consecutive temporal groupings of radiological reports using natural language processing, and we investigated whether appearance of dissimilarities between consecutive periods correlated with the beginning of the COVID-19 pandemic in France. CT reports from 67,368 consecutive adults across 62 emergency departments throughout France between October 2019 and March 2020 were collected. Reports were vectorized using time frequency-inverse document frequency (TF-IDF) analysis on one-grams. For each successive 2-week period, we performed unsupervised clustering of the reports based on TF-IDF values and partition-around-medoids. Next, we assessed the similarities between this clustering and a clustering from two weeks before according to the average adjusted Rand index (AARI). Statistical analyses included (1) cross-correlation functions (CCFs) with the number of positive SARS-CoV-2 tests and advanced sanitary index for flu syndromes (ASI-flu, from open-source dataset), and (2) linear regressions of time series at different lags to understand the variations of AARI over time. Overall, 13,235 chest CT reports were analyzed. AARI was correlated with ASI-flu at lag = + 1, + 5, and + 6 weeks (P = 0.0454, 0.0121, and 0.0042, respectively) and with SARS-CoV-2 positive tests at lag = - 1 and 0 week (P = 0.0057 and 0.0001, respectively). In the best fit, AARI correlated with the ASI-flu with a lag of 2 weeks (P = 0.0026), SARS-CoV-2-positive tests in the same week (P < 0.0001) and their interaction (P < 0.0001) (adjusted R2 = 0.921). Thus, our method enables the automatic monitoring of changes in radiological reports and could help capturing disease emergence.
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Affiliation(s)
- Amandine Crombé
- IMADIS, Lyon, France.
- SARCOTARGET Team, University of Bordeaux, Inserm, UMR1312, BRIC, BoRdeaux Institute of Oncology, 146 Rue Léo Saignat, Bordeaux, F-33076, France.
- Department of Radiology, Pellegrin University Hospital, CHU Bordeaux, Place Amélie Raba-Léon, Bordeaux, F-33076, France.
| | - Jean-Christophe Lecomte
- IMADIS, Lyon, France
- Centre Aquitain d'Imagerie médicale, Mérignac, France
- Centre Hospitalier de Saintes, Saintes, France
- Clinique Mutualiste Bordeaux Pessac, Pessac, France
| | | | - Nathan Banaste
- IMADIS, Lyon, France
- Clinique Convert, Ramsay, Bourg en Bresse, France
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Maleki Varnosfaderani S, Forouzanfar M. The Role of AI in Hospitals and Clinics: Transforming Healthcare in the 21st Century. Bioengineering (Basel) 2024; 11:337. [PMID: 38671759 PMCID: PMC11047988 DOI: 10.3390/bioengineering11040337] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2024] [Revised: 03/25/2024] [Accepted: 03/26/2024] [Indexed: 04/28/2024] Open
Abstract
As healthcare systems around the world face challenges such as escalating costs, limited access, and growing demand for personalized care, artificial intelligence (AI) is emerging as a key force for transformation. This review is motivated by the urgent need to harness AI's potential to mitigate these issues and aims to critically assess AI's integration in different healthcare domains. We explore how AI empowers clinical decision-making, optimizes hospital operation and management, refines medical image analysis, and revolutionizes patient care and monitoring through AI-powered wearables. Through several case studies, we review how AI has transformed specific healthcare domains and discuss the remaining challenges and possible solutions. Additionally, we will discuss methodologies for assessing AI healthcare solutions, ethical challenges of AI deployment, and the importance of data privacy and bias mitigation for responsible technology use. By presenting a critical assessment of AI's transformative potential, this review equips researchers with a deeper understanding of AI's current and future impact on healthcare. It encourages an interdisciplinary dialogue between researchers, clinicians, and technologists to navigate the complexities of AI implementation, fostering the development of AI-driven solutions that prioritize ethical standards, equity, and a patient-centered approach.
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Affiliation(s)
| | - Mohamad Forouzanfar
- Département de Génie des Systèmes, École de Technologie Supérieure (ÉTS), Université du Québec, Montréal, QC H3C 1K3, Canada
- Centre de Recherche de L’institut Universitaire de Gériatrie de Montréal (CRIUGM), Montréal, QC H3W 1W5, Canada
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Mjokane N, Akintemi EO, Sabiu S, Gcilitshana OMN, Albertyn J, Pohl CH, Sebolai OM. Aspergillus fumigatus secretes a protease(s) that displays in silico binding affinity towards the SARS-CoV-2 spike protein and mediates SARS-CoV-2 pseudovirion entry into HEK-293T cells. Virol J 2024; 21:58. [PMID: 38448991 PMCID: PMC10919004 DOI: 10.1186/s12985-024-02331-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Accepted: 02/27/2024] [Indexed: 03/08/2024] Open
Abstract
BACKGROUND The novel coronavirus disease of 2019 (COVID-19) is an infectious disease caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). Data from the COVID-19 clinical control case studies showed that this disease could also manifest in patients with underlying microbial infections such as aspergillosis. The current study aimed to determine if the Aspergillus (A.) fumigatus culture media (i.e., supernatant) possessed protease activity that was sufficient to activate the SARS-CoV-2 spike protein. METHODS The supernatant was first analysed for protease activity. Thereafter, it was assessed to determine if it possessed proteolytic activity to cleave a fluorogenic mimetic peptide of the SARS-CoV-2 spike protein that contained the S1/S2 site and a full-length spike protein contained in a SARS-CoV-2 pseudovirion. To complement this, a computer-based tool, HADDOCK, was used to predict if A. fumigatus alkaline protease 1 could bind to the SARS-CoV-2 spike protein. RESULTS We show that the supernatant possessed proteolytic activity, and analyses of the molecular docking parameters revealed that A. fumigatus alkaline protease 1 could bind to the spike protein. To confirm the in silico data, it was imperative to provide experimental evidence for enzymatic activity. Here, it was noted that the A. fumigatus supernatant cleaved the mimetic peptide as well as transduced the HEK-293T cells with SARS-CoV-2 pseudovirions. CONCLUSION These results suggest that A. fumigatus secretes a protease(s) that activates the SARS-CoV-2 spike protein. Importantly, should these two infectious agents co-occur, there is the potential for A. fumigatus to activate the SARS-CoV-2 spike protein, thus aggravating COVID-19 development.
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Affiliation(s)
- Nozethu Mjokane
- Department of Microbiology and Biochemistry, University of the Free State, 205 Nelson Mandela Drive, Park West, 9301, Bloemfontein, South Africa
| | - Eric O Akintemi
- Department of Microbiology and Biochemistry, University of the Free State, 205 Nelson Mandela Drive, Park West, 9301, Bloemfontein, South Africa
| | - Saheed Sabiu
- Department of Biotechnology and Food Science, Faculty of Applied Science, Durban University of Technology, 4000, Durban, P.O. Box 1334, South Africa
| | - Onele M N Gcilitshana
- Department of Microbiology and Biochemistry, University of the Free State, 205 Nelson Mandela Drive, Park West, 9301, Bloemfontein, South Africa
| | - Jacobus Albertyn
- Department of Microbiology and Biochemistry, University of the Free State, 205 Nelson Mandela Drive, Park West, 9301, Bloemfontein, South Africa
| | - Carolina H Pohl
- Department of Microbiology and Biochemistry, University of the Free State, 205 Nelson Mandela Drive, Park West, 9301, Bloemfontein, South Africa
| | - Olihile M Sebolai
- Department of Microbiology and Biochemistry, University of the Free State, 205 Nelson Mandela Drive, Park West, 9301, Bloemfontein, South Africa.
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Gu Y, Pan Y, Fang Z, Ma L, Zhu Y, Androjna C, Zhong K, Yu X, Shen D. Deep learning-assisted preclinical MR fingerprinting for sub-millimeter T 1 and T 2 mapping of entire macaque brain. Magn Reson Med 2024; 91:1149-1164. [PMID: 37929695 DOI: 10.1002/mrm.29905] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Revised: 09/10/2023] [Accepted: 10/10/2023] [Indexed: 11/07/2023]
Abstract
PURPOSE Preclinical MR fingerprinting (MRF) suffers from long acquisition time for organ-level coverage due to demanding image resolution and limited undersampling capacity. This study aims to develop a deep learning-assisted fast MRF framework for sub-millimeter T1 and T2 mapping of entire macaque brain on a preclinical 9.4 T MR system. METHODS Three dimensional MRF images were reconstructed by singular value decomposition (SVD) compressed reconstruction. T1 and T2 mapping for each axial slice exploited a self-attention assisted residual U-Net to suppress aliasing-induced quantification errors, and the transmit-field (B1 + ) measurements for robustness against B1 + inhomogeneity. Supervised network training used MRF images simulated via virtual parametric maps and a desired undersampling scheme. This strategy bypassed the difficulties of acquiring fully sampled preclinical MRF data to guide network training. The proposed fast MRF framework was tested on experimental data acquired from ex vivo and in vivo macaque brains. RESULTS The trained network showed reasonable adaptability to experimental MRF images, enabling robust delineation of various T1 and T2 distributions in the brain tissues. Further, the proposed MRF framework outperformed several existing fast MRF methods in handling the aliasing artifacts and capturing detailed cerebral structures in the mapping results. Parametric mapping of entire macaque brain at nominal resolution of 0.35× $$ \times $$ 0.35× $$ \times $$ 1 mm3 can be realized via a 20-min 3D MRF scan, which was sixfold faster than the baseline protocol. CONCLUSION Introducing deep learning to MRF framework paves the way for efficient organ-level high-resolution quantitative MRI in preclinical applications.
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Affiliation(s)
- Yuning Gu
- School of Biomedical Engineering & State Key Laboratory of Advanced Medical Materials and Devices, ShanghaiTech University, Shanghai, China
| | - Yongsheng Pan
- School of Biomedical Engineering & State Key Laboratory of Advanced Medical Materials and Devices, ShanghaiTech University, Shanghai, China
| | - Zhenghan Fang
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, USA
| | - Lei Ma
- School of Biomedical Engineering & State Key Laboratory of Advanced Medical Materials and Devices, ShanghaiTech University, Shanghai, China
| | - Yuran Zhu
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA
| | - Charlie Androjna
- Cleveland Clinic Pre-Clinical Magnetic Resonance Imaging Center, Cleveland Clinic Foundation, Cleveland, Ohio, USA
| | - Kai Zhong
- High Magnetic Field Laboratory, Chinese Academy of Sciences, Hefei, China
- Anhui Province Key Laboratory of High Field Magnetic Resonance Imaging, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, China
- Biomedical Engineering Department, Peking University, Beijing, China
| | - Xin Yu
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA
- Department of Radiology, Case Western Reserve University, Cleveland, Ohio, USA
| | - Dinggang Shen
- School of Biomedical Engineering & State Key Laboratory of Advanced Medical Materials and Devices, ShanghaiTech University, Shanghai, China
- Shanghai United Imaging Intelligence, Shanghai, China
- Shanghai Clinical Research and Trial Center, ShanghaiTech University, Shanghai, China
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Mertiri L, Freiling JT, Desai NK, Kralik SF, Huisman TAGM. Pediatric and adult meningeal, parenchymal, and spinal tuberculosis: A neuroimaging review. J Neuroimaging 2024; 34:179-194. [PMID: 38073450 DOI: 10.1111/jon.13177] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Revised: 11/24/2023] [Accepted: 11/28/2023] [Indexed: 03/12/2024] Open
Abstract
Neurotuberculosis is defined as a tuberculous infection of the meninges, brain parenchyma, vessels, cranial and spinal nerves, spinal cord, skull, and spine that can occur either in a localized or in a diffuse form. It is a heterogeneous disease characterized by many imaging appearances and it has been defined as "the great mimicker" due to similarities with many other conditions. The diagnosis of central nervous system (CNS) tuberculosis (TB) is based on clinical presentation, neuroimaging findings, laboratory and microbiological findings, and comprehensive evaluation of the response to anti-TB drug treatment. However, the absence of specific symptoms, the wide spectrum of neurological manifestations, the myriad of imaging findings, possible inconclusive laboratory results, and the paradoxical reaction to treatment make the diagnosis often challenging and difficult, potentially delaying adequate treatment with possible devastating short-term and long-term neurologic sequelae. Familiarity with the imaging characteristics helps in accurate diagnosis and may prevent or limit significantly morbidity and mortality. The goal of this review is to provide a comprehensive up-to-date overview of the conventional and advanced imaging features of CNS TB for radiologists, neuroradiologists, and pediatric radiologists. We discuss the most typical neurotuberculosis imaging findings and their differential diagnosis in children and adults with the goal to provide a global overview of this entity.
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Affiliation(s)
- Livja Mertiri
- Edward B. Singleton Department of Radiology, Texas Children's Hospital and Baylor College of Medicine, Houston, Texas, USA
| | - John T Freiling
- Edward B. Singleton Department of Radiology, Texas Children's Hospital and Baylor College of Medicine, Houston, Texas, USA
| | - Nilesh K Desai
- Edward B. Singleton Department of Radiology, Texas Children's Hospital and Baylor College of Medicine, Houston, Texas, USA
| | - Stephen F Kralik
- Edward B. Singleton Department of Radiology, Texas Children's Hospital and Baylor College of Medicine, Houston, Texas, USA
| | - Thierry A G M Huisman
- Edward B. Singleton Department of Radiology, Texas Children's Hospital and Baylor College of Medicine, Houston, Texas, USA
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Li Y, Deng W, Zhou Y, Luo Y, Wu Y, Wen J, Cheng L, Liang X, Wu T, Wang F, Huang Z, Tan C, Liu Y. A nomogram based on clinical factors and CT radiomics for predicting anti-MDA5+ DM complicated by RP-ILD. Rheumatology (Oxford) 2024; 63:809-816. [PMID: 37267146 DOI: 10.1093/rheumatology/kead263] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Revised: 03/30/2023] [Accepted: 05/08/2023] [Indexed: 06/04/2023] Open
Abstract
OBJECTIVES Anti-melanoma differentiation-associated gene 5 antibody-positive (anti-MDA5+) DM complicated by rapidly progressive interstitial lung disease (RP-ILD) has a high incidence and poor prognosis. The objective of this study was to establish a model for the prediction and early diagnosis of anti-MDA5+ DM-associated RP-ILD based on clinical manifestations and imaging features. METHODS A total of 103 patients with anti-MDA5+ DM were included. The patients were randomly split into training and testing sets of 72 and 31 patients, respectively. After image analysis, we collected clinical, imaging and radiomics features from each patient. Feature selection was performed first with the minimum redundancy and maximum relevance algorithm and then with the best subset selection method. The final remaining features comprised the radscore. A clinical model and imaging model were then constructed with the selected independent risk factors for the prediction of non-RP-ILD and RP-ILD. We also combined these models in different ways and compared their predictive abilities. A nomogram was also established. The predictive performances of the models were assessed based on receiver operating characteristics curves, calibration curves, discriminability and clinical utility. RESULTS The analyses showed that two clinical factors, dyspnoea (P = 0.000) and duration of illness in months (P = 0.001), and three radiomics features (P = 0.001, 0.044 and 0.008, separately) were independent predictors of non-RP-ILD and RP-ILD. However, no imaging features were significantly different between the two groups. The radiomics model built with the three radiomics features performed worse than the clinical model and showed areas under the curve (AUCs) of 0.805 and 0.754 in the training and test sets, respectively. The clinical model demonstrated a good predictive ability for RP-ILD in MDA5+ DM patients, with an AUC, sensitivity, specificity and accuracy of 0.954, 0.931, 0.837 and 0.847 in the training set and 0.890, 0.875, 0.800 and 0.774 in the testing set, respectively. The combination model built with clinical and radiomics features performed slightly better than the clinical model, with an AUC, sensitivity, specificity and accuracy of 0.994, 0.966, 0.977 and 0.931 in the training set and 0.890, 0.812, 1.000 and 0.839 in the testing set, respectively. The calibration curve and decision curve analyses showed satisfactory consistency and clinical utility of the nomogram. CONCLUSION Our results suggest that the combination model built with clinical and radiomics features could reliably predict the occurrence of RP-ILD in MDA5+ DM patients.
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Affiliation(s)
- Yanhong Li
- Department of Rheumatology and Immunology, West China Hospital, Sichuan University, Chengdu, China
- Rare Diseases Center, West China Hospital, Sichuan University, Chengdu, China
- Institute of Immunology and Inflammation, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Chengdu, China
| | - Wen Deng
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Yu Zhou
- Department of Respiratory and Critical Care Medicine, Chengdu First People's Hospital, Chengdu, China
| | - Yubin Luo
- Department of Rheumatology and Immunology, West China Hospital, Sichuan University, Chengdu, China
- Rare Diseases Center, West China Hospital, Sichuan University, Chengdu, China
- Institute of Immunology and Inflammation, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Chengdu, China
| | - Yinlan Wu
- Department of Rheumatology and Immunology, West China Hospital, Sichuan University, Chengdu, China
- Rare Diseases Center, West China Hospital, Sichuan University, Chengdu, China
- Institute of Immunology and Inflammation, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Chengdu, China
| | - Ji Wen
- Department of Rheumatology and Immunology, West China Hospital, Sichuan University, Chengdu, China
- Rare Diseases Center, West China Hospital, Sichuan University, Chengdu, China
- Institute of Immunology and Inflammation, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Chengdu, China
| | - Lu Cheng
- Department of Rheumatology and Immunology, West China Hospital, Sichuan University, Chengdu, China
- Rare Diseases Center, West China Hospital, Sichuan University, Chengdu, China
- Institute of Immunology and Inflammation, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Chengdu, China
| | - Xiuping Liang
- Department of Rheumatology and Immunology, West China Hospital, Sichuan University, Chengdu, China
- Rare Diseases Center, West China Hospital, Sichuan University, Chengdu, China
- Institute of Immunology and Inflammation, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Chengdu, China
| | - Tong Wu
- Department of Rheumatology and Immunology, West China Hospital, Sichuan University, Chengdu, China
- Rare Diseases Center, West China Hospital, Sichuan University, Chengdu, China
- Institute of Immunology and Inflammation, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Chengdu, China
| | - Fang Wang
- Department of Research and Development, Shanghai United Imaging Intelligence, Shanghai, China
| | - Zixing Huang
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Chunyu Tan
- Department of Rheumatology and Immunology, West China Hospital, Sichuan University, Chengdu, China
- Rare Diseases Center, West China Hospital, Sichuan University, Chengdu, China
- Institute of Immunology and Inflammation, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Chengdu, China
| | - Yi Liu
- Department of Rheumatology and Immunology, West China Hospital, Sichuan University, Chengdu, China
- Rare Diseases Center, West China Hospital, Sichuan University, Chengdu, China
- Institute of Immunology and Inflammation, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Chengdu, China
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Teng L, Wang B, Xu X, Zhang J, Mei L, Feng Q, Shen D. Beam-wise dose composition learning for head and neck cancer dose prediction in radiotherapy. Med Image Anal 2024; 92:103045. [PMID: 38071865 DOI: 10.1016/j.media.2023.103045] [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: 10/29/2022] [Revised: 10/12/2023] [Accepted: 11/27/2023] [Indexed: 01/12/2024]
Abstract
Automatic and accurate dose distribution prediction plays an important role in radiotherapy plan. Although previous methods can provide promising performance, most methods did not consider beam-shaped radiation of treatment delivery in clinical practice. This leads to inaccurate prediction, especially on beam paths. To solve this problem, we propose a beam-wise dose composition learning (BDCL) method for dose prediction in the context of head and neck (H&N) radiotherapy plan. Specifically, a global dose network is first utilized to predict coarse dose values in the whole-image space. Then, we propose to generate individual beam masks to decompose the coarse dose distribution into multiple field doses, called beam voters, which are further refined by a subsequent beam dose network and reassembled to form the final dose distribution. In particular, we design an overlap consistency module to keep the similarity of high-level features in overlapping regions between different beam voters. To make the predicted dose distribution more consistent with the real radiotherapy plan, we also propose a dose-volume histogram (DVH) calibration process to facilitate feature learning in some clinically concerned regions. We further apply an edge enhancement procedure to enhance the learning of the extracted feature from the dose falloff regions. Experimental results on a public H&N cancer dataset from the AAPM OpenKBP challenge show that our method achieves superior performance over other state-of-the-art approaches by significant margins. Source code is released at https://github.com/TL9792/BDCLDosePrediction.
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Affiliation(s)
- Lin Teng
- School of Biomedical Engineering, ShanghaiTech University, Shanghai 201210, China; School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China
| | - Bin Wang
- School of Biomedical Engineering, ShanghaiTech University, Shanghai 201210, China
| | - Xuanang Xu
- Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180, USA
| | - Jiadong Zhang
- School of Biomedical Engineering, ShanghaiTech University, Shanghai 201210, China
| | - Lanzhuju Mei
- School of Biomedical Engineering, ShanghaiTech University, Shanghai 201210, China
| | - Qianjin Feng
- School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China
| | - Dinggang Shen
- School of Biomedical Engineering, ShanghaiTech University, Shanghai 201210, China; Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai 200230, China; Shanghai Clinical Research and Trial Center, Shanghai 201210, China.
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Ghaddaripouri K, Ghaddaripouri M, Mousavi AS, Mousavi Baigi SF, Rezaei Sarsari M, Dahmardeh Kemmak F, Mazaheri Habibi MR. The effect of machine learning algorithms in the prediction, and diagnosis of meningitis: A systematic review. Health Sci Rep 2024; 7:e1893. [PMID: 38357491 PMCID: PMC10865276 DOI: 10.1002/hsr2.1893] [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: 09/30/2023] [Revised: 01/23/2024] [Accepted: 01/24/2024] [Indexed: 02/16/2024] Open
Abstract
Background and Aims This systematic review aimed to evaluating the effectiveness of machine learning (ML) algorithms for the prediction and diagnosis of meningitis. Methods On November 12, 2022, a systematic review was carried out using a keyword search in the reliable scientific databases PubMed, EMBASE, Scopus, and Web of Science. The recommendations of Preferred Reporting for Systematic Reviews and Meta-Analyses (PRISMA) were adhered to. Studies conducted in English that employed ML to predict and identify meningitis were deemed to match the inclusion criteria. The eligibility requirements were used to independently review the titles and abstracts. The whole text was then obtained and independently reviewed in accordance with the eligibility requirements. Results After all the research matched the inclusion criteria, a total of 16 studies were added to the systematic review. Studies on the application of ML algorithms in the three categories of disease diagnosis ability (8.16) and disease prediction ability (8.16) (including cases related to identifying patients (50%), risk of death in patients (25%), the consequences of the disease in childhood (12.5%), and its etiology [12.5%]) were placed. Among the ML algorithms used in this study, logistic regression (LR) (4.16, 25%) and multiple logistic regression (MLR) (4.16, 25%) were the most used. All the included studies indicated improvements in the processes of diagnosis, prediction, and disease outbreak with the help of ML algorithms. Conclusion The results of the study showed that in all included studies, ML algorithms were an effective approach to facilitate diagnosis, predict consequences for risk classification, and improve resource utilization by predicting the volume of patients or services as well as discovering risk factors. The role of ML algorithms in improving disease diagnosis was more significant than disease prediction and prevalence. Meanwhile, the use of combined methods can optimize differential diagnoses and facilitate the decision-making process for healthcare providers.
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Affiliation(s)
- Kosar Ghaddaripouri
- Department of Health Information Management, School of Health Management and Information SciencesShiraz University of Medical SciencesShirazIran
| | - Maryam Ghaddaripouri
- Department of Laboratory Sciences, School of Paramedical and Rehabilitation SciencesMashhad University of Medical SciencesMashhadIran
| | | | - Seyyedeh Fatemeh Mousavi Baigi
- Mashhad University of Medical SciencesMashhadIran
- Student Research CommitteeMashhad University of Medical SciencesMashhadIran
| | | | - Fatemeh Dahmardeh Kemmak
- Mashhad University of Medical SciencesMashhadIran
- Student Research CommitteeMashhad University of Medical SciencesMashhadIran
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Santos HO, Delpino FM, Veloso OM, Freire JMR, Gomes ESN, Pereira CGM. Elevated neutrophil-lymphocyte ratio is associated with high rates of ICU mortality, length of stay, and invasive mechanical ventilation in critically ill patients with COVID-19 : NRL and severe COVID-19. Immunol Res 2024; 72:147-154. [PMID: 37768500 DOI: 10.1007/s12026-023-09424-x] [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: 12/01/2022] [Accepted: 09/12/2023] [Indexed: 09/29/2023]
Abstract
Neutrophil and lymphocyte ratio (NLR) has emerged as a complementary marker in intensive care. This study aimed to associate high NLR values with mortality as the primary outcome, and length of stay and need for invasive mechanical ventilation as secondary outcomes, in critically ill patients with COVID-19. A cross-sectional study encompassing 189 critically ill patients with COVID-19 was performed. The receiver operating characteristic curve was used to identify the best NLR cutoff value for ICU mortality (≥ 10.6). An NLR ≥ 10.6, compared with an NLR < 10.6, was associated with higher odds of ICU mortality (odds ratio [OR], 2.77; 95% confidence interval [CI], 1.24-6.18), ICU length of stay ≥ 14 days (OR, 3.56; 95% CI, 1.01-12.5), and need for invasive mechanical ventilation (OR, 5.39; 95% CI, 1.96-14.81) in the fully adjusted model (age, sex, kidney dysfunction, diabetes, obesity, hypertension, deep vein thrombosis, antibiotics, anticoagulants, antivirals, corticoids, neuromuscular blockers, and vasoactive drugs). In conclusion, elevated NLR is associated with high rates of mortality, length of stay, and need for invasive mechanical ventilation in critically ill patients with COVID-19.
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Affiliation(s)
- Heitor O Santos
- School of Medicine, Federal University of Uberlandia (UFU), Para Street, 1720, Umuarama. Block 2H, Uberlandia, 38400-902, MG, Brazil.
| | - Felipe M Delpino
- Postgraduate in Nursing, Federal University of Pelotas (UFPel), Pelotas, Rio Grande do Sul, Brazil
| | - Octavio M Veloso
- Department of Medicine, Federal University of Sergipe (UFS), Sergipe. Augusto Franco Avenue, 3500. Unit 134. Aracaju - Sergipe, Aracaju, Sergipe, Brazil
| | | | | | - Cristina G M Pereira
- Department of Medicine, Federal University of Sergipe (UFS), Sergipe. Augusto Franco Avenue, 3500. Unit 134. Aracaju - Sergipe, Aracaju, Sergipe, Brazil
- São Lucas Hospital - Rede D'OR (HSL), Aracaju, Sergipe, Brazil
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Moosavi AS, Mahboobi A, Arabzadeh F, Ramezani N, Moosavi HS, Mehrpoor G. Segmentation and classification of lungs CT-scan for detecting COVID-19 abnormalities by deep learning technique: U-Net model. J Family Med Prim Care 2024; 13:691-698. [PMID: 38605799 PMCID: PMC11006039 DOI: 10.4103/jfmpc.jfmpc_695_23] [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: 04/25/2023] [Revised: 07/12/2023] [Accepted: 09/22/2023] [Indexed: 04/13/2024] Open
Abstract
Background Artificial intelligence (AI) techniques have been ascertained useful in the analysis and description of infectious areas in radiological images promptly. Our aim in this study was to design a web-based application for detecting and labeling infected tissues on CT (computed tomography) lung images of patients based on the deep learning (DL) method as a type of AI. Materials and Methods The U-Net architecture, one of the DL networks, is used as a hybrid model with pre-trained densely connected convolutional network 121 (DenseNet121) architecture for the segmentation process. The proposed model was constructed on 1031 persons' CT-scan images from Ibn Sina Hospital of Iran in 2021 and some publicly available datasets. The network was trained using 6000 slices, validated on 1000 slices images, and tested against the 150 slices. Accuracy, sensitivity, specificity, and area under the receiver operating characteristics (ROC) curve (AUC) were calculated to evaluate model performance. Results The results indicate the acceptable ability of the U-Net-DenseNet121 model in detecting COVID-19 abnormality (accuracy = 0.88 and AUC = 0.96 for thresholds of 0.13 and accuracy = 0.88 and AUC = 0.90 for thresholds of 0.2). Based on this model, we developed the "Imaging-Tech" web-based application for use at hospitals and clinics to make our project's output more practical and attractive in the market. Conclusion We designed a DL-based model for the segmentation of COVID-19 CT scan images and, based on this model, constructed a web-based application that, according to the results, is a reliable detector for infected tissue in lung CT-scans. The availability of such tools would aid in automating, prioritizing, fastening, and broadening the treatment of COVID-19 patients globally.
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Affiliation(s)
| | - Ashraf Mahboobi
- Department of Radiologist, Babol University of Medical Sciences, Babol, Iran
| | - Farzin Arabzadeh
- Department of Radiologist, Dr. Arabzadeh Radiology and Sonography Clinic, Behbahan, Iran
| | - Nazanin Ramezani
- School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Helia S. Moosavi
- Computer Science Bachelor Degree, University of Toronto, On, Canada
| | - Golbarg Mehrpoor
- Department of Rheumatologist, Alborz University of Medical Sciences, Karaj, Iran
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Sarmini, Lailiyah F, Suprapto, Faidah M. The important issue of awareness of disaster response to the COVID-19. Heliyon 2024; 10:e23880. [PMID: 38226289 PMCID: PMC10788434 DOI: 10.1016/j.heliyon.2023.e23880] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2021] [Revised: 12/04/2023] [Accepted: 12/14/2023] [Indexed: 01/17/2024] Open
Abstract
This study aimed to analyze the community's disaster response awareness during the Covid-19 pandemic during the implementation of The Large-Scale Social Restrictions (LSSR) in Gresik. Self-awareness was observed using Peter L. Berger and Thomas Luck man's Social Construction Theory through a dialectical process of internalization, objectification, and externalization. The results showed that there had been no good awareness in efforts to prevent the spread of Covid-19 in Gresik. Socially, the community had not taken the dangers of Covid-19 seriously. There was also an inconsistency between knowledge and reality related to disaster response. In coping with the dialectical process, the community had not implemented a disaster-aware culture by obeying the existing regulations. At least, the sociocultural environment determined a person's construction for self-identification, interaction, and adjustment to the social changes that occurred. Hence, the sociocultural construction of the community had never made the disease outbreak a serious problem and considered it as well as God's reminder even though infected cases continued to increase. A situation was indeed difficult for the Task Force to succeed in the Large-Scale Social Restrictions (LSSR) to Community Activities Restrictions Enforcement (CARE) suppressed the increase of Covid-19 positive cases in Gresik, Indonesia. This research sees that the government's policies in handling Covid- 19 are not enough, it needs more optimal involvement of community and religious leaders, to provide education on the importance of maintaining health protocols and building self-awareness of the dangers of Covid.
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Affiliation(s)
- Sarmini
- Universitas Negeri Surabaya, Indonesia
| | | | - Suprapto
- Universitas Negeri Surabaya, Indonesia
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Zhan Y, Cai DC, Liu Y, Song F, Shan F, Song P, Chen G, Zhang Y, Wang H, Shi Y. Altered metabolism in right basal ganglia associated with asymptomatic neurocognitive impairment in HIV-infected individuals. Heliyon 2024; 10:e23342. [PMID: 38169709 PMCID: PMC10758793 DOI: 10.1016/j.heliyon.2023.e23342] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Revised: 06/02/2023] [Accepted: 12/01/2023] [Indexed: 01/05/2024] Open
Abstract
Background Only few studies have focused on the metabolite differences between asymptomatic neurocognitive impairment (ANI) and cognitively normal people living with HIV (PLWH). The current study aims to examine whether brain metabolisms in basal ganglia (BG) by magnetic resonance spectroscopy (MRS) were potential to discriminate ANI from cognitively normal PLWH. Methods According to neuropsychological (NP) test, 80 PLWH (37.4 ± 10.2 years) were divided into ANI group (HIV-ANI, n = 31) and NP normal group (HIV-normal, n = 49). Brain metabolisms by MRS from right BG were compared between groups, including N-acetylaspartate and N-acetyl aspartylglutamate (tNAA), creatine and phosphocreatine (tCr), and choline-containing compounds (tCho). A total value of three metabolites were introduced. All brain metabolisms were evaluated as its percentage of total. Furthermore, correlations between MRS and NP and clinical measures were evaluated. A logistic regression model was applied, and the AUC values for the model and the continuous factors were compared using receiver operating curve (ROC) analysis. Results Compared to HIV-normal group, tNAA/total was lower and tCr/total was higher in the HIV-ANI group (P < 0.05). Both tNAA/total and tCr/total values were correlated with NP score (P < 0.05), especially in verbal fluency, speed of information processing, learning, and recall (P < 0.05). The logistic model included BG-tCr/total, current CD4 and infection years of PLWH. The AUC value for the BG-tCr/total was 0.696 and was not significantly lower than that for logistic model (P < 0.01). Conclusion The altered brain metabolites in the right BG were found in the ANI group compared to PLWH with normal cognition, and further associated with NP deficits. The current findings indicated that brain metabolites assessed by MRS has the potential to discriminate ANI from cognitively normal PLWH.
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Affiliation(s)
- Yi Zhan
- Department of Radiology, Shanghai Public Health Clinical Center, Fudan University, Shanghai, China
| | - Dan-Chao Cai
- Department of Radiology, Shanghai Public Health Clinical Center, Fudan University, Shanghai, China
| | - Ying Liu
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Fengxiang Song
- Department of Radiology, Shanghai Public Health Clinical Center, Fudan University, Shanghai, China
| | - Fei Shan
- Department of Radiology, Shanghai Public Health Clinical Center, Fudan University, Shanghai, China
| | - Pengrui Song
- Department of Radiology, Shanghai Public Health Clinical Center, Fudan University, Shanghai, China
| | - Guochao Chen
- Department of Radiology, Shanghai Public Health Clinical Center, Fudan University, Shanghai, China
| | - Yijun Zhang
- Department of Radiology, Shanghai Public Health Clinical Center, Fudan University, Shanghai, China
| | - He Wang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Human Phenome Institute, Fudan University, Shanghai, China
| | - Yuxin Shi
- Department of Radiology, Shanghai Public Health Clinical Center, Fudan University, Shanghai, China
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Kataoka Y, Tanabe N, Shirata M, Hamao N, Oi I, Maetani T, Shiraishi Y, Hashimoto K, Yamazoe M, Shima H, Ajimizu H, Oguma T, Emura M, Endo K, Hasegawa Y, Mio T, Shiota T, Yasui H, Nakaji H, Tsuchiya M, Tomii K, Hirai T, Ito I. Artificial intelligence-based analysis of the spatial distribution of abnormal computed tomography patterns in SARS-CoV-2 pneumonia: association with disease severity. Respir Res 2024; 25:24. [PMID: 38200566 PMCID: PMC10777587 DOI: 10.1186/s12931-024-02673-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Accepted: 01/03/2024] [Indexed: 01/12/2024] Open
Abstract
BACKGROUND The substantial heterogeneity of clinical presentations in patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pneumonia still requires robust chest computed tomography analysis to identify high-risk patients. While extension of ground-glass opacity and consolidation from peripheral to central lung fields on chest computed tomography (CT) might be associated with severely ill conditions, quantification of the central-peripheral distribution of ground glass opacity and consolidation in assessments of SARS-CoV-2 pneumonia remains unestablished. This study aimed to examine whether the central-peripheral distributions of ground glass opacity and consolidation were associated with severe outcomes in patients with SARS-CoV-2 pneumonia independent of the whole-lung extents of these abnormal shadows. METHODS This multicenter retrospective cohort included hospitalized patients with SARS-CoV-2 pneumonia between January 2020 and August 2021. An artificial intelligence-based image analysis technology was used to segment abnormal shadows, including ground glass opacity and consolidation. The area ratio of ground glass opacity and consolidation to the whole lung (GGO%, CON%) and the ratio of ground glass opacity and consolidation areas in the central lungs to those in the peripheral lungs (GGO(C/P)) and (CON(C/P)) were automatically calculated. Severe outcome was defined as in-hospital death or requirement for endotracheal intubation. RESULTS Of 512 enrolled patients, the severe outcome was observed in 77 patients. GGO% and CON% were higher in patients with severe outcomes than in those without. Multivariable logistic models showed that GGO(C/P), but not CON(C/P), was associated with the severe outcome independent of age, sex, comorbidities, GGO%, and CON%. CONCLUSION In addition to GGO% and CON% in the whole lung, the higher the ratio of ground glass opacity in the central regions to that in the peripheral regions was, the more severe the outcomes in patients with SARS-CoV-2 pneumonia were. The proposed method might be useful to reproducibly quantify the extension of ground glass opacity from peripheral to central lungs and to estimate prognosis.
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Affiliation(s)
- Yusuke Kataoka
- Department of Respiratory Medicine, Graduate School of Medicine, Kyoto University, 54 Kawahara-Cho, Shogoin, Sakyo-Ku, Kyoto, 606-8507, Japan
| | - Naoya Tanabe
- Department of Respiratory Medicine, Graduate School of Medicine, Kyoto University, 54 Kawahara-Cho, Shogoin, Sakyo-Ku, Kyoto, 606-8507, Japan.
| | - Masahiro Shirata
- Department of Respiratory Medicine, Graduate School of Medicine, Kyoto University, 54 Kawahara-Cho, Shogoin, Sakyo-Ku, Kyoto, 606-8507, Japan
| | - Nobuyoshi Hamao
- Department of Respiratory Medicine, Graduate School of Medicine, Kyoto University, 54 Kawahara-Cho, Shogoin, Sakyo-Ku, Kyoto, 606-8507, Japan
- Department of Internal Medicine, Sugita Genpaku Memorial Obama Municipal Hospital, Obama, Japan
| | - Issei Oi
- Department of Respiratory Medicine, Graduate School of Medicine, Kyoto University, 54 Kawahara-Cho, Shogoin, Sakyo-Ku, Kyoto, 606-8507, Japan
- Department of Internal Medicine, Sugita Genpaku Memorial Obama Municipal Hospital, Obama, Japan
| | - Tomoki Maetani
- Department of Respiratory Medicine, Graduate School of Medicine, Kyoto University, 54 Kawahara-Cho, Shogoin, Sakyo-Ku, Kyoto, 606-8507, Japan
| | - Yusuke Shiraishi
- Department of Respiratory Medicine, Graduate School of Medicine, Kyoto University, 54 Kawahara-Cho, Shogoin, Sakyo-Ku, Kyoto, 606-8507, Japan
| | - Kentaro Hashimoto
- Department of Respiratory Medicine, Graduate School of Medicine, Kyoto University, 54 Kawahara-Cho, Shogoin, Sakyo-Ku, Kyoto, 606-8507, Japan
| | - Masatoshi Yamazoe
- Department of Respiratory Medicine, Graduate School of Medicine, Kyoto University, 54 Kawahara-Cho, Shogoin, Sakyo-Ku, Kyoto, 606-8507, Japan
| | - Hiroshi Shima
- Department of Respiratory Medicine, Graduate School of Medicine, Kyoto University, 54 Kawahara-Cho, Shogoin, Sakyo-Ku, Kyoto, 606-8507, Japan
| | - Hitomi Ajimizu
- Department of Respiratory Medicine, Graduate School of Medicine, Kyoto University, 54 Kawahara-Cho, Shogoin, Sakyo-Ku, Kyoto, 606-8507, Japan
| | - Tsuyoshi Oguma
- Department of Respiratory Medicine, Graduate School of Medicine, Kyoto University, 54 Kawahara-Cho, Shogoin, Sakyo-Ku, Kyoto, 606-8507, Japan
- Department of Respiratory Medicine, Kyoto City Hospital, Kyoto, Japan
| | - Masahito Emura
- Department of Respiratory Medicine, Kyoto City Hospital, Kyoto, Japan
| | - Kazuo Endo
- Department of Respiratory Medicine, Hyogo Prefectural Amagasaki General Medical Center, Amagasaki, Japan
| | - Yoshinori Hasegawa
- Department of Respiratory Medicine, Osaka Saiseikai Nakatsu Hospital, Osaka, Japan
| | - Tadashi Mio
- Division of Respiratory Medicine, Center for Respiratory Diseases, National Hospital Organization Kyoto Medical Center, Kyoto, Japan
| | | | - Hiroaki Yasui
- Department of Internal Medicine, Horikawa Hospital, Kyoto, Japan
| | - Hitoshi Nakaji
- Department of Respiratory Medicine, Toyooka Hospital, Toyooka, Japan
| | - Michiko Tsuchiya
- Department of Respiratory Medicine, Rakuwakai Otowa Hospital, Kyoto, Japan
| | - Keisuke Tomii
- Department of Respiratory Medicine, Kobe City Medical Center General Hospital, Kobe, Japan
| | - Toyohiro Hirai
- Department of Respiratory Medicine, Graduate School of Medicine, Kyoto University, 54 Kawahara-Cho, Shogoin, Sakyo-Ku, Kyoto, 606-8507, Japan
| | - Isao Ito
- Department of Respiratory Medicine, Graduate School of Medicine, Kyoto University, 54 Kawahara-Cho, Shogoin, Sakyo-Ku, Kyoto, 606-8507, Japan.
- Department of Internal Medicine, Sugita Genpaku Memorial Obama Municipal Hospital, Obama, Japan.
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36
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Chen X, Chen F, Liang C, He G, Chen H, Wu Y, Chen Y, Shuai J, Yang Y, Dai C, Cao L, Wang X, Cai E, Wang J, Wu M, Zeng L, Zhu J, Hai D, Pan W, Pan S, Zhang C, Quan S, Su F. MRI advances in the imaging diagnosis of tuberculous meningitis: opportunities and innovations. Front Microbiol 2023; 14:1308149. [PMID: 38149270 PMCID: PMC10750405 DOI: 10.3389/fmicb.2023.1308149] [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: 10/11/2023] [Accepted: 11/14/2023] [Indexed: 12/28/2023] Open
Abstract
Tuberculous meningitis (TBM) is not only one of the most fatal forms of tuberculosis, but also a major public health concern worldwide, presenting grave clinical challenges due to its nonspecific symptoms and the urgent need for timely intervention. The severity and the rapid progression of TBM underscore the necessity of early and accurate diagnosis to prevent irreversible neurological deficits and reduce mortality rates. Traditional diagnostic methods, reliant primarily on clinical findings and cerebrospinal fluid analysis, often falter in delivering timely and conclusive results. Moreover, such methods struggle to distinguish TBM from other forms of neuroinfections, making it critical to seek advanced diagnostic solutions. Against this backdrop, magnetic resonance imaging (MRI) has emerged as an indispensable modality in diagnostics, owing to its unique advantages. This review provides an overview of the advancements in MRI technology, specifically emphasizing its crucial applications in the early detection and identification of complex pathological changes in TBM. The integration of artificial intelligence (AI) has further enhanced the transformative impact of MRI on TBM diagnostic imaging. When these cutting-edge technologies synergize with deep learning algorithms, they substantially improve diagnostic precision and efficiency. Currently, the field of TBM imaging diagnosis is undergoing a phase of technological amalgamation. The melding of MRI and AI technologies unquestionably signals new opportunities in this specialized area.
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Affiliation(s)
- Xingyu Chen
- Department of Infectious Diseases, Wenzhou Central Hospital, Wenzhou, China
- The First School of Medicine, Wenzhou Medical University, Wenzhou, China
| | - Fanxuan Chen
- School of Biomedical Engineering, School of Ophthalmology and Optometry, Eye Hospital, Wenzhou Medical University, Wenzhou, China
| | - Chenglong Liang
- The First School of Medicine, Wenzhou Medical University, Wenzhou, China
| | - Guoqiang He
- Postgraduate Training Base Alliance of Wenzhou Medical University, Wenzhou, China
- Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou, China
| | - Hao Chen
- The First School of Medicine, Wenzhou Medical University, Wenzhou, China
| | - Yanchan Wu
- School of Electrical and Information Engineering, Quzhou University, Quzhou, China
| | - Yinda Chen
- School of Electrical and Information Engineering, Quzhou University, Quzhou, China
| | - Jincen Shuai
- Baskin Engineering, University of California, Santa Cruz, CA, United States
| | - Yilei Yang
- The First School of Medicine, Wenzhou Medical University, Wenzhou, China
| | | | - Luhuan Cao
- Wenzhou Medical University, Wenzhou, China
| | - Xian Wang
- Wenzhou Medical University, Wenzhou, China
| | - Enna Cai
- Wenzhou Medical University, Wenzhou, China
| | | | | | - Li Zeng
- Wenzhou Medical University, Wenzhou, China
| | | | - Darong Hai
- Wenzhou Medical University, Wenzhou, China
| | - Wangzheng Pan
- Renji College of Wenzhou Medical University, Wenzhou, China
| | - Shuo Pan
- The First School of Medicine, Wenzhou Medical University, Wenzhou, China
| | - Chengxi Zhang
- School of Materials Science and Engineering, Shandong Jianzhu University, Jinan, China
| | - Shichao Quan
- Department of Big Data in Health Science, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
- Key Laboratory of Intelligent Treatment and Life Support for Critical Diseases of Zhejiang Province, Wenzhou, China
- Zhejiang Engineering Research Center for Hospital Emergency and Process Digitization, Wenzhou, China
| | - Feifei Su
- Department of Infectious Diseases, Wenzhou Central Hospital, Wenzhou, China
- Wenzhou Key Laboratory of Diagnosis and Treatment of Emerging and Recurrent Infectious Diseases, Wenzhou, China
- Department of Infectious Diseases, Wenzhou Sixth People’s Hospital, Wenzhou, China
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37
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Staudner ST, Leininger SB, Vogel MJ, Mustroph J, Hubauer U, Meindl C, Wallner S, Lehn P, Burkhardt R, Hanses F, Zimmermann M, Scharf G, Hamer OW, Maier LS, Hupf J, Jungbauer CG. Dipeptidyl-peptidase 3 and IL-6: potential biomarkers for diagnostics in COVID-19 and association with pulmonary infiltrates. Clin Exp Med 2023; 23:4919-4935. [PMID: 37733154 PMCID: PMC10725357 DOI: 10.1007/s10238-023-01193-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Accepted: 09/08/2023] [Indexed: 09/22/2023]
Abstract
Coronavirus SARS-CoV-2 spread worldwide, causing a respiratory disease known as COVID-19. The aim of the present study was to examine whether Dipeptidyl-peptidase 3 (DPP3) and the inflammatory biomarkers IL-6, CRP, and leucocytes are associated with COVID-19 and able to predict the severity of pulmonary infiltrates in COVID-19 patients versus non-COVID-19 patients. 114 COVID-19 patients and 35 patients with respiratory infections other than SARS-CoV-2 were included in our prospective observational study. Blood samples were collected at presentation to the emergency department. 102 COVID-19 patients and 28 non-COVID-19 patients received CT imaging (19 outpatients did not receive CT imaging). If CT imaging was available, artificial intelligence software (CT Pneumonia Analysis) was used to quantify pulmonary infiltrates. According to the median of infiltrate (14.45%), patients who obtained quantitative CT analysis were divided into two groups (> median: 55 COVID-19 and nine non-COVID-19, ≤ median: 47 COVID-19 and 19 non-COVID-19). DPP3 was significantly elevated in COVID-19 patients (median 20.85 ng/ml, 95% CI 18.34-24.40 ng/ml), as opposed to those without SARS-CoV-2 (median 13.80 ng/ml, 95% CI 11.30-17.65 ng/ml; p < 0.001, AUC = 0.72), opposite to IL-6, CRP (each p = n.s.) and leucocytes (p < 0.05, but lower levels in COVID-19 patients). Regarding binary logistic regression analysis, higher DPP3 concentrations (OR = 1.12, p < 0.001) and lower leucocytes counts (OR = 0.76, p < 0.001) were identified as significant and independent predictors of SARS-CoV-2 infection, as opposed to IL-6 and CRP (each p = n.s.). IL-6 was significantly increased in patients with infiltrate above the median compared to infiltrate below the median both in COVID-19 (p < 0.001, AUC = 0.78) and in non-COVID-19 (p < 0.05, AUC = 0.81). CRP, DPP3, and leucocytes were increased in COVID-19 patients with infiltrate above median (each p < 0.05, AUC: CRP 0.82, DPP3 0.70, leucocytes 0.67) compared to infiltrate below median, opposite to non-COVID-19 (each p = n.s.). Regarding multiple linear regression analysis in COVID-19, CRP, IL-6, and leucocytes (each p < 0.05) were associated with the degree of pulmonary infiltrates, as opposed to DPP3 (p = n.s.). DPP3 showed the potential to be a COVID-19-specific biomarker. IL-6 might serve as a prognostic marker to assess the extent of pulmonary infiltrates in respiratory patients.
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Affiliation(s)
- Stephan T Staudner
- Department of Internal Medicine II, University Hospital Regensburg, Regensburg, Germany.
| | - Simon B Leininger
- Department of Internal Medicine II, University Hospital Regensburg, Regensburg, Germany
| | - Manuel J Vogel
- Department of Internal Medicine II, University Hospital Regensburg, Regensburg, Germany
| | - Julian Mustroph
- Department of Internal Medicine II, University Hospital Regensburg, Regensburg, Germany
| | - Ute Hubauer
- Department of Internal Medicine II, University Hospital Regensburg, Regensburg, Germany
| | - Christine Meindl
- Department of Internal Medicine II, University Hospital Regensburg, Regensburg, Germany
| | - Stefan Wallner
- Department of Clinical Chemistry and Laboratory Medicine, University Hospital Regensburg, Regensburg, Germany
| | - Petra Lehn
- Department of Clinical Chemistry and Laboratory Medicine, University Hospital Regensburg, Regensburg, Germany
| | - Ralph Burkhardt
- Department of Clinical Chemistry and Laboratory Medicine, University Hospital Regensburg, Regensburg, Germany
| | - Frank Hanses
- Emergency Department, University Hospital Regensburg, Regensburg, Germany
- Department of Infection Prevention and Infectious Diseases, University Hospital Regensburg, Regensburg, Germany
| | - Markus Zimmermann
- Emergency Department, University Hospital Regensburg, Regensburg, Germany
| | - Gregor Scharf
- Department of Radiology, University Hospital Regensburg, Regensburg, Germany
| | - Okka W Hamer
- Department of Radiology, University Hospital Regensburg, Regensburg, Germany
| | - Lars S Maier
- Department of Internal Medicine II, University Hospital Regensburg, Regensburg, Germany
| | - Julian Hupf
- Emergency Department, University Hospital Regensburg, Regensburg, Germany
| | - Carsten G Jungbauer
- Department of Internal Medicine II, University Hospital Regensburg, Regensburg, Germany
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38
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Li Y, Shen Y, Zhang J, Song S, Li Z, Ke J, Shen D. A Hierarchical Graph V-Net With Semi-Supervised Pre-Training for Histological Image Based Breast Cancer Classification. IEEE TRANSACTIONS ON MEDICAL IMAGING 2023; 42:3907-3918. [PMID: 37725717 DOI: 10.1109/tmi.2023.3317132] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/21/2023]
Abstract
Numerous patch-based methods have recently been proposed for histological image based breast cancer classification. However, their performance could be highly affected by ignoring spatial contextual information in the whole slide image (WSI). To address this issue, we propose a novel hierarchical Graph V-Net by integrating 1) patch-level pre-training and 2) context-based fine-tuning, with a hierarchical graph network. Specifically, a semi-supervised framework based on knowledge distillation is first developed to pre-train a patch encoder for extracting disease-relevant features. Then, a hierarchical Graph V-Net is designed to construct a hierarchical graph representation from neighboring/similar individual patches for coarse-to-fine classification, where each graph node (corresponding to one patch) is attached with extracted disease-relevant features and its target label during training is the average label of all pixels in the corresponding patch. To evaluate the performance of our proposed hierarchical Graph V-Net, we collect a large WSI dataset of 560 WSIs, with 30 labeled WSIs from the BACH dataset (through our further refinement), 30 labeled WSIs and 500 unlabeled WSIs from Yunnan Cancer Hospital. Those 500 unlabeled WSIs are employed for patch-level pre-training to improve feature representation, while 60 labeled WSIs are used to train and test our proposed hierarchical Graph V-Net. Both comparative assessment and ablation studies demonstrate the superiority of our proposed hierarchical Graph V-Net over state-of-the-art methods in classifying breast cancer from WSIs. The source code and our annotations for the BACH dataset have been released at https://github.com/lyhkevin/Graph-V-Net.
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Zhang J, Cui Z, Zhou L, Sun Y, Li Z, Liu Z, Shen D. Breast Fibroglandular Tissue Segmentation for Automated BPE Quantification With Iterative Cycle-Consistent Semi-Supervised Learning. IEEE TRANSACTIONS ON MEDICAL IMAGING 2023; 42:3944-3955. [PMID: 37756174 DOI: 10.1109/tmi.2023.3319646] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/29/2023]
Abstract
Background Parenchymal Enhancement (BPE) quantification in Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) plays a pivotal role in clinical breast cancer diagnosis and prognosis. However, the emerging deep learning-based breast fibroglandular tissue segmentation, a crucial step in automated BPE quantification, often suffers from limited training samples with accurate annotations. To address this challenge, we propose a novel iterative cycle-consistent semi-supervised framework to leverage segmentation performance by using a large amount of paired pre-/post-contrast images without annotations. Specifically, we design the reconstruction network, cascaded with the segmentation network, to learn a mapping from the pre-contrast images and segmentation predictions to the post-contrast images. Thus, we can implicitly use the reconstruction task to explore the inter-relationship between these two-phase images, which in return guides the segmentation task. Moreover, the reconstructed post-contrast images across multiple auto-context modeling-based iterations can be viewed as new augmentations, facilitating cycle-consistent constraints across each segmentation output. Extensive experiments on two datasets with various data distributions show great segmentation and BPE quantification accuracy compared with other state-of-the-art semi-supervised methods. Importantly, our method achieves 11.80 times of quantification accuracy improvement along with 10 times faster, compared with clinical physicians, demonstrating its potential for automated BPE quantification. The code is available at https://github.com/ZhangJD-ong/Iterative-Cycle-consistent-Semi-supervised-Learning-for-fibroglandular-tissue-segmentation.
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40
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Nardi C, Magnini A, Calistri L, Cavigli E, Peired AJ, Rastrelli V, Carlesi E, Zantonelli G, Smorchkova O, Cinci L, Orlandi M, Landini N, Berillo E, Lorini C, Mencarini J, Colao MG, Gori L, Luzzi V, Lazzeri C, Cipriani E, Bonizzoli M, Pieralli F, Nozzoli C, Morettini A, Lavorini F, Bartoloni A, Rossolini GM, Matucci-Cerinic M, Tomassetti S, Colagrande S. Doubts and concerns about COVID-19 uncertainties on imaging data, clinical score, and outcomes. BMC Pulm Med 2023; 23:472. [PMID: 38007479 PMCID: PMC10675953 DOI: 10.1186/s12890-023-02763-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Accepted: 11/15/2023] [Indexed: 11/27/2023] Open
Abstract
BACKGROUND COVID-19 is a pandemic disease affecting predominantly the respiratory apparatus with clinical manifestations ranging from asymptomatic to respiratory failure. Chest CT is a crucial tool in diagnosing and evaluating the severity of pulmonary involvement through dedicated scoring systems. Nonetheless, many questions regarding the relationship of radiologic and clinical features of the disease have emerged in multidisciplinary meetings. The aim of this retrospective study was to explore such relationship throughout an innovative and alternative approach. MATERIALS AND METHODS This study included 550 patients (range 25-98 years; 354 males, mean age 66.1; 196 females, mean age 70.9) hospitalized for COVID-19 with available radiological and clinical data between 1 March 2021 and 30 April 2022. Radiological data included CO-RADS, chest CT score, dominant pattern, and typical/atypical findings detected on CT examinations. Clinical data included clinical score and outcome. The relationship between such features was investigated through the development of the main four frequently asked questions summarizing the many issues arisen in multidisciplinary meetings, as follows 1) CO-RADS, chest CT score, clinical score, and outcomes; 2) the involvement of a specific lung lobe and outcomes; 3) dominant pattern/distribution and severity score for the same chest CT score; 4) additional factors and outcomes. RESULTS 1) If CT was suggestive for COVID, a strong correlation between CT/clinical score and prognosis was found; 2) Middle lobe CT involvement was an unfavorable prognostic criterion; 3) If CT score < 50%, the pattern was not influential, whereas if CT score > 50%, crazy paving as dominant pattern leaded to a 15% increased death rate, stacked up against other patterns, thus almost doubling it; 4) Additional factors usually did not matter, but lymph-nodes and pleural effusion worsened prognosis. CONCLUSIONS This study outlined those radiological features of COVID-19 most relevant towards disease severity and outcome with an innovative approach.
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Affiliation(s)
- Cosimo Nardi
- Department of Experimental and Clinical Biomedical Sciences, Radiodiagnostic Unit n. 2, University of Florence-Careggi University Hospital, Largo Brambilla 3, 50134, Florence, Italy
| | - Andrea Magnini
- Department of Experimental and Clinical Biomedical Sciences, Radiodiagnostic Unit n. 2, University of Florence-Careggi University Hospital, Largo Brambilla 3, 50134, Florence, Italy
| | - Linda Calistri
- Department of Experimental and Clinical Biomedical Sciences, Radiodiagnostic Unit n. 2, University of Florence-Careggi University Hospital, Largo Brambilla 3, 50134, Florence, Italy
| | - Edoardo Cavigli
- Department of Radiology, Careggi University Hospital, Largo Brambilla 3, 50134, Florence, Italy
| | - Anna Julie Peired
- Department of Experimental and Clinical Biomedical Sciences "Mario Serio", University of Florence, Largo Brambilla 3, 50134, Florence, Italy
| | - Vieri Rastrelli
- Department of Experimental and Clinical Biomedical Sciences, Radiodiagnostic Unit n. 2, University of Florence-Careggi University Hospital, Largo Brambilla 3, 50134, Florence, Italy
| | - Edoardo Carlesi
- Neuroradiology Unit, Department of Radiology, Careggi University Hospital, Largo Brambilla 3, 50134, Florence, Italy
| | - Giulia Zantonelli
- Department of Experimental and Clinical Biomedical Sciences, Radiodiagnostic Unit n. 2, University of Florence-Careggi University Hospital, Largo Brambilla 3, 50134, Florence, Italy
| | - Olga Smorchkova
- Department of Experimental and Clinical Biomedical Sciences, Radiodiagnostic Unit n. 2, University of Florence-Careggi University Hospital, Largo Brambilla 3, 50134, Florence, Italy
| | - Lorenzo Cinci
- Department of Experimental and Clinical Biomedical Sciences, Radiodiagnostic Unit n. 2, University of Florence-Careggi University Hospital, Largo Brambilla 3, 50134, Florence, Italy
| | - Martina Orlandi
- Department of Experimental and Clinical Medicine, Division of Rheumatology, Careggi University Hospital, University of Florence, Largo Brambilla 3, 50134, Florence, Italy
| | - Nicholas Landini
- Department of Radiological, Oncological and Pathological Sciences, Policlinico Umberto I Hospital, "Sapienza" Rome University, Rome, Italy
| | - Edoardo Berillo
- Department of Clinical and Experimental Medicine, Interventional Pulmonology Unit, Careggi University Hospital, Largo Brambilla 3, 50134, Florence, Italy
| | - Chiara Lorini
- Department of Health Sciences, University of Florence, Largo Brambilla 3, 50134, Florence, Italy
| | - Jessica Mencarini
- Department of Experimental and Clinical Medicine, Infectious and Tropical Diseases Unit, Careggi University Hospital, University of Florence, Largo Brambilla 3, 50134, Florence, Italy
| | - Maria Grazia Colao
- Department of Experimental and Clinical Medicine, University of Florence, Largo Brambilla 3, 50134, Florence, Italy
- Clinical Microbiology and Virology Unit, Florence Careggi University Hospital, Largo Brambilla 3, 50134, Florence, Italy
| | - Leonardo Gori
- Department of Clinical and Experimental Medicine, Interventional Pulmonology Unit, Careggi University Hospital, Largo Brambilla 3, 50134, Florence, Italy
| | - Valentina Luzzi
- Department of Clinical and Experimental Medicine, Interventional Pulmonology Unit, Careggi University Hospital, Largo Brambilla 3, 50134, Florence, Italy
| | - Chiara Lazzeri
- Intensive Care Unit and Regional ECMO Referral Centre, Azienda Ospedaliero-Universitaria Careggi, Largo Brambilla 3, 50134, Florence, Italy
| | - Elisa Cipriani
- Intensive Care Unit and Regional ECMO Referral Centre, Azienda Ospedaliero-Universitaria Careggi, Largo Brambilla 3, 50134, Florence, Italy
| | - Manuela Bonizzoli
- Intensive Care Unit and Regional ECMO Referral Centre, Azienda Ospedaliero-Universitaria Careggi, Largo Brambilla 3, 50134, Florence, Italy
| | - Filippo Pieralli
- Intermediate Care Unit, University Hospital Careggi, Largo Brambilla 3, 50134, Florence, Italy
| | - Carlo Nozzoli
- Internal Medicine Unit 1, Careggi University Hospital, Largo Brambilla 3, 50134, Florence, Italy
| | - Alessandro Morettini
- Internal Medicine Unit 2, Careggi University Hospital, Largo Brambilla 3, 50134, Florence, Italy
| | - Federico Lavorini
- Department of Experimental and Clinical Medicine, Division of Pulmonology, Careggi University Hospital, University of Florence, Largo Brambilla 3, 50134, Florence, Italy
| | - Alessandro Bartoloni
- Department of Experimental and Clinical Medicine, Infectious and Tropical Diseases Unit, Careggi University Hospital, University of Florence, Largo Brambilla 3, 50134, Florence, Italy
| | - Gian Maria Rossolini
- Department of Experimental and Clinical Medicine, University of Florence, Largo Brambilla 3, 50134, Florence, Italy
- Clinical Microbiology and Virology Unit, Florence Careggi University Hospital, Largo Brambilla 3, 50134, Florence, Italy
| | - Marco Matucci-Cerinic
- Department of Experimental and Clinical Medicine, Division of Rheumatology, Careggi University Hospital, University of Florence, Largo Brambilla 3, 50134, Florence, Italy
| | - Sara Tomassetti
- Department of Clinical and Experimental Medicine, Interventional Pulmonology Unit, Careggi University Hospital, Largo Brambilla 3, 50134, Florence, Italy
| | - Stefano Colagrande
- Department of Experimental and Clinical Biomedical Sciences, Radiodiagnostic Unit n. 2, University of Florence-Careggi University Hospital, Largo Brambilla 3, 50134, Florence, Italy.
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Chu WT, Castro MA, Reza S, Cooper TK, Bartlinski S, Bradley D, Anthony SM, Worwa G, Finch CL, Kuhn JH, Crozier I, Solomon J. Novel machine-learning analysis of SARS-CoV-2 infection in a subclinical nonhuman primate model using radiomics and blood biomarkers. Sci Rep 2023; 13:19607. [PMID: 37950044 PMCID: PMC10638262 DOI: 10.1038/s41598-023-46694-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Accepted: 11/03/2023] [Indexed: 11/12/2023] Open
Abstract
Detection of the physiological response to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is challenging in the absence of overt clinical signs but remains necessary to understand a full subclinical disease spectrum. In this study, our objective was to use radiomics (from computed tomography images) and blood biomarkers to predict SARS-CoV-2 infection in a nonhuman primate model (NHP) with inapparent clinical disease. To accomplish this aim, we built machine-learning models to predict SARS-CoV-2 infection in a NHP model of subclinical disease using baseline-normalized radiomic and blood sample analyses data from SARS-CoV-2-exposed and control (mock-exposed) crab-eating macaques. We applied a novel adaptation of the minimum redundancy maximum relevance (mRMR) feature-selection technique, called mRMR-permute, for statistically-thresholded and unbiased feature selection. Through performance comparison of eight machine-learning models trained on 14 feature sets, we demonstrated that a logistic regression model trained on the mRMR-permute feature set can predict SARS-CoV-2 infection with very high accuracy. Eighty-nine percent of mRMR-permute selected features had strong and significant class effects. Through this work, we identified a key set of radiomic and blood biomarkers that can be used to predict infection status even in the absence of clinical signs. Furthermore, we proposed and demonstrated the utility of a novel feature-selection technique called mRMR-permute. This work lays the foundation for the prediction and classification of SARS-CoV-2 disease severity.
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Affiliation(s)
- Winston T Chu
- Center for Infectious Disease Imaging, Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, MD, USA
| | - Marcelo A Castro
- Integrated Research Facility at Fort Detrick, Division of Clinical Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Frederick, MD, USA
| | - Syed Reza
- Center for Infectious Disease Imaging, Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, MD, USA
| | - Timothy K Cooper
- Integrated Research Facility at Fort Detrick, Division of Clinical Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Frederick, MD, USA
| | - Sean Bartlinski
- Integrated Research Facility at Fort Detrick, Division of Clinical Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Frederick, MD, USA
| | - Dara Bradley
- Center for Infectious Disease Imaging, Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, MD, USA
| | - Scott M Anthony
- Integrated Research Facility at Fort Detrick, Division of Clinical Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Frederick, MD, USA
| | - Gabriella Worwa
- Integrated Research Facility at Fort Detrick, Division of Clinical Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Frederick, MD, USA
| | - Courtney L Finch
- Integrated Research Facility at Fort Detrick, Division of Clinical Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Frederick, MD, USA
| | - Jens H Kuhn
- Integrated Research Facility at Fort Detrick, Division of Clinical Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Frederick, MD, USA
| | - Ian Crozier
- Clinical Monitoring Research Program Directorate, Frederick National Laboratory for Cancer Research, Frederick, MD, USA
| | - Jeffrey Solomon
- Clinical Monitoring Research Program Directorate, Frederick National Laboratory for Cancer Research, Frederick, MD, USA.
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42
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Zhunissova U, Dzierżak R, Omiotek Z, Lytvynenko V. A Novel COVID-19 Diagnosis Approach Utilizing a Comprehensive Set of Diagnostic Information (CSDI). J Clin Med 2023; 12:6912. [PMID: 37959377 PMCID: PMC10649663 DOI: 10.3390/jcm12216912] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Revised: 10/19/2023] [Accepted: 10/31/2023] [Indexed: 11/15/2023] Open
Abstract
The aim of the study was to develop a computerized method for distinguishing COVID-19-affected cases from cases of pneumonia. This task continues to be a real challenge in the practice of diagnosing COVID-19 disease. In the study, a new approach was proposed, using a comprehensive set of diagnostic information (CSDI) including, among other things, medical history, demographic data, signs and symptoms of the disease, and laboratory results. These data have the advantage of being much more reliable compared with data based on a single source of information, such as radiological imaging. On this basis, a comprehensive process of building predictive models was carried out, including such steps as data preprocessing, feature selection, training, and evaluation of classification models. During the study, 9 different methods for feature selection were used, while the grid search method and 12 popular classification algorithms were employed to build classification models. The most effective model achieved a classification accuracy (ACC) of 85%, a sensitivity (TPR) equal to 83%, and a specificity (TNR) of 88%. The model was built using the random forest method with 15 features selected using the recursive feature elimination selection method. The results provide an opportunity to build a computer system to assist the physician in the diagnosis of the COVID-19 disease.
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Affiliation(s)
- Ulzhalgas Zhunissova
- Department of Biostatistics, Bioinformatics and Information Technologies, Astana Medical University, Beibitshilik Street 49A, Astana 010000, Kazakhstan
| | - Róża Dzierżak
- Faculty of Electrical Engineering and Computer Science, Lublin University of Technology, Nadbystrzycka 38 A, 20-618 Lublin, Poland
| | - Zbigniew Omiotek
- Faculty of Electrical Engineering and Computer Science, Lublin University of Technology, Nadbystrzycka 38 A, 20-618 Lublin, Poland
| | - Volodymyr Lytvynenko
- Department of Informatics and Computer Science, Kherson National Technical University, Beryslavs’ke Hwy, 24, 730082 Kherson, Kherson Oblast, Ukraine
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Schaudt D, von Schwerin R, Hafner A, Riedel P, Reichert M, von Schwerin M, Beer M, Kloth C. Augmentation strategies for an imbalanced learning problem on a novel COVID-19 severity dataset. Sci Rep 2023; 13:18299. [PMID: 37880333 PMCID: PMC10600145 DOI: 10.1038/s41598-023-45532-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Accepted: 10/20/2023] [Indexed: 10/27/2023] Open
Abstract
Since the beginning of the COVID-19 pandemic, many different machine learning models have been developed to detect and verify COVID-19 pneumonia based on chest X-ray images. Although promising, binary models have only limited implications for medical treatment, whereas the prediction of disease severity suggests more suitable and specific treatment options. In this study, we publish severity scores for the 2358 COVID-19 positive images in the COVIDx8B dataset, creating one of the largest collections of publicly available COVID-19 severity data. Furthermore, we train and evaluate deep learning models on the newly created dataset to provide a first benchmark for the severity classification task. One of the main challenges of this dataset is the skewed class distribution, resulting in undesirable model performance for the most severe cases. We therefore propose and examine different augmentation strategies, specifically targeting majority and minority classes. Our augmentation strategies show significant improvements in precision and recall values for the rare and most severe cases. While the models might not yet fulfill medical requirements, they serve as an appropriate starting point for further research with the proposed dataset to optimize clinical resource allocation and treatment.
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Affiliation(s)
- Daniel Schaudt
- Department of Computer Science, Ulm University of Applied Science, Albert-Einstein-Allee 55, 89081, Ulm, Baden-Wurttemberg, Germany.
| | - Reinhold von Schwerin
- Department of Computer Science, Ulm University of Applied Science, Albert-Einstein-Allee 55, 89081, Ulm, Baden-Wurttemberg, Germany
| | - Alexander Hafner
- Department of Computer Science, Ulm University of Applied Science, Albert-Einstein-Allee 55, 89081, Ulm, Baden-Wurttemberg, Germany
| | - Pascal Riedel
- Department of Computer Science, Ulm University of Applied Science, Albert-Einstein-Allee 55, 89081, Ulm, Baden-Wurttemberg, Germany
| | - Manfred Reichert
- Institute of Databases and Information Systems, Ulm University, James-Franck-Ring, 89081, Ulm, Baden-Wurttemberg, Germany
| | - Marianne von Schwerin
- Department of Computer Science, Ulm University of Applied Science, Albert-Einstein-Allee 55, 89081, Ulm, Baden-Wurttemberg, Germany
| | - Meinrad Beer
- Department of Radiology, University Hospital of Ulm, Albert-Einstein-Allee 23, 89081, Ulm, Baden-Wurttemberg, Germany
| | - Christopher Kloth
- Department of Radiology, University Hospital of Ulm, Albert-Einstein-Allee 23, 89081, Ulm, Baden-Wurttemberg, Germany
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Shi Y, Zhang C, Pan S, Chen Y, Miao X, He G, Wu Y, Ye H, Weng C, Zhang H, Zhou W, Yang X, Liang C, Chen D, Hong L, Su F. The diagnosis of tuberculous meningitis: advancements in new technologies and machine learning algorithms. Front Microbiol 2023; 14:1290746. [PMID: 37942080 PMCID: PMC10628659 DOI: 10.3389/fmicb.2023.1290746] [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: 09/08/2023] [Accepted: 10/09/2023] [Indexed: 11/10/2023] Open
Abstract
Tuberculous meningitis (TBM) poses a diagnostic challenge, particularly impacting vulnerable populations such as infants and those with untreated HIV. Given the diagnostic intricacies of TBM, there's a pressing need for rapid and reliable diagnostic tools. This review scrutinizes the efficacy of up-and-coming technologies like machine learning in transforming TBM diagnostics and management. Advanced diagnostic technologies like targeted gene sequencing, real-time polymerase chain reaction (RT-PCR), miRNA assays, and metagenomic next-generation sequencing (mNGS) offer promising avenues for early TBM detection. The capabilities of these technologies are further augmented when paired with mass spectrometry, metabolomics, and proteomics, enriching the pool of disease-specific biomarkers. Machine learning algorithms, adept at sifting through voluminous datasets like medical imaging, genomic profiles, and patient histories, are increasingly revealing nuanced disease pathways, thereby elevating diagnostic accuracy and guiding treatment strategies. While these burgeoning technologies offer hope for more precise TBM diagnosis, hurdles remain in terms of their clinical implementation. Future endeavors should zero in on the validation of these tools through prospective studies, critically evaluating their limitations, and outlining protocols for seamless incorporation into established healthcare frameworks. Through this review, we aim to present an exhaustive snapshot of emerging diagnostic modalities in TBM, the current standing of machine learning in meningitis diagnostics, and the challenges and future prospects of converging these domains.
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Affiliation(s)
- Yi Shi
- Department of Infectious Diseases, Wenzhou Central Hospital, Wenzhou, China
- The First School of Medicine, Wenzhou Medical University, Wenzhou, China
| | - Chengxi Zhang
- School of Materials Science and Engineering, Shandong Jianzhu University, Jinan, China
| | - Shuo Pan
- The First School of Medicine, Wenzhou Medical University, Wenzhou, China
| | - Yi Chen
- The First School of Medicine, Wenzhou Medical University, Wenzhou, China
| | - Xingguo Miao
- Department of Infectious Diseases, Wenzhou Central Hospital, Wenzhou, China
- Department of Infectious Diseases, Wenzhou Sixth People’s Hospital, Wenzhou, China
- Wenzhou Key Laboratory of Diagnosis and Treatment of Emerging and Recurrent Infectious Diseases, Wenzhou, China
| | - Guoqiang He
- Postgraduate Training Base Alliance of Wenzhou Medical University, Wenzhou, China
- Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou, China
| | - Yanchan Wu
- School of Electrical and Information Engineering, Quzhou University, Quzhou, China
| | - Hui Ye
- Department of Infectious Diseases, Wenzhou Central Hospital, Wenzhou, China
- Department of Infectious Diseases, Wenzhou Sixth People’s Hospital, Wenzhou, China
- Wenzhou Key Laboratory of Diagnosis and Treatment of Emerging and Recurrent Infectious Diseases, Wenzhou, China
| | - Chujun Weng
- The Fourth Affiliated Hospital Zhejiang University School of Medicine, Yiwu, China
| | - Huanhuan Zhang
- School and Hospital of Stomatology, Wenzhou Medical University, Wenzhou, China
| | - Wenya Zhou
- School and Hospital of Stomatology, Wenzhou Medical University, Wenzhou, China
| | - Xiaojie Yang
- Wenzhou Medical University Renji College, Wenzhou, China
| | - Chenglong Liang
- The First School of Medicine, Wenzhou Medical University, Wenzhou, China
| | - Dong Chen
- Wenzhou Key Laboratory of Diagnosis and Treatment of Emerging and Recurrent Infectious Diseases, Wenzhou, China
- Wenzhou Central Blood Station, Wenzhou, China
| | - Liang Hong
- Department of Infectious Diseases, The Third Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Feifei Su
- Department of Infectious Diseases, Wenzhou Central Hospital, Wenzhou, China
- Department of Infectious Diseases, Wenzhou Sixth People’s Hospital, Wenzhou, China
- Wenzhou Key Laboratory of Diagnosis and Treatment of Emerging and Recurrent Infectious Diseases, Wenzhou, China
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Moll-Bernardes R, Ferreira JR, Schaustz EB, Sousa AS, Mattos JD, Tortelly MB, Pimentel AL, Figueiredo ACBS, Noya-Rabelo MM, Fortier S, Matos E Silva FA, Vera N, Conde L, Cabral-Castro MJ, Albuquerque DC, Rosado-de-Castro PH, Camargo GC, Pinheiro MVT, Freitas DOL, Pittella AM, Araújo JAM, Marques AC, Gouvêa EP, Terzi FVO, Zukowski CN, Gismondi RAOC, Bandeira BS, Oliveira RS, Abufaiad BEJ, Miranda JSS, Miranda LG, Souza OF, Bozza FA, Luiz RR, Medei E. New Insights on the Mechanisms of Myocardial Injury in Hypertensive Patients With COVID-19. J Clin Immunol 2023; 43:1496-1505. [PMID: 37294518 PMCID: PMC10250847 DOI: 10.1007/s10875-023-01523-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Accepted: 05/22/2023] [Indexed: 06/10/2023]
Abstract
PURPOSE Myocardial injury is common in hypertensive patients with 2019 coronavirus disease (COVID-19). Immune dysregulation could be associated to cardiac injury in these patients, but the underlying mechanism has not been fully elucidated. METHODS All patients were selected prospectively from a multicenter registry of adults hospitalized with confirmed COVID-19. Cases had hypertension and myocardial injury, defined by troponin levels above the 99th percentile upper reference limit, and controls were hypertensive patients with no myocardial injury. Biomarkers and immune cell subsets were quantified and compared between the two groups. A multiple logistic regression model was used to analyze the associations of clinical and immune variables with myocardial injury. RESULTS The sample comprised 193 patients divided into two groups: 47 cases and 146 controls. Relative to controls, cases had lower total lymphocyte count, percentage of T lymphocytes, CD8+CD38+ mean fluorescence intensity (MFI), and percentage of CD8+ human leukocyte antigen DR isotope (HLA-DR)+ CD38-cells and higher percentage of natural killer lymphocytes, natural killer group 2A (NKG2A)+ MFI, percentage of CD8+CD38+cells, CD8+HLA-DR+MFI, CD8+NKG2A+MFI, and percentage of CD8+HLA-DR-CD38+cells. On multivariate regression, the CD8+HLA-DR+MFI, CD8+CD38+MFI, and total lymphocyte count were associated significantly with myocardial injury. CONCLUSION Our findings suggest that lymphopenia, CD8+CD38+MFI, and CD8+HLA-DR+MFI are immune biomarkers of myocardial injury in hypertensive patients with COVID-19. The immune signature described here may aid in understanding the mechanisms underlying myocardial injury in these patients. The study data might open a new window for improvement in the treatment of hypertensive patients with COVID-19 and myocardial injury.
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Affiliation(s)
- Renata Moll-Bernardes
- D'Or Institute for Research and Education, Rua Diniz Cordeiro, 30, 22281100, Rio de Janeiro, Brazil
| | - Juliana R Ferreira
- D'Or Institute for Research and Education, Rua Diniz Cordeiro, 30, 22281100, Rio de Janeiro, Brazil
- Cardiology and Internal Medicine Department, Rede D'Or São Luiz, Brazil
| | - Eduardo B Schaustz
- D'Or Institute for Research and Education, Rua Diniz Cordeiro, 30, 22281100, Rio de Janeiro, Brazil
| | - Andréa S Sousa
- D'Or Institute for Research and Education, Rua Diniz Cordeiro, 30, 22281100, Rio de Janeiro, Brazil
- Evandro Chagas National Institute of Infectious Disease, Oswaldo Cruz Foundation, Rio de Janeiro, Brazil
| | - João D Mattos
- D'Or Institute for Research and Education, Rua Diniz Cordeiro, 30, 22281100, Rio de Janeiro, Brazil
| | - Mariana B Tortelly
- D'Or Institute for Research and Education, Rua Diniz Cordeiro, 30, 22281100, Rio de Janeiro, Brazil
- Cardiology and Internal Medicine Department, Rede D'Or São Luiz, Brazil
| | - Adriana L Pimentel
- D'Or Institute for Research and Education, Rua Diniz Cordeiro, 30, 22281100, Rio de Janeiro, Brazil
- Cardiology and Internal Medicine Department, Rede D'Or São Luiz, Brazil
| | - Ana Cristina B S Figueiredo
- D'Or Institute for Research and Education, Rua Diniz Cordeiro, 30, 22281100, Rio de Janeiro, Brazil
- Cardiology and Internal Medicine Department, Rede D'Or São Luiz, Brazil
| | - Marcia M Noya-Rabelo
- D'Or Institute for Research and Education, Rua Diniz Cordeiro, 30, 22281100, Rio de Janeiro, Brazil
- Cardiology and Internal Medicine Department, Rede D'Or São Luiz, Brazil
- Bahia School of Medicine and Public Health, Bahia, Brazil
| | - Sergio Fortier
- D'Or Institute for Research and Education, Rua Diniz Cordeiro, 30, 22281100, Rio de Janeiro, Brazil
| | - Flavia A Matos E Silva
- D'Or Institute for Research and Education, Rua Diniz Cordeiro, 30, 22281100, Rio de Janeiro, Brazil
| | - Narendra Vera
- Institute of Biophysics Carlos Chagas Filho, Federal University of Rio de Janeiro (UFRJ), Rio de Janeiro, Brazil
| | - Luciana Conde
- Institute of Biophysics Carlos Chagas Filho, Federal University of Rio de Janeiro (UFRJ), Rio de Janeiro, Brazil
| | - Mauro Jorge Cabral-Castro
- Institute of Microbiology Paulo de Góes, UFRJ, Rio de Janeiro, Brazil
- Department of Pathology, Faculty of Medicine, Fluminense Federal University, Niterói, Rio de Janeiro, Brazil
| | - Denilson C Albuquerque
- D'Or Institute for Research and Education, Rua Diniz Cordeiro, 30, 22281100, Rio de Janeiro, Brazil
- Cardiology Department, Rio de Janeiro State University, Rio de Janeiro, Brazil
| | | | - Gabriel C Camargo
- D'Or Institute for Research and Education, Rua Diniz Cordeiro, 30, 22281100, Rio de Janeiro, Brazil
| | - Martha V T Pinheiro
- D'Or Institute for Research and Education, Rua Diniz Cordeiro, 30, 22281100, Rio de Janeiro, Brazil
| | - Daniele O L Freitas
- D'Or Institute for Research and Education, Rua Diniz Cordeiro, 30, 22281100, Rio de Janeiro, Brazil
| | - Ana M Pittella
- D'Or Institute for Research and Education, Rua Diniz Cordeiro, 30, 22281100, Rio de Janeiro, Brazil
| | - José Afonso M Araújo
- D'Or Institute for Research and Education, Rua Diniz Cordeiro, 30, 22281100, Rio de Janeiro, Brazil
- Cardiology and Internal Medicine Department, Rede D'Or São Luiz, Brazil
| | - André C Marques
- D'Or Institute for Research and Education, Rua Diniz Cordeiro, 30, 22281100, Rio de Janeiro, Brazil
- Cardiology and Internal Medicine Department, Rede D'Or São Luiz, Brazil
| | - Elias P Gouvêa
- D'Or Institute for Research and Education, Rua Diniz Cordeiro, 30, 22281100, Rio de Janeiro, Brazil
- Cardiology and Internal Medicine Department, Rede D'Or São Luiz, Brazil
| | - Flavia V O Terzi
- D'Or Institute for Research and Education, Rua Diniz Cordeiro, 30, 22281100, Rio de Janeiro, Brazil
- Cardiology and Internal Medicine Department, Rede D'Or São Luiz, Brazil
| | - Cleverson N Zukowski
- D'Or Institute for Research and Education, Rua Diniz Cordeiro, 30, 22281100, Rio de Janeiro, Brazil
- Cardiology and Internal Medicine Department, Rede D'Or São Luiz, Brazil
| | - Ronaldo A O C Gismondi
- D'Or Institute for Research and Education, Rua Diniz Cordeiro, 30, 22281100, Rio de Janeiro, Brazil
- Cardiology and Internal Medicine Department, Rede D'Or São Luiz, Brazil
| | - Bruno S Bandeira
- D'Or Institute for Research and Education, Rua Diniz Cordeiro, 30, 22281100, Rio de Janeiro, Brazil
- Cardiology and Internal Medicine Department, Rede D'Or São Luiz, Brazil
| | - Renée S Oliveira
- D'Or Institute for Research and Education, Rua Diniz Cordeiro, 30, 22281100, Rio de Janeiro, Brazil
- Cardiology and Internal Medicine Department, Rede D'Or São Luiz, Brazil
- Internal Medicine Department, Federal University of the State of Rio de Janeiro, Rio de Janeiro, Brazil
| | - Barbara E J Abufaiad
- D'Or Institute for Research and Education, Rua Diniz Cordeiro, 30, 22281100, Rio de Janeiro, Brazil
- Cardiology and Internal Medicine Department, Rede D'Or São Luiz, Brazil
| | - Jacqueline S S Miranda
- D'Or Institute for Research and Education, Rua Diniz Cordeiro, 30, 22281100, Rio de Janeiro, Brazil
- Cardiology and Internal Medicine Department, Rede D'Or São Luiz, Brazil
| | - Luiz Guilherme Miranda
- D'Or Institute for Research and Education, Rua Diniz Cordeiro, 30, 22281100, Rio de Janeiro, Brazil
- Cardiology and Internal Medicine Department, Rede D'Or São Luiz, Brazil
| | - Olga F Souza
- D'Or Institute for Research and Education, Rua Diniz Cordeiro, 30, 22281100, Rio de Janeiro, Brazil
- Cardiology and Internal Medicine Department, Rede D'Or São Luiz, Brazil
| | - Fernando A Bozza
- D'Or Institute for Research and Education, Rua Diniz Cordeiro, 30, 22281100, Rio de Janeiro, Brazil
- Evandro Chagas National Institute of Infectious Disease, Oswaldo Cruz Foundation, Rio de Janeiro, Brazil
| | - Ronir R Luiz
- D'Or Institute for Research and Education, Rua Diniz Cordeiro, 30, 22281100, Rio de Janeiro, Brazil
- Institute for Studies in Public Health-IESC, UFRJ, Rio de Janeiro, Brazil
| | - Emiliano Medei
- D'Or Institute for Research and Education, Rua Diniz Cordeiro, 30, 22281100, Rio de Janeiro, Brazil.
- Institute of Biophysics Carlos Chagas Filho, Federal University of Rio de Janeiro (UFRJ), Rio de Janeiro, Brazil.
- National Center for Structural Biology and Bioimaging, UFRJ, Rio de Janeiro, Brazil.
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Cheng FC, Tang LH, Lee KJ, Wei YF, Liu BL, Chen MH, Chiang CP. Online courses for dentist continuing education: A new trend after the COVID-19 pandemic. J Dent Sci 2023; 18:1812-1821. [PMID: 37795131 PMCID: PMC10307533 DOI: 10.1016/j.jds.2023.06.020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2023] [Indexed: 10/06/2023] Open
Abstract
Background/purpose Online courses have been widely used in all levels of education during the COVID-19 pandemic. This study explored the effectiveness of a dentist continuing education (DCE) course through the online devices in Taiwan. Materials and methods The practicing dentists who participated in the online course of dental radiation technology for DCE offered by the Taiwan Dental Association (TWDA) in October 2022 and in March 2023 were enrolled in this study. The composition of participating dentists was confirmed by the public inquiry system and their learning effectiveness was evaluated by a questionnaire-based survey after the online DCE class. Results All participating dentists (132 in October 2022 and 117 in March 2023) obtained consistent good learning outcomes in this online DCE course. Of these 249 dentists, there were 170 (68.27%) males and 79 (31.73%) females, 127 (51.00%) dental specialists and 122 (49.00%) general dentists, as well as 50 (20.08%) hospital dentists and 199 (79.92%) clinic dentists. The participation rates for this course of practicing dentists in non-municipalities (4.70%), counties (3.88%), eastern region (8.08%), and outlying islands (3.60%) were much higher than those in municipalities (0.79%), cities (1.16%), and the western region including the northern region (0.88%), central region (1.96%), and southern region (1.94%), respectively. Conclusion The participating dentists express positive feedback on the online DCE courses, and the online DCE courses can reduce the urban-rural gap in dental education resources. The use of online DCE courses in dental education will be a future trend.
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Affiliation(s)
- Feng-Chou Cheng
- Chia-Te Dental Clinic, New Taipei City, Taiwan
- School of Life Science, National Taiwan Normal University, Taipei, Taiwan
- Science Education Center, National Taiwan Normal University, Taipei, Taiwan
| | - Li-Hua Tang
- Department of Nuclear Medicine, National Taiwan University Hospital, College of Medicine, National Taiwan University, Taipei, Taiwan
| | - Kou-Jung Lee
- Hong-Yuan Dental Clinic, Chiayi County, Taiwan
- Dental Radiation Safety Committee, Taiwan Dental Association, Taipei, Taiwan
| | - Yuh-Fen Wei
- Department of Medical Imaging, National Taiwan University Hospital Hsin-Chu Branch, Hsinchu County, Taiwan
| | - Bo-Lin Liu
- Department of Medical Imaging, National Taiwan University Hospital, College of Medicine, National Taiwan University, Taipei, Taiwan
| | - Mu-Hsiung Chen
- Department of Dentistry, National Taiwan University Hospital, College of Medicine, National Taiwan University, Taipei, Taiwan
| | - Chun-Pin Chiang
- Department of Dentistry, National Taiwan University Hospital, College of Medicine, National Taiwan University, Taipei, Taiwan
- Graduate Institute of Clinical Dentistry, School of Dentistry, National Taiwan University, Taipei, Taiwan
- Graduate Institute of Oral Biology, School of Dentistry, National Taiwan University, Taipei, Taiwan
- Department of Dentistry, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Hualien, Taiwan
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Mohammedain SA, Badran S, Elzouki AY, Salim H, Chalaby A, Siddiqui MYA, Hussein YY, Rahim HA, Thalib L, Alam MF, Al-Badriyeh D, Al-Maadeed S, Doi SAR. Validation of a risk prediction model for COVID-19: the PERIL prospective cohort study. Future Virol 2023:10.2217/fvl-2023-0036. [PMID: 37970094 PMCID: PMC10630949 DOI: 10.2217/fvl-2023-0036] [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: 02/14/2023] [Accepted: 10/03/2023] [Indexed: 11/17/2023]
Abstract
Aim: This study aims to perform an external validation of a recently developed prognostic model for early prediction of the risk of progression to severe COVID-19. Patients & methods/materials: Patients were recruited at their initial diagnosis at two facilities within Hamad Medical Corporation in Qatar. 356 adults were included for analysis. Predictors for progression of COVID-19 were all measured at disease onset and first contact with the health system. Results: The C statistic was 83% (95% CI: 78%-87%) and the calibration plot showed that the model was well-calibrated. Conclusion: The published prognostic model for the progression of COVID-19 infection showed satisfactory discrimination and calibration and the model is easy to apply in clinical practice.d.
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Affiliation(s)
- Shahd A Mohammedain
- Department of Population Medicine, College of Medicine, QU Health, Qatar University, Doha, Qatar
| | - Saif Badran
- Department of Population Medicine, College of Medicine, QU Health, Qatar University, Doha, Qatar
- Department of Plastic Surgery, Hamad Medical Corporation, Doha, Qatar
| | - AbdelNaser Y Elzouki
- Department of Internal Medicine Hamad General Hospital Hamad Medical Corporation, Doha, Qatar
| | - Halla Salim
- Department of Internal Medicine Hamad General Hospital Hamad Medical Corporation, Doha, Qatar
| | - Ayesha Chalaby
- Department of Internal Medicine Hamad General Hospital Hamad Medical Corporation, Doha, Qatar
| | - MYA Siddiqui
- Department of Internal Medicine Hamad General Hospital Hamad Medical Corporation, Doha, Qatar
| | - Yehia Y Hussein
- Department of Population Medicine, College of Medicine, QU Health, Qatar University, Doha, Qatar
| | - Hanan Abdul Rahim
- Department of Public Health, College of Health Sciences, QU Health, Qatar University, Doha, Qatar
| | - Lukman Thalib
- Department of Biostatistics, Faculty of Medicine, Istanbul Aydin University, Istanbul, Turkey
| | - Mohammed Fasihul Alam
- Department of Public Health, College of Health Sciences, QU Health, Qatar University, Doha, Qatar
| | | | - Sumaya Al-Maadeed
- Department of Computer Science, College of Engineering, Qatar University, Doha, Qatar
| | - Suhail AR Doi
- Department of Population Medicine, College of Medicine, QU Health, Qatar University, Doha, Qatar
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Lin M, Lin N, Yu S, Sha Y, Zeng Y, Liu A, Niu Y. Automated Prediction of Early Recurrence in Advanced Sinonasal Squamous Cell Carcinoma With Deep Learning and Multi-parametric MRI-based Radiomics Nomogram. Acad Radiol 2023; 30:2201-2211. [PMID: 36925335 DOI: 10.1016/j.acra.2022.11.013] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Revised: 11/12/2022] [Accepted: 11/13/2022] [Indexed: 03/16/2023]
Abstract
RATIONALE AND OBJECTIVES Preoperative prediction of the recurrence risk in patients with advanced sinonasal squamous cell carcinoma (SNSCC) is critical for individualized treatment. To evaluate the predictive ability of radiomics signature (RS) based on deep learning and multiparametric MRI for the risk of 2-year recurrence in advanced SNSCC. MATERIALS AND METHODS Preoperative MRI datasets were retrospectively collected from 265 SNSCC patients (145 recurrences) who underwent preoperative MRI, including T2-weighted (T2W), contrast-enhanced T1-weighted (T1c) sequences and diffusion-weighted (DW). All patients were divided into 165 training cohort and 70 test cohort. A deep learning segmentation model based on VB-Net was used to segment regions of interest (ROIs) for preoperative MRI and radiomics features were extracted from automatically segmented ROIs. Least absolute shrinkage and selection operator (LASSO) and logistic regression (LR) were applied for feature selection and radiomics score construction. Combined with meaningful clinicopathological predictors, a nomogram was developed and its performance was evaluated. In addition, X-title software was used to divide patients into high-risk or low-risk early relapse (ER) subgroups. Recurrence-free survival probability (RFS) was assessed for each subgroup. RESULTS The radiomics score, T stage, histological grade and Ki-67 predictors were independent predictors. The segmentation models of T2WI, T1c, and apparent diffusion coefficient (ADC) sequences achieved Dice coefficients of 0.720, 0.727, and 0.756, respectively, in the test cohort. RS-T2, RS-T1c and RS-ADC were derived from single-parameter MRI. RS-Combined (combined with T2WI, T1c, and ADC features) was derived from multiparametric MRI and reached area under curve (AUC) and accuracy of 0.854 (0.749-0.927) and 74.3% (0.624-0.840), respectively, in the test cohort. The calibration curve and decision curve analysis (DCA) illustrate its value in clinical practice. Kaplan-Meier analysis showed that the 2-year RFS rate for low-risk patients was significantly greater than that for high-risk patients in both the training and testing cohorts (p < 0.001). CONCLUSION Automated nomograms based on multi-sequence MRI help to predict ER in SNSCC patients preoperatively.
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Affiliation(s)
- Mengyan Lin
- Shanghai Institute of Medical Imaging, Shanghai, China
| | - Naier Lin
- Department of Radiology, Eye & ENT Hospital, Fudan University, Shanghai, China
| | - Sihui Yu
- Department of Radiology, Eye & ENT Hospital, Fudan University, Shanghai, China
| | - Yan Sha
- Department of Radiology, Eye & ENT Hospital, Fudan University, Shanghai, China.
| | - Yan Zeng
- Department of Research Center, Shanghai United Imaging Intelligence Inc., Shanghai, China
| | - Aie Liu
- Department of Research Center, Shanghai United Imaging Intelligence Inc., Shanghai, China
| | - Yue Niu
- Department of Radiology, Eye & ENT Hospital, Fudan University, Shanghai, China
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Zhao MR, Ma WJ, Song XC, Li ZJ, Shao ZZ, Lu H, Zhao R, Guo YJ, Ye ZX, Liu PF. Feasibility analysis of magnetic resonance imaging-based radiomics features for preoperative prediction of nuclear grading of ductal carcinoma in situ. Gland Surg 2023; 12:1209-1223. [PMID: 37842532 PMCID: PMC10570967 DOI: 10.21037/gs-23-132] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Accepted: 08/27/2023] [Indexed: 10/17/2023]
Abstract
Background The nuclear grading of ductal carcinoma in situ (DCIS) affects its clinical risk. The aim of this study was to investigate the possibility of predicting the nuclear grading of DCIS, by magnetic resonance imaging (MRI)-based radiomics features. And to develop a nomogram combining radiomics features and MRI semantic features to explore the potential role of MRI radiomic features in the assessment of DCIS nuclear grading. Methods A total of 156 patients (159 lesions) with DCIS and DCIS with microinvasive (DCIS-MI) were enrolled in this retrospective study, with 112 lesions included in the training cohort and 47 lesions included in the validation cohort. Radiomics features were extracted from Dynamic contrast-enhanced MRI (DCE-MRI) phases 1st and 5th. After feature selection, radiomics signature was constructed and radiomics score (Rad-score) was calculated. Multivariate analysis was used to identify MRI semantic features that were significantly associated with DCIS nuclear grading and combined with Rad-score to construct a Nomogram. Receiver operating characteristic curves were used to evaluate the predictive performance of Rad-score and Nomogram, and decision curve analysis (DCA) was used to evaluate the clinical utility. Results In multivariate analyses of MRI semantic features, larger tumor size and heterogeneous enhancement pattern were significantly associated with high-nuclear grade DCIS (HNG DCIS). In the training cohort, Nomogram had an area under curve (AUC) of 0.879 and Rad-score had an AUC of 0.828. Similarly, in the independent validation cohort, Nomogram had an AUC value of 0.828 and Rad-score had an AUC of 0.772. In both the training and validation cohorts, Nomogram had a significantly higher AUC value than Rad-score (P<0.05). DCA confirmed that Nomogram had a higher net clinical benefit. Conclusions MRI-based radiomic features can be used as potential biomarkers for assessing nuclear grading of DCIS. The nomogram constructed by radiomic features combined with semantic features is feasible in discriminating non-HNG and HNG DCIS.
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Affiliation(s)
- Meng-Ran Zhao
- Department of Radiology, Tianjin Medical University Cancer Institute & Hospital, National Clinical Research Center for Cancer, Tianjin’s Clinical Research Center for Cancer, Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
| | - Wen-Juan Ma
- Department of Breast Imaging, Tianjin Medical University Cancer Institute & Hospital, National Clinical Research Center for Cancer, Tianjin’s Clinical Research Center for Cancer, Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
| | - Xiang-Chao Song
- Department of Radiology, Tianjin Medical University Cancer Institute & Hospital, National Clinical Research Center for Cancer, Tianjin’s Clinical Research Center for Cancer, Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
| | - Zhi-Jun Li
- Department of Radiology, Tianjin Medical University Cancer Institute & Hospital, National Clinical Research Center for Cancer, Tianjin’s Clinical Research Center for Cancer, Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
| | - Zhen-Zhen Shao
- Department of Breast Imaging, Tianjin Medical University Cancer Institute & Hospital, National Clinical Research Center for Cancer, Tianjin’s Clinical Research Center for Cancer, Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
| | - Hong Lu
- Department of Breast Imaging, Tianjin Medical University Cancer Institute & Hospital, National Clinical Research Center for Cancer, Tianjin’s Clinical Research Center for Cancer, Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
| | - Rui Zhao
- Department of Breast Imaging, Tianjin Medical University Cancer Institute & Hospital, National Clinical Research Center for Cancer, Tianjin’s Clinical Research Center for Cancer, Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
| | - Yi-Jun Guo
- Department of Breast Imaging, Tianjin Medical University Cancer Institute & Hospital, National Clinical Research Center for Cancer, Tianjin’s Clinical Research Center for Cancer, Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
| | - Zhao-Xiang Ye
- Department of Radiology, Tianjin Medical University Cancer Institute & Hospital, National Clinical Research Center for Cancer, Tianjin’s Clinical Research Center for Cancer, Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
| | - Pei-Fang Liu
- Department of Breast Imaging, Tianjin Medical University Cancer Institute & Hospital, National Clinical Research Center for Cancer, Tianjin’s Clinical Research Center for Cancer, Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
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Li X, Zhang C, Li T, Lin X, Wu D, Yang G, Cao D. Early acquired resistance to EGFR-TKIs in lung adenocarcinomas before radiographic advanced identified by CT radiomic delta model based on two central studies. Sci Rep 2023; 13:15586. [PMID: 37730961 PMCID: PMC10511693 DOI: 10.1038/s41598-023-42916-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Accepted: 09/16/2023] [Indexed: 09/22/2023] Open
Abstract
Early acquired resistance (EAR) to epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs) in lung adenocarcinomas before radiographic advance cannot be perceived by the naked eye. This study aimed to discover and validate a CT radiomic model to precisely identify the EAR. Training cohort (n = 67) and internal test cohort (n = 29) were from the First Affiliated Hospital of Fujian Medical University, and external test cohort (n = 29) was from the Second Affiliated Hospital of Xiamen Medical College. Follow-up CT images at three different times of each patient were collected: (1) baseline images before EGFR-TKIs therapy; (2) first follow-up images after EGFR-TKIs therapy (FFT); (3) EAR images, which were the last follow-up images before radiographic advance. The features extracted from FFT and EAR were used to construct the classic radiomic model. The delta features which were calculated by subtracting the baseline from either FFT or EAR were used to construct the delta radiomic model. The classic radiomic model achieved AUC 0.682 and 0.641 in training and internal test cohorts, respectively. The delta radiomic model achieved AUC 0.730 and 0.704 in training and internal test cohorts, respectively. Over the external test cohort, the delta radiomic model achieved AUC 0.661. The decision curve analysis showed that when threshold of the probability of the EAR to the EGFR-TKIs was between 0.3 and 0.82, the proposed model was more benefit than treating all patients. Based on two central studies, the delta radiomic model derived from the follow-up non-enhanced CT images can help clinicians to identify the EAR to EGFR-TKIs in lung adenocarcinomas before radiographic advance and optimize clinical outcomes.
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Affiliation(s)
- Xiumei Li
- Department of Radiology, The First Affiliated Hospital of Fujian Medical University, 20 Cha-Zhong Road, Fuzhou, 350005, Fujian, China
| | - Chengxiu Zhang
- Shanghai Key Laboratory of Magnetic Resonance, School of Physics and Electronic Science, East China Normal University, 3663 North Zhangshan Road, Shanghai, 200062, China
| | - Tingting Li
- Department of Radiology, The Second Affiliated Hospital of Xiamen Medical College, Xiamen, 361021, Fujian, China
| | - Xiuqiang Lin
- Department of Radiology, The First Affiliated Hospital of Fujian Medical University, 20 Cha-Zhong Road, Fuzhou, 350005, Fujian, China
| | - Dongmei Wu
- Shanghai Key Laboratory of Magnetic Resonance, School of Physics and Electronic Science, East China Normal University, 3663 North Zhangshan Road, Shanghai, 200062, China
| | - Guang Yang
- Shanghai Key Laboratory of Magnetic Resonance, School of Physics and Electronic Science, East China Normal University, 3663 North Zhangshan Road, Shanghai, 200062, China.
| | - Dairong Cao
- Department of Radiology, The First Affiliated Hospital of Fujian Medical University, 20 Cha-Zhong Road, Fuzhou, 350005, Fujian, China.
- Department of Radiology, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, 350212, Fujian, China.
- Fujian Key Laboratory of Precision Medicine for Cancer, The First Affiliated Hospital, Fujian Medical University, Fuzhou, 350005, Fujian, China.
- Key Laboratory of Radiation Biology of Fujian Higher Education Institutions, The First Affiliated Hospital, Fujian Medical University, Shanghai, 200062, China.
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