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Liu Q, Lun L, Meng S, Wang Z, Qu Y, Huang X, Chen X, Wang J, Zhang J, Wang K, Wu R, Zhang Y, Yi J, Luo J. Feasibility of Omitting Contralateral Neck Irradiation in Patients with Node-Negative Sinonasal Squamous Cell Carcinoma Crossing the Midline. Int J Radiat Oncol Biol Phys 2023; 117:e600. [PMID: 37785813 DOI: 10.1016/j.ijrobp.2023.06.1961] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/04/2023]
Abstract
PURPOSE/OBJECTIVE(S) This study aims to analyze the nodal target volume in patients with node-negative SNSCC crossing the midline. MATERIALS/METHODS One hundred and four patients with node-negative advanced sinonasal squamous cell carcinoma (SNSCC) crossing the midline were included. Survival rates were estimated and compared between treatment groups. RESULTS Sixty-four patients received contralateral ENI (contralateral ENI group), while forty patients did not (non-contralateral ENI group). The median follow-up time was 89.99 and 95.01 months in the contralateral and non-contralateral ENI groups, respectively. At 5 years, the regional relapse-free survival and contralateral regional relapse-free survival were 57.68% vs. 55.83% (p = 0.372), and 57.68% vs. 61.62% (p = 0.541), in contralateral ENI group vs. non-contralateral ENI group, respectively. Five-year overall survival, local relapse-free survival, and distant metastasis-free survival were similar in the two groups (all p > 0.05). CONCLUSION In patients with node-negative SNSCC crossing the midline, omission of contralateral ENI did not affect regional control and survival outcomes on the premise of receiving ipsilateral ENI covering at least levels Ib and II.
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Affiliation(s)
- Q Liu
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - L Lun
- Department of Head and Neck Radiotherapy, Harbin Medical University Cancer Hospital, Harbin, China
| | - S Meng
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Z Wang
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Y Qu
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - X Huang
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - X Chen
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - J Wang
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - J Zhang
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - K Wang
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - R Wu
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Y Zhang
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - J Yi
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - J Luo
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
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Lin L, Xie B, Shi J, Zhou CM, Yi J, Chen J, He JX, Wei HL. [IL-8 Links NF-κB and Wnt/β-Catenin Pathways in Persistent Inflammatory Response Induced by Chronic Helicobacter pylori Infection]. Mol Biol (Mosk) 2023; 57:713-716. [PMID: 37528793 DOI: 10.31857/s0026898423040134, edn: qlukej] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Accepted: 02/03/2023] [Indexed: 08/03/2023]
Abstract
Helicobacter pylori (H. pylori) infection can cause persistent inflammatory response in human gastric mucosal epithelial cells, which may result in the occurrence of cancer. However, the underlying mechanism of carcinogenesis has not been elucidated yet. Herein, we established the models of chronic H. pylori infection in GES-1 cells and C57BL/6J mice. Interleukin 8 (IL-8) level was detected by ELISA. The expression of NF-κB p65, IL-8, Wnt2 and β-catenin mRNA and proteins was evaluated by real-time PCR, Western blotting, immunofluorescence staining, and immunohistochemistry. The infection of H. pylori in mice was evaluated by rapid urease test, H&E staining and Warthin-Starry silver staining. The morphological changes of gastric mucosa were observed by electron microscopy. Our results showed that in H. pylori infected gastric mucosal cells along with activation of NF-κB signaling pathway and increase of IL-8 level, the expression of Wnt2 was also increased significantly, which preliminarily indicates that IL-8 can positively regulate the expression of Wnt2. Studies in chronic H. pylori infected C57BL/6J mice models showed that there was an increased incidence of premalignant lesions in the gastric mucosa tissue. Through comparing changes of gastric mucosal cell ultrastructure and analyzing the relationship between NF-κB signaling pathway and Wnt2 expression, we found that H. pylori infection activated NF-κB signal pathways, and the massive release of IL-8 was positively correlated with the high expression of Wnt2 protein. Subsequently, the activated Wnt/β-catenin signal pathways may be involved in the malignant transformation of gastric mucosal cells. Collectively, H. pylori chronic infection may continuously lead to persistent inflammatory response: activate NF-κB pathway, promote IL-8 release and thereby activate Wnt/β-catenin pathway. IL-8 probably plays an important role of a linker in coupling these two signal pathways.
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Affiliation(s)
- L Lin
- Department of Hematology and Oncology, Gansu Provincial Maternity and Child-Care Hospital, Lanzhou, Gansu, 730050 China
| | - B Xie
- Key Laboratory of Preclinical Study for New Drugs of Gansu Province, School of Basic Medical Sciences, Lanzhou University, Lanzhou, Gansu, 730000 China
| | - J Shi
- Department of Blood Transfusion, The Second Hospital of Lanzhou University, Lanzhou, Gansu, 730000 China
| | - C M Zhou
- Department of Clinical Laboratory Center, The First Hospital of Lanzhou University, Lanzhou, Gansu, 730000 China
| | - J Yi
- Key Laboratory of Preclinical Study for New Drugs of Gansu Province, School of Basic Medical Sciences, Lanzhou University, Lanzhou, Gansu, 730000 China
| | - J Chen
- Key Laboratory of Preclinical Study for New Drugs of Gansu Province, School of Basic Medical Sciences, Lanzhou University, Lanzhou, Gansu, 730000 China
| | - J X He
- Basic Medical College, Gansu University of Chinese Medicine, Lanzhou, Gansu, 730000 China
| | - H L Wei
- Key Laboratory of Preclinical Study for New Drugs of Gansu Province, School of Basic Medical Sciences, Lanzhou University, Lanzhou, Gansu, 730000 China
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Liu H, Han D, Mao Y, Vonder M, Heuvelmans M, Yi J, Ye Z, De Koning H, Oudkerk M. 108P Optimization of automatic emphysema detection in lung cancer screening dataset. J Thorac Oncol 2023. [DOI: 10.1016/s1556-0864(23)00363-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/03/2023]
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4
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Lancaster H, Heuvelmans M, Yu D, Yi J, de Bock G, Oudkerk M. 106P AI negative predictive performance exceeds that of radiologists in volumetric-based risk stratification of lung nodules detected at baseline in a lung cancer screening population. J Thorac Oncol 2023. [DOI: 10.1016/s1556-0864(23)00361-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/03/2023]
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Mao Y, Lancaster H, Jiang B, Han D, Vonder M, Dorrius M, Yu D, Yi J, de Bock G, Oudkerk M. 107P Artificial intelligence-based volumetric classification of pulmonary nodules in Chinese baseline lung cancer screening population (NELCIN-B3). J Thorac Oncol 2023. [DOI: 10.1016/s1556-0864(23)00362-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/03/2023]
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6
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Xu Y, Zhang Y, Yi J. A Radiomics-Based Nomogram for the Prediction of Occult Lymph Node Metastasis in cN0 Supraglottic Carcinoma. Int J Radiat Oncol Biol Phys 2022. [DOI: 10.1016/j.ijrobp.2022.07.1308] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Griebel L, Whitmyre N, Buckner-Petty S, Yi J. 8881 Outcomes of Single Port Robotic Sacrocolpopexy Compared with Traditional Multi-Port Approaches. J Minim Invasive Gynecol 2022. [DOI: 10.1016/j.jmig.2022.09.502] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Bagaria M, Yi J. Robotic Nerve Sparing Uterosacral Ligament Suspension. J Minim Invasive Gynecol 2022. [DOI: 10.1016/j.jmig.2022.09.233] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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Wenbin Y, Liu T, He M, Yi J, Tang L, Ou X, Hu C. 226MO Is induction chemotherapy beneficial in locally recurrent nasopharyngeal carcinoma before re-irradiation? A multicenter retrospective analysis. Ann Oncol 2022. [DOI: 10.1016/j.annonc.2022.10.261] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/07/2022] Open
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Brito L, Abrao M, Dune T, Yi J. 8097 How Do You Do It? a Survey on the Preferences of Surgeons Regarding Uterosacral Ligament Suspension (USLS) Technique. J Minim Invasive Gynecol 2022. [DOI: 10.1016/j.jmig.2022.09.367] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Bagaria M, Magtibay P, Yi J. Robotic Trachelectomy: Surgical Techniques and Principles. J Minim Invasive Gynecol 2022. [DOI: 10.1016/j.jmig.2022.09.277] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Zhang Y, Zhao Y, Yi J, Tian P. Clinical Efficacy on Severe Acute Radiation Dermatitis Treated by Topical Compound Danxiong Granules in Patients Receiving Radiation: A Prospective Randomized Trial. Int J Radiat Oncol Biol Phys 2022. [DOI: 10.1016/j.ijrobp.2022.07.362] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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Ma Y, Xiao J, Zhang Y, Qingfeng L, Zhang H, Tian Y, Xu Y, Bi N, Chen X, Wang W, Wang K, Huang X, Zhao R, Yang S, Yi J, LI Y. Hypofractionated Stereotactic Radiotherapy with or without Whole Brain Radiotherapy with Helical Tomotherapy for Multiple Brain Metastases – Long-Term Follow-Up Results of a Phase II Trial. Int J Radiat Oncol Biol Phys 2022. [DOI: 10.1016/j.ijrobp.2022.07.802] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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Liu Q, Qu Y, Wang K, Wu R, Zhang Y, Huang X, Chen X, Wang J, Zhang S, Zhang J, Xiao J, Yi J, Xu G, Luo J. Lymph Node Metastasis Spread Patterns and the Effectiveness of Prophylactic Neck Irradiation in Sinonasal Squamous Cell Carcinoma (SNSCC). Int J Radiat Oncol Biol Phys 2022. [DOI: 10.1016/j.ijrobp.2022.07.1342] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Liu J, Yi J, Chen A. Double-inputs Illumination Pattern Recognizing Model with Automatic Shadow Detection Network in a Single Face Image. INT J ARTIF INTELL T 2022. [DOI: 10.1142/s0218213023500100] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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McKean M, Barve M, Hong D, Parikh A, Rosen E, Yang J, Picard R, Yi J, Brail L, Vecchio D, Meniawy T, John T, Wang J. Preliminary results from FLAGSHP-1: A Phase I dose escalation study of ERAS-601, a potent SHP2 inhibitor, in patients with previously treated advanced or metastatic solid tumors. Eur J Cancer 2022. [DOI: 10.1016/s0959-8049(22)00890-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
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Feng LZ, Jiang HY, Yi J, Qian LL, Xu JD, Zheng LB, Ma ZB, Peng SJ, Jiang ST, Xu EF, Chen LH, Wang LD, Gao WZ, Yang W. [Introduction and implications of WHO position paper: vaccines against influenza, May 2022]. Zhonghua Yi Xue Za Zhi 2022; 102:2315-2318. [PMID: 35970790 DOI: 10.3760/cma.j.cn112137-20220518-01090] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
On May 13, 2022, World Health Organization(WHO) Position Paper on Influenza Vaccine (2022 edition) was published. This position paper updates information on influenza epidemiology, high risk population, the impact of immunization on disease, influenza vaccines and effectiveness and safety, and propose WHO's position and recommendation that all countries should consider implementing seasonal influenza vaccine immunization programmes to prepare for an influenza pandemic. In addition, it proposes that the influenza surveillance platform can be integrated with the surveillance of other respiratory viruses, such as SARS-CoV-2 and Respiratory Syncytial Virus. This position paper has some implications for the prevention and control of influenza and other respiratory infectious diseases in China: (1) Optimize influenza vaccine policies to facilitate the implementation of immunization services; (2) Influenza prevention and control should from the perspective of Population Medicine focus on the individual and community to integrate with "Promotion, Prevention, Diagnosis, Control, Treatment, Rehabilitation"; (3) Incorporate prevention and control of other respiratory infectious diseases such as influenza, COVID-19, respiratory syncytial virus and adenovirus, and intelligently monitor by integrating multi-channel data to achieve the goal of co-prevention and control of multiple diseases.
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Affiliation(s)
- L Z Feng
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, China
| | - H Y Jiang
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, China
| | - J Yi
- Chinese Prevention Medicine Association, Beijing 100021, China
| | - L L Qian
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, China
| | - J D Xu
- Institute for Non-communicable Disease Control and Prevention, Qinghai Center for Disease Control and Prevention, Xining 810001, China
| | - L B Zheng
- Division of Infectious Diseases, Chinese Center for Disease Control and Prevention, Beijing 102206, China
| | - Z B Ma
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, China
| | - S J Peng
- Division of Infectious Diseases, Chinese Center for Disease Control and Prevention, Beijing 102206, China
| | - S T Jiang
- Department of Immunization Planning, Nanshan District Center for Disease Control and Prevention, Shenzhen 518055, China
| | - E F Xu
- National Institute for Viral Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing 102206, China
| | - L H Chen
- Zhejiang Center for Disease Control and Prevention, Hangzhou 310051, China
| | - L D Wang
- Beijing Ditan Hospital, Capital Medical University, Beijing 100015, China
| | - W Z Gao
- Hunan Center for Disease Control and Prevention, Changsha 410005, China
| | - Weizhong Yang
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, China
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Choi H, Kim H, Jin KN, Jeong YJ, Chae KJ, Lee KH, Yong HS, Gil B, Lee HJ, Lee KY, Jeon KN, Yi J, Seo S, Ahn C, Lee J, Oh K, Goo JM. A Challenge for Emphysema Quantification Using a Deep Learning Algorithm With Low-dose Chest Computed Tomography. J Thorac Imaging 2022; 37:253-261. [PMID: 35749623 DOI: 10.1097/rti.0000000000000647] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
PURPOSE We aimed to identify clinically relevant deep learning algorithms for emphysema quantification using low-dose chest computed tomography (LDCT) through an invitation-based competition. MATERIALS AND METHODS The Korean Society of Imaging Informatics in Medicine (KSIIM) organized a challenge for emphysema quantification between November 24, 2020 and January 26, 2021. Seven invited research teams participated in this challenge. In total, 558 pairs of computed tomography (CT) scans (468 pairs for the training set, and 90 pairs for the test set) from 9 hospitals were collected retrospectively or prospectively. CT acquisition followed the hospitals' protocols to reflect the real-world clinical setting. Using the training set, each team developed an algorithm that generated converted LDCT by changing the pixel values of LDCT to simulate those of standard-dose CT (SDCT). The agreement between SDCT and LDCT was evaluated using the intraclass correlation coefficient (ICC; 2-way random effects, absolute agreement, and single rater) for the percentage of low-attenuated area below -950 HU (LAA-950 HU), κ value for emphysema categorization (LAA-950 HU, <5%, 5% to 10%, and ≥10%) and cosine similarity of LAA-950 HU. RESULTS The mean LAA-950 HU of the test set was 14.2%±10.5% for SDCT, 25.4%±10.2% for unconverted LDCT, and 12.9%±10.4%, 11.7%±10.8%, and 12.4%±10.5% for converted LDCT (top 3 teams). The agreement between the SDCT and converted LDCT of the first-place team was 0.94 (95% confidence interval: 0.90, 0.97) for ICC, 0.71 (95% confidence interval: 0.58, 0.84) for categorical agreement, and 0.97 (interquartile range: 0.94 to 0.99) for cosine similarity. CONCLUSIONS Emphysema quantification with LDCT was feasible through deep learning-based CT conversion strategies.
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Affiliation(s)
- Hyewon Choi
- Department of Radiology, Chung-Ang University Hospital, Chung-Ang University College of Medicine
| | - Hyungjin Kim
- Department of Radiology, Seoul National University College of Medicine
| | - Kwang Nam Jin
- Department of Radiology, Seoul Metropolitan Government-Seoul National University Boramae Medical Center, Seoul
| | - Yeon Joo Jeong
- Department of Radiology and Biomedical Research Institute, Pusan National University Hospital, Busan
| | - Kum Ju Chae
- Department of Radiology, Research Institute of Clinical Medicine of Jeonbuk National University-Biomedical Research Institute of Jeonbuk National University Hospital, Jeonju
| | - Kyung Hee Lee
- Department of Radiology, Seoul National University Bundang Hospital, Seongnam-si, Gyeonggi-do
| | - Hwan Seok Yong
- Department of Radiology, Korea University Guro Hospital, Korea University College of Medicine
| | - Bomi Gil
- Department of Radiology, College of Medicine, The Catholic University of Korea
| | - Hye-Jeong Lee
- Department of Radiology, Research Institute of Radiological Science, Severance Hospital, Yonsei University College of Medicine
| | - Ki Yeol Lee
- Department of Radiology, Korea University College of Medicine
| | - Kyung Nyeo Jeon
- Department of Radiology, Gyeongsang National University, Jinju, Korea
| | | | | | | | | | - Kyuhyup Oh
- Bio Medical Research Center, Korea Testing Laboratory
| | - Jin Mo Goo
- Department of Radiology, Seoul National University College of Medicine
- Cancer Research Institute, Seoul National University, Seoul
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Griebel L, Misal M, Cornella J, Khan A, Wolter C, Yi J. Single port robotic assisted sacrocolpopexy: technique and tips. Int Urogynecol J 2022; 33:2905. [PMID: 35333928 DOI: 10.1007/s00192-022-05084-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Accepted: 12/08/2021] [Indexed: 10/18/2022]
Abstract
INTRODUCTION AND HYPOTHESIS Sacrocolpopexy is the most durable surgical procedure for the treatment of symptomatic pelvic organ prolapse (Maher et al. Cochrane Database Syst Rev. 2013;(4):CD004014). The single port robotic platform has recently been approved in the USA for use in urological surgery. Innovation in robotic surgery continues to evolve, minimizing abdominal wall trauma while improving instrumentation and technical feasibility. Identifying the appropriate procedures to utilize novel technology is important to understand the role of new surgical tools. Sacrocolpopexy procedure, when performed with supracervical hysterectomy, requires extension of an incision for specimen retrieval, making it ideal for single port surgery. The technique and adaptation to new instrumentation is demonstrated in this video. METHOD A surgical demonstration of single port robotic sacrocolpopexy is shown. RESULTS Sacrocolpopexy was successfully completed using the single port robotic platform. CONCLUSIONS Sacrocolpopexy is technically feasible with use of the single port robotic platform.
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Affiliation(s)
- Lauren Griebel
- Department of Gynecologic Surgery, Mayo Clinic Arizona, Phoenix, AZ, USA.
| | - M Misal
- Department of Gynecologic Surgery, Mayo Clinic Arizona, Phoenix, AZ, USA
| | - J Cornella
- Department of Gynecologic Surgery, Mayo Clinic Arizona, Phoenix, AZ, USA
| | - A Khan
- Department of Urology, Mayo Clinic Arizona, Phoenix, AZ, USA
| | - C Wolter
- Department of Urology, Mayo Clinic Arizona, Phoenix, AZ, USA
| | - J Yi
- Department of Gynecologic Surgery, Mayo Clinic Arizona, Phoenix, AZ, USA
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Yi J, Bagaria M. Transvaginal mesh excision with urethral reconstruction: tips and tricks. Am J Obstet Gynecol 2022. [DOI: 10.1016/j.ajog.2021.12.207] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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21
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Lancaster HL, Zheng S, Aleshina OO, Yu D, Yu Chernina V, Heuvelmans MA, de Bock GH, Dorrius MD, Gratama JW, Morozov SP, Gombolevskiy VA, Silva M, Yi J, Oudkerk M. Outstanding negative prediction performance of solid pulmonary nodule volume AI for ultra-LDCT baseline lung cancer screening risk stratification. Lung Cancer 2022; 165:133-140. [PMID: 35123156 DOI: 10.1016/j.lungcan.2022.01.002] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Revised: 10/04/2021] [Accepted: 01/03/2022] [Indexed: 12/17/2022]
Abstract
OBJECTIVE To evaluate performance of AI as a standalone reader in ultra-low-dose CT lung cancer baseline screening, and compare it to that of experienced radiologists. METHODS 283 participants who underwent a baseline ultra-LDCT scan in Moscow Lung Cancer Screening, between February 2017-2018, and had at least one solid lung nodule, were included. Volumetric nodule measurements were performed by five experienced blinded radiologists, and independently assessed using an AI lung cancer screening prototype (AVIEW LCS, v1.0.34, Coreline Soft, Co. ltd, Seoul, Korea) to automatically detect, measure, and classify solid nodules. Discrepancies were stratified into two groups: positive-misclassification (PM); nodule classified by the reader as a NELSON-plus /EUPS-indeterminate/positive nodule, which at the reference consensus read was < 100 mm3, and negative-misclassification (NM); nodule classified as a NELSON-plus /EUPS-negative nodule, which at consensus read was ≥ 100 mm3. RESULTS 1149 nodules with a solid-component were detected, of which 878 were classified as solid nodules. For the largest solid nodule per participant (n = 283); 61 [21.6 %; 53 PM, 8 NM] discrepancies were reported for AI as a standalone reader, compared to 43 [15.1 %; 22 PM, 21 NM], 36 [12.7 %; 25 PM, 11 NM], 29 [10.2 %; 25 PM, 4 NM], 28 [9.9 %; 6 PM, 22 NM], and 50 [17.7 %; 15 PM, 35 NM] discrepancies for readers 1, 2, 3, 4, and 5 respectively. CONCLUSION Our results suggest that through the use of AI as an impartial reader in baseline lung cancer screening, negative-misclassification results could exceed that of four out of five experienced radiologists, and radiologists' workload could be drastically diminished by up to 86.7%.
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Affiliation(s)
- Harriet L Lancaster
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands; Institute for Diagnostic Accuracy, Groningen, Netherlands
| | - Sunyi Zheng
- Department of Radiotherapy, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands; Institute for Diagnostic Accuracy, Groningen, Netherlands
| | - Olga O Aleshina
- State Budget-Funded Health Care Institution of the City of Moscow «Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department, Moscow, Russian Federation
| | | | - Valeria Yu Chernina
- State Budget-Funded Health Care Institution of the City of Moscow «Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department, Moscow, Russian Federation
| | - Marjolein A Heuvelmans
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands; Institute for Diagnostic Accuracy, Groningen, Netherlands
| | - Geertruida H de Bock
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Monique D Dorrius
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands; Department of Radiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | | | - Sergey P Morozov
- State Budget-Funded Health Care Institution of the City of Moscow «Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department, Moscow, Russian Federation
| | - Victor A Gombolevskiy
- State Budget-Funded Health Care Institution of the City of Moscow «Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department, Moscow, Russian Federation; AIRI, Moscow, Russian Federation
| | - Mario Silva
- Scienze Radiologiche, Department of Medicine and Surgery (DiMeC), University of Parma, Parma, Italy
| | | | - Matthijs Oudkerk
- Institute for Diagnostic Accuracy, Groningen, Netherlands; Faculty of Medical Sciences, University of Groningen, Groningen, Netherlands.
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22
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Griebel L, Butler K, Larson N, Ruddy K, Klanderman M, Yi J. Considering surgical menopause in breast cancer: the role of oophorectomy. Am J Obstet Gynecol 2022. [DOI: 10.1016/j.ajog.2021.12.154] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/01/2022]
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23
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McKee D, Yi J. Trigger point injections for myofascial pelvic pain. Am J Obstet Gynecol 2022. [DOI: 10.1016/j.ajog.2021.12.241] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/01/2022]
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Abstract
As the largest salivary gland in oral cavity, the parotid gland plays an important role in initial digesting and lubricating food. The abnormal secretory function of the parotid gland can lead to dental caries and oral mucosal inflammation. In recent years, single-cell RNA sequencing (scRNA-seq) has been used to explore the heterogeneity and diversity of cells in various organs and tissues. However, the transcription profile of the human parotid gland at single-cell resolution has not been reported yet. In this study, we constructed the cell atlas of human parotid gland using the 10× Genomics platform. Characteristic gene analysis identified the biological functions of serous acinar cell populations in secreting digestive enzymes and antibacterial proteins. We revealed the specificity and similarity of the parotid gland compared to other digestive glands through comparative analyses of other published scRNA-seq data sets. We also identified the cell-specific expression of hub genes for Sjögren syndrome in the human parotid gland by integrating the results of genome-wide association studies and bulk RNA-seq, which highlighted the importance of immune cell dysfunction in parotid Sjögren syndrome pathogenesis.
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Affiliation(s)
- M. Chen
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, China
- Department of Orthodontics, West China Hospital of Stomatology, Sichuan University, Chengdu, China
| | - W. Lin
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, China
- Department of Oral Implantology, West China Hospital of Stomatology, Sichuan University, Chengdu, China
| | - J. Gan
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, China
- Department of Head and Neck Oncology, West China Hospital of Stomatology, Sichuan University, Chengdu, China
| | - W. Lu
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, China
- Department of Orthodontics, West China Hospital of Stomatology, Sichuan University, Chengdu, China
| | - M. Wang
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, China
| | - X. Wang
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, China
- Department of Head and Neck Oncology, West China Hospital of Stomatology, Sichuan University, Chengdu, China
| | - J. Yi
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, China
- Department of Orthodontics, West China Hospital of Stomatology, Sichuan University, Chengdu, China
| | - Z. Zhao
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, China
- Department of Orthodontics, West China Hospital of Stomatology, Sichuan University, Chengdu, China
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25
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Vonder M, Zheng S, Dorrius MD, van der Aalst CM, de Koning HJ, Yi J, Yu D, Gratama JWC, Kuijpers D, Oudkerk M. Deep Learning for Automatic Calcium Scoring in Population-Based Cardiovascular Screening. JACC Cardiovasc Imaging 2022; 15:366-367. [PMID: 34419401 DOI: 10.1016/j.jcmg.2021.07.012] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Revised: 06/15/2021] [Accepted: 07/09/2021] [Indexed: 11/24/2022]
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26
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Lin X, Li J, Yu Y, Huang X, Yi J. Monosialotetrahexosylganglioside Sodium Promotes the Cortical Neurogenesis in Traumatic Brain Injury Rats. Indian J Pharm Sci 2022. [DOI: 10.36468/pharmaceutical-sciences.1056] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023] Open
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27
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Gu L, Wu Y, Yi J, Liu XW. [Current status and research advances on the use of assisted traction technique in endoscopic full-thickness resection]. Zhonghua Wei Chang Wai Ke Za Zhi 2021; 24:1122-1128. [PMID: 34923801 DOI: 10.3760/cma.j.cn441530-20210412-00160] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Endoscopic full-thickness resection (EFTR) allows completely resecting deep submucosal tumors (SMTs) in the gastrointestinal wall, which has a broad application prospect in clinic. However, its application and promotion are limited by complex surgical procedures and high surgical risk. Various auxiliary traction techniques are expected to reduce the operation difficulty and risk of EFTR and improve its operative success rate. To provide a reference for clinicians, we summarize various auxiliary traction techniques in EFTR in this article. The clip-with-line method is simple to operate and widely used, whereas its traction is limited and there is a risk of clip falling off. The snare traction method and the clip-snare traction method has advantage of large traction force, but its thrust is affected by the hardness of snare. The traction point of the grasping forceps traction method is flexible and easy to adjust. Nevertheless, it requires the use of a dual-channel upper endoscope, which is difficult to operate. The transparent cap traction method and the full-thickness resection device traction method takes a short time and is easy to promote, whereas the resectable lesion is limited, and the size of the lesion may affect the success rate. In contrast, the suture loop needle-T-tag tissue anchors assisted method has a large resection range, but the operation is complicated and the feasibility has not been verified. The robot-assisted method has flexible operation and excellent visualization, whereas it is expensive and difficult to operate. There is no report of the application of magnetic anchor technology in EFTR, but it may have good application prospects in the auxiliary traction of EFTR.
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Affiliation(s)
- L Gu
- Department of Gastroenterology, Xiangya Hospital, Central South University, Hunan International Scientific and Technological Cooperation Base of Artificial Intelligence Computer Aided Diagnosis and Treatment for Digestive Disease, Changsha 410008, China
| | - Y Wu
- Department of Gastroenterology, Xiangya Hospital, Central South University, Hunan International Scientific and Technological Cooperation Base of Artificial Intelligence Computer Aided Diagnosis and Treatment for Digestive Disease, Changsha 410008, China
| | - J Yi
- Department of Gastroenterology, Xiangya Hospital, Central South University, Hunan International Scientific and Technological Cooperation Base of Artificial Intelligence Computer Aided Diagnosis and Treatment for Digestive Disease, Changsha 410008, China
| | - X W Liu
- Department of Gastroenterology, Xiangya Hospital, Central South University, Hunan International Scientific and Technological Cooperation Base of Artificial Intelligence Computer Aided Diagnosis and Treatment for Digestive Disease, Changsha 410008, China
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28
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Zhou F, Wang J, Shayan G, Huang X, Wang K, Qu Y, Chen X, Wu R, Zhang Y, Sun S, Luo J, Liu Q, Zhang J, Xiao J, Yi J. Prognostic Significance of Tumor Infiltrating Lymphocytes (TILs) and Programmed Cell Death-Ligand 1 (PD-L1) in Nasopharyngeal Carcinoma. Int J Radiat Oncol Biol Phys 2021. [DOI: 10.1016/j.ijrobp.2021.07.1134] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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29
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Luo X, Yi J, Wu R, Huang X, Qu Y, Chen X, Zhang Y, Liu Q, Wang J, Zhang J, Luo J, Gao L, Xu G. Response-Adapted Strategy Based on Early Response to Radiotherapy Achieves Favorable Survival With Functional Larynx in Resectable, Locally Advanced Hypopharyngeal Cancer: An Analysis of 423 Real-World Cases. Int J Radiat Oncol Biol Phys 2021. [DOI: 10.1016/j.ijrobp.2021.07.1080] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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30
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McKee DC, Chapman H, Yi J, Magtibay PM. Robotic Excision of Transobturator Midurethral Sling. J Minim Invasive Gynecol 2021. [DOI: 10.1016/j.jmig.2021.09.470] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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31
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Luo X, Yi J, Wang J, Wu R, Huang X, Zhang Y, Wang K, Qu Y, Chen X, Zhang J, Luo J, GAO L, Xu G. Hypopharyngeal Carcinoma With Synchronous and Metachronous Multiple Malignancies: Clinical Characteristics and Prognosis Analysis of 673 Real World Cases. Int J Radiat Oncol Biol Phys 2021. [DOI: 10.1016/j.ijrobp.2021.07.1081] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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32
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Wu R, GAO L, Huang X, Xiao J, Wang K, Qu Y, Liu Q, Wang J, Zhang Y, Zhang J, Chen X, Luo J, Yi J. Stereotactic Body Radiation Therapy for the First-Line Comprehensive Treatment of Oligometastatic Nasopharyngeal Carcinoma: A Prospective, Single-Arm, Phase II Trial. Int J Radiat Oncol Biol Phys 2021. [DOI: 10.1016/j.ijrobp.2021.07.1337] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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33
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Kho K, Chen I, Berman J, Yi J, Zanotti S, Al Hilli M, Balk E, Saldanha I. Systematic Review of Outcomes after Radiofrequency Ablation for Fibroids: An Aagl Practice Committee Evidence Review. J Minim Invasive Gynecol 2021. [DOI: 10.1016/j.jmig.2021.09.338] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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34
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Vonder M, Zheng S, Dorrius MD, Van Der Aalst CM, De Koning HJ, Yi J, Yu D, Gratama JWC, Kuijpers D, Oudkerk M. Deep learning for automatic calcium scoring in population based cardiovascular screening. Eur Heart J 2021. [DOI: 10.1093/eurheartj/ehab724.0186] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Abstract
Background
High volumes of standardized coronary artery calcium (CAC) scans are generated in screening that need to be scored accurately and efficiently to risk stratify individuals.
Purpose
To evaluate the performance of deep learning based software for automatic coronary calcium scoring in a screening setting.
Methods
Participants from the Robinsca trial that underwent low-dose ECG-triggered cardiac CT for calcium scoring were included. CAC was measured with fully automated deep learning prototype and compared to the original manual assessment of the Robinsca trial. Detection rate, positive Agatston score and risk categorization (0–99, 100–399, ≥400) were compared using McNemar test, ICC, and Cohen's kappa. False negative (FN), false positive (FP) rate and diagnostic accuracy were determined for preventive treatment initiation (cut-off ≥100 AU).
Results
In total, 997 participants were included between December 2015 and June 2016. Median age was 61.0 y (IQR: 11.0) and 54.4% was male. A high agreement for detection was found between deep learning based and manual scoring, κ=0.87 (95% CI 0.85–0.89). Median Agatston score was 58.4 (IQR: 12.3–200.2) and 61.2 (IQR: 13.9–212.9) for deep learning based and manual assessment respectively, ICC was 0.958 (95% CI 0.951–0.964). Reclassification rate was 2.0%, with a very high agreement with κ=0.960 (95% CI: 0.943–0.997), p<0.001. FN rate was 0.7% and FP rate was 0.1% and diagnostic accuracy was 99.2% for initiation of preventive treatment.
Conclusion
Deep learning based software for automatic CAC scoring can be used in a cardiovascular CT screening setting with high accuracy for risk categorization and initiation of preventive treatment.
Funding Acknowledgement
Type of funding sources: Public grant(s) – EU funding. Main funding source(s): Robinsca trial was supported by advanced grant of European Research Council
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Affiliation(s)
- M Vonder
- University Medical Center Groningen, Epidemiology, Groningen, Netherlands (The)
| | - S Zheng
- University Medical Center Groningen, Radiotherapy, Groningen, Netherlands (The)
| | - M D Dorrius
- University Medical Center Groningen, Radiology, Groningen, Netherlands (The)
| | - C M Van Der Aalst
- Erasmus University Medical Centre, Cancer Institute, Rotterdam, Netherlands (The)
| | - H J De Koning
- Erasmus University Medical Centre, Cancer Institute, Rotterdam, Netherlands (The)
| | - J Yi
- Coreline Soft, Seoul, Korea (Democratic People's Republic of)
| | - D Yu
- Coreline Soft, Seoul, Korea (Democratic People's Republic of)
| | - J W C Gratama
- Gelre Hospital of Apeldoorn, Radiology, Apeldoorn, Netherlands (The)
| | - D Kuijpers
- Haaglanden Medical Center, Radiology, The Hague, Netherlands (The)
| | - M Oudkerk
- University of Groningen, Faculty of Medical Sciences, Groningen, Netherlands (The)
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35
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Choe J, Hwang HJ, Seo JB, Lee SM, Yun J, Kim MJ, Jeong J, Lee Y, Jin K, Park R, Kim J, Jeon H, Kim N, Yi J, Yu D, Kim B. Content-based Image Retrieval by Using Deep Learning for Interstitial Lung Disease Diagnosis with Chest CT. Radiology 2021; 302:187-197. [PMID: 34636634 DOI: 10.1148/radiol.2021204164] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Background Evaluation of interstitial lung disease (ILD) at CT is a challenging task that requires experience and is subject to substantial interreader variability. Purpose To investigate whether a proposed content-based image retrieval (CBIR) of similar chest CT images by using deep learning can aid in the diagnosis of ILD by readers with different levels of experience. Materials and methods This retrospective study included patients with confirmed ILD after multidisciplinary discussion and available CT images identified between January 2000 and December 2015. Database was composed of four disease classes: usual interstitial pneumonia (UIP), nonspecific interstitial pneumonia (NSIP), cryptogenic organizing pneumonia, and chronic hypersensitivity pneumonitis. Eighty patients were selected as queries from the database. The proposed CBIR retrieved the top three similar CT images with diagnosis from the database by comparing the extent and distribution of different regional disease patterns quantified by a deep learning algorithm. Eight readers with varying experience interpreted the query CT images and provided their most probable diagnosis in two reading sessions 2 weeks apart, before and after applying CBIR. Diagnostic accuracy was analyzed by using McNemar test and generalized estimating equation, and interreader agreement was analyzed by using Fleiss κ. Results A total of 288 patients were included (mean age, 58 years ± 11 [standard deviation]; 145 women). After applying CBIR, the overall diagnostic accuracy improved in all readers (before CBIR, 46.1% [95% CI: 37.1, 55.3]; after CBIR, 60.9% [95% CI: 51.8, 69.3]; P < .001). In terms of disease category, the diagnostic accuracy improved after applying CBIR in UIP (before vs after CBIR, 52.4% vs 72.8%, respectively; P < .001) and NSIP cases (before vs after CBIR, 42.9% vs 61.6%, respectively; P < .001). Interreader agreement improved after CBIR (before vs after CBIR Fleiss κ, 0.32 vs 0.47, respectively; P = .005). Conclusion The proposed content-based image retrieval system for chest CT images with deep learning improved the diagnostic accuracy of interstitial lung disease and interreader agreement in readers with different levels of experience. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Wielpütz in this issue.
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Affiliation(s)
- Jooae Choe
- From the Department of Radiology and Research Institute of Radiology (J.C., H.J.H., J.B.S., S.M.L., K.J., R.P., J.K., N.K.), Department of Convergence Medicine, Biomedical Engineering Research Center (J. Yun), and Department of Clinical Epidemiology and Biostatistics (M.J.K.), University of Ulsan College of Medicine, Asan Medical Center, 86 Asanbyeongwon-Gil, Songpa-Gu, Seoul 138-735, Korea; Department of Radiology, Soonchunhyang University Seoul Hospital, Soonchunhyang University College of Medicine, Seoul, Korea (J.J.); Department of Internal Medicine, Ajou University School of Medicine, Suwon, Korea (Y.L.); Department of Internal Medicine, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea (H.J.); and Coreline Soft, Seoul, Korea (J. Yi, D.Y., B.K.)
| | - Hye Jeon Hwang
- From the Department of Radiology and Research Institute of Radiology (J.C., H.J.H., J.B.S., S.M.L., K.J., R.P., J.K., N.K.), Department of Convergence Medicine, Biomedical Engineering Research Center (J. Yun), and Department of Clinical Epidemiology and Biostatistics (M.J.K.), University of Ulsan College of Medicine, Asan Medical Center, 86 Asanbyeongwon-Gil, Songpa-Gu, Seoul 138-735, Korea; Department of Radiology, Soonchunhyang University Seoul Hospital, Soonchunhyang University College of Medicine, Seoul, Korea (J.J.); Department of Internal Medicine, Ajou University School of Medicine, Suwon, Korea (Y.L.); Department of Internal Medicine, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea (H.J.); and Coreline Soft, Seoul, Korea (J. Yi, D.Y., B.K.)
| | - Joon Beom Seo
- From the Department of Radiology and Research Institute of Radiology (J.C., H.J.H., J.B.S., S.M.L., K.J., R.P., J.K., N.K.), Department of Convergence Medicine, Biomedical Engineering Research Center (J. Yun), and Department of Clinical Epidemiology and Biostatistics (M.J.K.), University of Ulsan College of Medicine, Asan Medical Center, 86 Asanbyeongwon-Gil, Songpa-Gu, Seoul 138-735, Korea; Department of Radiology, Soonchunhyang University Seoul Hospital, Soonchunhyang University College of Medicine, Seoul, Korea (J.J.); Department of Internal Medicine, Ajou University School of Medicine, Suwon, Korea (Y.L.); Department of Internal Medicine, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea (H.J.); and Coreline Soft, Seoul, Korea (J. Yi, D.Y., B.K.)
| | - Sang Min Lee
- From the Department of Radiology and Research Institute of Radiology (J.C., H.J.H., J.B.S., S.M.L., K.J., R.P., J.K., N.K.), Department of Convergence Medicine, Biomedical Engineering Research Center (J. Yun), and Department of Clinical Epidemiology and Biostatistics (M.J.K.), University of Ulsan College of Medicine, Asan Medical Center, 86 Asanbyeongwon-Gil, Songpa-Gu, Seoul 138-735, Korea; Department of Radiology, Soonchunhyang University Seoul Hospital, Soonchunhyang University College of Medicine, Seoul, Korea (J.J.); Department of Internal Medicine, Ajou University School of Medicine, Suwon, Korea (Y.L.); Department of Internal Medicine, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea (H.J.); and Coreline Soft, Seoul, Korea (J. Yi, D.Y., B.K.)
| | - Jihye Yun
- From the Department of Radiology and Research Institute of Radiology (J.C., H.J.H., J.B.S., S.M.L., K.J., R.P., J.K., N.K.), Department of Convergence Medicine, Biomedical Engineering Research Center (J. Yun), and Department of Clinical Epidemiology and Biostatistics (M.J.K.), University of Ulsan College of Medicine, Asan Medical Center, 86 Asanbyeongwon-Gil, Songpa-Gu, Seoul 138-735, Korea; Department of Radiology, Soonchunhyang University Seoul Hospital, Soonchunhyang University College of Medicine, Seoul, Korea (J.J.); Department of Internal Medicine, Ajou University School of Medicine, Suwon, Korea (Y.L.); Department of Internal Medicine, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea (H.J.); and Coreline Soft, Seoul, Korea (J. Yi, D.Y., B.K.)
| | - Min-Ju Kim
- From the Department of Radiology and Research Institute of Radiology (J.C., H.J.H., J.B.S., S.M.L., K.J., R.P., J.K., N.K.), Department of Convergence Medicine, Biomedical Engineering Research Center (J. Yun), and Department of Clinical Epidemiology and Biostatistics (M.J.K.), University of Ulsan College of Medicine, Asan Medical Center, 86 Asanbyeongwon-Gil, Songpa-Gu, Seoul 138-735, Korea; Department of Radiology, Soonchunhyang University Seoul Hospital, Soonchunhyang University College of Medicine, Seoul, Korea (J.J.); Department of Internal Medicine, Ajou University School of Medicine, Suwon, Korea (Y.L.); Department of Internal Medicine, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea (H.J.); and Coreline Soft, Seoul, Korea (J. Yi, D.Y., B.K.)
| | - Jewon Jeong
- From the Department of Radiology and Research Institute of Radiology (J.C., H.J.H., J.B.S., S.M.L., K.J., R.P., J.K., N.K.), Department of Convergence Medicine, Biomedical Engineering Research Center (J. Yun), and Department of Clinical Epidemiology and Biostatistics (M.J.K.), University of Ulsan College of Medicine, Asan Medical Center, 86 Asanbyeongwon-Gil, Songpa-Gu, Seoul 138-735, Korea; Department of Radiology, Soonchunhyang University Seoul Hospital, Soonchunhyang University College of Medicine, Seoul, Korea (J.J.); Department of Internal Medicine, Ajou University School of Medicine, Suwon, Korea (Y.L.); Department of Internal Medicine, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea (H.J.); and Coreline Soft, Seoul, Korea (J. Yi, D.Y., B.K.)
| | - Youngsoo Lee
- From the Department of Radiology and Research Institute of Radiology (J.C., H.J.H., J.B.S., S.M.L., K.J., R.P., J.K., N.K.), Department of Convergence Medicine, Biomedical Engineering Research Center (J. Yun), and Department of Clinical Epidemiology and Biostatistics (M.J.K.), University of Ulsan College of Medicine, Asan Medical Center, 86 Asanbyeongwon-Gil, Songpa-Gu, Seoul 138-735, Korea; Department of Radiology, Soonchunhyang University Seoul Hospital, Soonchunhyang University College of Medicine, Seoul, Korea (J.J.); Department of Internal Medicine, Ajou University School of Medicine, Suwon, Korea (Y.L.); Department of Internal Medicine, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea (H.J.); and Coreline Soft, Seoul, Korea (J. Yi, D.Y., B.K.)
| | - Kiok Jin
- From the Department of Radiology and Research Institute of Radiology (J.C., H.J.H., J.B.S., S.M.L., K.J., R.P., J.K., N.K.), Department of Convergence Medicine, Biomedical Engineering Research Center (J. Yun), and Department of Clinical Epidemiology and Biostatistics (M.J.K.), University of Ulsan College of Medicine, Asan Medical Center, 86 Asanbyeongwon-Gil, Songpa-Gu, Seoul 138-735, Korea; Department of Radiology, Soonchunhyang University Seoul Hospital, Soonchunhyang University College of Medicine, Seoul, Korea (J.J.); Department of Internal Medicine, Ajou University School of Medicine, Suwon, Korea (Y.L.); Department of Internal Medicine, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea (H.J.); and Coreline Soft, Seoul, Korea (J. Yi, D.Y., B.K.)
| | - Rohee Park
- From the Department of Radiology and Research Institute of Radiology (J.C., H.J.H., J.B.S., S.M.L., K.J., R.P., J.K., N.K.), Department of Convergence Medicine, Biomedical Engineering Research Center (J. Yun), and Department of Clinical Epidemiology and Biostatistics (M.J.K.), University of Ulsan College of Medicine, Asan Medical Center, 86 Asanbyeongwon-Gil, Songpa-Gu, Seoul 138-735, Korea; Department of Radiology, Soonchunhyang University Seoul Hospital, Soonchunhyang University College of Medicine, Seoul, Korea (J.J.); Department of Internal Medicine, Ajou University School of Medicine, Suwon, Korea (Y.L.); Department of Internal Medicine, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea (H.J.); and Coreline Soft, Seoul, Korea (J. Yi, D.Y., B.K.)
| | - Jihoon Kim
- From the Department of Radiology and Research Institute of Radiology (J.C., H.J.H., J.B.S., S.M.L., K.J., R.P., J.K., N.K.), Department of Convergence Medicine, Biomedical Engineering Research Center (J. Yun), and Department of Clinical Epidemiology and Biostatistics (M.J.K.), University of Ulsan College of Medicine, Asan Medical Center, 86 Asanbyeongwon-Gil, Songpa-Gu, Seoul 138-735, Korea; Department of Radiology, Soonchunhyang University Seoul Hospital, Soonchunhyang University College of Medicine, Seoul, Korea (J.J.); Department of Internal Medicine, Ajou University School of Medicine, Suwon, Korea (Y.L.); Department of Internal Medicine, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea (H.J.); and Coreline Soft, Seoul, Korea (J. Yi, D.Y., B.K.)
| | - Howook Jeon
- From the Department of Radiology and Research Institute of Radiology (J.C., H.J.H., J.B.S., S.M.L., K.J., R.P., J.K., N.K.), Department of Convergence Medicine, Biomedical Engineering Research Center (J. Yun), and Department of Clinical Epidemiology and Biostatistics (M.J.K.), University of Ulsan College of Medicine, Asan Medical Center, 86 Asanbyeongwon-Gil, Songpa-Gu, Seoul 138-735, Korea; Department of Radiology, Soonchunhyang University Seoul Hospital, Soonchunhyang University College of Medicine, Seoul, Korea (J.J.); Department of Internal Medicine, Ajou University School of Medicine, Suwon, Korea (Y.L.); Department of Internal Medicine, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea (H.J.); and Coreline Soft, Seoul, Korea (J. Yi, D.Y., B.K.)
| | - Namkug Kim
- From the Department of Radiology and Research Institute of Radiology (J.C., H.J.H., J.B.S., S.M.L., K.J., R.P., J.K., N.K.), Department of Convergence Medicine, Biomedical Engineering Research Center (J. Yun), and Department of Clinical Epidemiology and Biostatistics (M.J.K.), University of Ulsan College of Medicine, Asan Medical Center, 86 Asanbyeongwon-Gil, Songpa-Gu, Seoul 138-735, Korea; Department of Radiology, Soonchunhyang University Seoul Hospital, Soonchunhyang University College of Medicine, Seoul, Korea (J.J.); Department of Internal Medicine, Ajou University School of Medicine, Suwon, Korea (Y.L.); Department of Internal Medicine, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea (H.J.); and Coreline Soft, Seoul, Korea (J. Yi, D.Y., B.K.)
| | - Jaeyoun Yi
- From the Department of Radiology and Research Institute of Radiology (J.C., H.J.H., J.B.S., S.M.L., K.J., R.P., J.K., N.K.), Department of Convergence Medicine, Biomedical Engineering Research Center (J. Yun), and Department of Clinical Epidemiology and Biostatistics (M.J.K.), University of Ulsan College of Medicine, Asan Medical Center, 86 Asanbyeongwon-Gil, Songpa-Gu, Seoul 138-735, Korea; Department of Radiology, Soonchunhyang University Seoul Hospital, Soonchunhyang University College of Medicine, Seoul, Korea (J.J.); Department of Internal Medicine, Ajou University School of Medicine, Suwon, Korea (Y.L.); Department of Internal Medicine, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea (H.J.); and Coreline Soft, Seoul, Korea (J. Yi, D.Y., B.K.)
| | - Donghoon Yu
- From the Department of Radiology and Research Institute of Radiology (J.C., H.J.H., J.B.S., S.M.L., K.J., R.P., J.K., N.K.), Department of Convergence Medicine, Biomedical Engineering Research Center (J. Yun), and Department of Clinical Epidemiology and Biostatistics (M.J.K.), University of Ulsan College of Medicine, Asan Medical Center, 86 Asanbyeongwon-Gil, Songpa-Gu, Seoul 138-735, Korea; Department of Radiology, Soonchunhyang University Seoul Hospital, Soonchunhyang University College of Medicine, Seoul, Korea (J.J.); Department of Internal Medicine, Ajou University School of Medicine, Suwon, Korea (Y.L.); Department of Internal Medicine, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea (H.J.); and Coreline Soft, Seoul, Korea (J. Yi, D.Y., B.K.)
| | - Byeongsoo Kim
- From the Department of Radiology and Research Institute of Radiology (J.C., H.J.H., J.B.S., S.M.L., K.J., R.P., J.K., N.K.), Department of Convergence Medicine, Biomedical Engineering Research Center (J. Yun), and Department of Clinical Epidemiology and Biostatistics (M.J.K.), University of Ulsan College of Medicine, Asan Medical Center, 86 Asanbyeongwon-Gil, Songpa-Gu, Seoul 138-735, Korea; Department of Radiology, Soonchunhyang University Seoul Hospital, Soonchunhyang University College of Medicine, Seoul, Korea (J.J.); Department of Internal Medicine, Ajou University School of Medicine, Suwon, Korea (Y.L.); Department of Internal Medicine, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea (H.J.); and Coreline Soft, Seoul, Korea (J. Yi, D.Y., B.K.)
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Tian L, Hunt B, Bell MAL, Yi J, Smith JT, Ochoa M, Intes X, Durr NJ. Deep Learning in Biomedical Optics. Lasers Surg Med 2021; 53:748-775. [PMID: 34015146 PMCID: PMC8273152 DOI: 10.1002/lsm.23414] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2021] [Revised: 04/02/2021] [Accepted: 04/15/2021] [Indexed: 01/02/2023]
Abstract
This article reviews deep learning applications in biomedical optics with a particular emphasis on image formation. The review is organized by imaging domains within biomedical optics and includes microscopy, fluorescence lifetime imaging, in vivo microscopy, widefield endoscopy, optical coherence tomography, photoacoustic imaging, diffuse tomography, and functional optical brain imaging. For each of these domains, we summarize how deep learning has been applied and highlight methods by which deep learning can enable new capabilities for optics in medicine. Challenges and opportunities to improve translation and adoption of deep learning in biomedical optics are also summarized. Lasers Surg. Med. © 2021 Wiley Periodicals LLC.
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Affiliation(s)
- L. Tian
- Department of Electrical and Computer Engineering, Boston University, Boston, MA, USA
| | - B. Hunt
- Thayer School of Engineering, Dartmouth College, Hanover, NH, USA
| | - M. A. L. Bell
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, USA
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA
| | - J. Yi
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
- Department of Ophthalmology, Johns Hopkins University, Baltimore, MD, USA
| | - J. T. Smith
- Center for Modeling, Simulation, and Imaging in Medicine, Rensselaer Polytechnic Institute, Troy, New York NY 12180
| | - M. Ochoa
- Center for Modeling, Simulation, and Imaging in Medicine, Rensselaer Polytechnic Institute, Troy, New York NY 12180
| | - X. Intes
- Center for Modeling, Simulation, and Imaging in Medicine, Rensselaer Polytechnic Institute, Troy, New York NY 12180
| | - N. J. Durr
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, USA
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
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Hwang HJ, Seo JB, Lee SM, Kim N, Yi J, Lee JS, Lee SW, Oh YM, Lee SD. New Method for Combined Quantitative Assessment of Air-Trapping and Emphysema on Chest Computed Tomography in Chronic Obstructive Pulmonary Disease: Comparison with Parametric Response Mapping. Korean J Radiol 2021; 22:1719-1729. [PMID: 34269529 PMCID: PMC8484152 DOI: 10.3348/kjr.2021.0033] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Revised: 03/23/2021] [Accepted: 03/26/2021] [Indexed: 11/15/2022] Open
Abstract
Objective Emphysema and small-airway disease are the two major components of chronic obstructive pulmonary disease (COPD). We propose a novel method of quantitative computed tomography (CT) emphysema air-trapping composite (EAtC) mapping to assess each COPD component. We analyzed the potential use of this method for assessing lung function in patients with COPD. Materials and Methods A total of 584 patients with COPD underwent inspiration and expiration CTs. Using pairwise analysis of inspiration and expiration CTs with non-rigid registration, EAtC mapping classified lung parenchyma into three areas: Normal, functional air trapping (fAT), and emphysema (Emph). We defined fAT as the area with a density change of less than 60 Hounsfield units (HU) between inspiration and expiration CTs among areas with a density less than −856 HU on inspiration CT. The volume fraction of each area was compared with clinical parameters and pulmonary function tests (PFTs). The results were compared with those of parametric response mapping (PRM) analysis. Results The relative volumes of the EAtC classes differed according to the Global Initiative for Chronic Obstructive Lung Disease stages (p < 0.001). Each class showed moderate correlations with forced expiratory volume in 1 second (FEV1) and FEV1/forced vital capacity (FVC) (r = −0.659–0.674, p < 0.001). Both fAT and Emph were significant predictors of FEV1 and FEV1/FVC (R2 = 0.352 and 0.488, respectively; p < 0.001). fAT was a significant predictor of mean forced expiratory flow between 25% and 75% and residual volume/total vital capacity (R2 = 0.264 and 0.233, respectively; p < 0.001), while Emph and age were significant predictors of carbon monoxide diffusing capacity (R2 = 0.303; p < 0.001). fAT showed better correlations with PFTs than with small-airway disease on PRM. Conclusion The proposed quantitative CT EAtC mapping provides comprehensive lung functional information on each disease component of COPD, which may serve as an imaging biomarker of lung function.
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Affiliation(s)
- Hye Jeon Hwang
- Departments of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Joon Beom Seo
- Departments of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.
| | - Sang Min Lee
- Departments of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Namkug Kim
- Departments of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | | | - Jae Seung Lee
- Department of Pulmonary and Critical Care Medicine and Clinical Research Center for Chronic Obstructive Airway Diseases, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Sei Won Lee
- Department of Pulmonary and Critical Care Medicine and Clinical Research Center for Chronic Obstructive Airway Diseases, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Yeon Mok Oh
- Department of Pulmonary and Critical Care Medicine and Clinical Research Center for Chronic Obstructive Airway Diseases, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Sang Do Lee
- Department of Pulmonary and Critical Care Medicine and Clinical Research Center for Chronic Obstructive Airway Diseases, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
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Dong J, Huan Y, Huang B, Yi J, Liu YH, Sun BA, Wang WH, Bai HY. Unusually thick shear-softening surface of micrometer-size metallic glasses. ACTA ACUST UNITED AC 2021; 2:100106. [PMID: 34557757 PMCID: PMC8454631 DOI: 10.1016/j.xinn.2021.100106] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Accepted: 04/12/2021] [Indexed: 11/29/2022]
Abstract
The surface of glass is crucial for understanding many fundamental processes in glassy solids. A common notion is that a glass surface is a thin layer with liquid-like atomic dynamics and a thickness of a few tens of nanometers. Here, we measured the shear modulus at the surface of both millimeter-size and micrometer-size metallic glasses (MGs) through high-sensitivity torsion techniques. We found a pronounced shear-modulus softening at the surface of MGs. Compared with the bulk, the maximum decrease in the surface shear modulus (G) for the micro-scale MGs reaches ~27%, which is close to the decrease in the G upon glass transition, yet it still behaves solid-like. Strikingly, the surface thickness estimated from the shear-modulus softening is at least 400 nm, which is approximately one order of magnitude larger than that revealed from the glass dynamics. The unusually thick surface is also confirmed by measurements using X-ray nano-computed tomography, and this may account for the brittle-to-ductile transition of the MGs with size reductions. The unique and unusual properties at the surface of the micrometer-size MGs are physically related to the negative pressure effect during the thermoplastic formation process, which can dramatically reduce the density of the proximate surface region in the supercooled liquid state. The shear modulus and thickness of metallic glass (MG) surface is determined through torsion testing on micrometer-size wires The surface region of MG wires has a significant shear-modulus softening close to the supercooled liquid, yet still behaves solid-like The thickness of the soft surface of MG wires is at least 400 nm, which is about one order of magnitude larger than those revealed from surface dynamics The unusually thick surface accounts for the brittle-to-ductile transition of the MGs with size reduction
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Affiliation(s)
- J Dong
- Institute of Physics, Chinese Academy of Sciences, Beijing 100190, China.,Songshan Lake Materials Laboratory, Dongguan, Guangdong 523808, China
| | - Y Huan
- State Key Laboratory of Nonlinear Mechanics (LNM), Institute of Mechanics, Chinese Academy of Sciences, Beijing 100190, China
| | - B Huang
- Institute of Materials, School of Materials Science and Engineering, Shanghai University, Shanghai 200444, China
| | - J Yi
- Institute of Materials, School of Materials Science and Engineering, Shanghai University, Shanghai 200444, China
| | - Y H Liu
- Institute of Physics, Chinese Academy of Sciences, Beijing 100190, China.,Songshan Lake Materials Laboratory, Dongguan, Guangdong 523808, China
| | - B A Sun
- Institute of Physics, Chinese Academy of Sciences, Beijing 100190, China.,College of Materials Science and Opto-Electronic Technology, University of Chinese Academy of Sciences, Beijing 100049, China.,Songshan Lake Materials Laboratory, Dongguan, Guangdong 523808, China
| | - W H Wang
- Institute of Physics, Chinese Academy of Sciences, Beijing 100190, China.,College of Materials Science and Opto-Electronic Technology, University of Chinese Academy of Sciences, Beijing 100049, China.,Songshan Lake Materials Laboratory, Dongguan, Guangdong 523808, China
| | - H Y Bai
- Institute of Physics, Chinese Academy of Sciences, Beijing 100190, China.,College of Materials Science and Opto-Electronic Technology, University of Chinese Academy of Sciences, Beijing 100049, China.,Songshan Lake Materials Laboratory, Dongguan, Guangdong 523808, China
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Hwang EJ, Goo JM, Kim HY, Yi J, Kim Y. Optimum diameter threshold for lung nodules at baseline lung cancer screening with low-dose chest CT: exploration of results from the Korean Lung Cancer Screening Project. Eur Radiol 2021; 31:7202-7212. [PMID: 33738597 DOI: 10.1007/s00330-021-07827-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2020] [Revised: 01/01/2021] [Accepted: 02/22/2021] [Indexed: 11/29/2022]
Abstract
OBJECTIVES To explore the optimum diameter threshold for solid nodules to define positive results at baseline screening low-dose CT (LDCT) and to compare two-dimensional and volumetric measurement of lung nodules for the diagnosis of lung cancers. METHODS We included consecutive participants from the Korean Lung Cancer Screening project between 2017 and 2018. The average transverse diameter and effective diameter (diameter of a sphere with the same volume) of lung nodules were measured by semi-automated segmentation. Diagnostic performances for lung cancers diagnosed within 1 year after LDCT were evaluated using area under receiver-operating characteristic curves (AUCs), sensitivities, and specificities, with diameter thresholds for solid nodules ranging from 6 to 10 mm. The reduction of unnecessary follow-up LDCTs and the diagnostic delay of lung cancers were estimated for each threshold. RESULTS Fifty-two lung cancers were diagnosed among 10,424 (10,141 men; median age 62 years) participants within 1 year after LDCT. Average transverse (0.980) and effective diameters (0.981) showed similar AUCs (p = .739). Elevating the average transverse diameter threshold from 6 to 9 mm resulted in a significantly increased specificity (91.7 to 96.7%, p < .001), a modest reduction in sensitivity (96.2 to 94.2%, p = .317), a 60.2% estimated reduction of unnecessary follow-up LDCTs, and a diagnostic delay in 1.9% of lung cancers. Elevating the threshold to 10 mm led to a significant reduction in sensitivity (86.5%, p = .025). CONCLUSIONS Elevating the diameter threshold for solid nodules from 6 to 9 mm may lead to a substantial reduction in unnecessary follow-up LDCTs with a small proportion of diagnostic delay of lung cancers. KEY POINTS • Elevation of the diameter threshold for solid nodules from 6 to 9 mm can substantially reduce unnecessary follow-up LDCTs with a small proportion of diagnostic delay of lung cancers. • The average transverse and effective diameters of lung nodules showed similar performances for the prediction of a lung cancer diagnosis.
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Affiliation(s)
- Eui Jin Hwang
- Department of Radiology and Institute of Radiation Medicine, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul, 03080, South Korea
| | - Jin Mo Goo
- Department of Radiology and Institute of Radiation Medicine, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul, 03080, South Korea. .,Cancer Research Institute, Seoul National University, Seoul, South Korea.
| | - Hyae Young Kim
- Department of Radiology, National Cancer Center, Goyang, South Korea
| | | | - Yeol Kim
- National Cancer Control Institute, National Cancer Center, Goyang, South Korea
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40
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Yi J, Yang MM, Luo XD, Rosenkranz A, Wang B, Song H, Jiang N. Unprecedented tribological performance of binary Sb/Ag-doped MoS2 coatings fabricated with chemical vapor deposition. Appl Nanosci 2021. [DOI: 10.1007/s13204-020-01638-x] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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41
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Delara R, Misal M, Yi J, Wasson M. Barriers to Referral to Minimally Invasive Gynecology Surgical Subspecialists. J Minim Invasive Gynecol 2020. [DOI: 10.1016/j.jmig.2020.08.072] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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Behbehani S, Suarez-Salvador E, Kosiorek H, Yi J, Magrina J. Impact of a Revised Cuff Closure Technique on the Rate of Vaginal Cuff Dehiscence with Endoscopic Hysterectomy. J Minim Invasive Gynecol 2020. [DOI: 10.1016/j.jmig.2020.08.150] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Sun M, Wang K, Qu Y, Zhang S, Chen X, Wu R, Zhang Y, Huang X, Yi J, Xiao J, Xu G, Luo J. Clinical Outcomes And Patterns Of Treatment Failure In Patients With Esthesioneuroblastomas (ENB). Int J Radiat Oncol Biol Phys 2020. [DOI: 10.1016/j.ijrobp.2020.07.322] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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44
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Behbehani S, Salvador ES, Kosiorek H, Yi J, Magrina J. The Risk of Vaginal Cuff Dehiscence with Different Suture Types Following Endoscopic Hysterectomy. J Minim Invasive Gynecol 2020. [DOI: 10.1016/j.jmig.2020.08.055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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45
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Ghaith S, Voleti S, Newman H, Magtibay P, Yi J. A Comparison of Hysterectomy and Prostatectomy Medicare Reimbursement Rates: 2010-2019. J Minim Invasive Gynecol 2020. [DOI: 10.1016/j.jmig.2020.08.184] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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46
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Haverland R, Luckritz T, Lim E, Buras M, Yi J. Engaging the Opioid Epidemic Head on: Improving Proper Disposal of Unused Opioid Medications after Surgery. J Minim Invasive Gynecol 2020. [DOI: 10.1016/j.jmig.2020.08.305] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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47
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Misal M, Yi J. Exploring the Retropubic Space: Resection of Urethral Leiomyoma. J Minim Invasive Gynecol 2020. [DOI: 10.1016/j.jmig.2020.08.094] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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48
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Delara R, Islam M, Thomas N, Mi L, Lim E, Yi J. Shared Decision Making in Opioid Prescribing in Gynecologic Surgery: A Prospective Randomized Controlled Trial. J Minim Invasive Gynecol 2020. [DOI: 10.1016/j.jmig.2020.08.141] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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Hwang EJ, Goo JM, Kim HY, Yoon SH, Jin GY, Yi J, Kim Y. Variability in interpretation of low-dose chest CT using computerized assessment in a nationwide lung cancer screening program: comparison of prospective reading at individual institutions and retrospective central reading. Eur Radiol 2020; 31:2845-2855. [PMID: 33123794 DOI: 10.1007/s00330-020-07424-1] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2020] [Revised: 09/29/2020] [Accepted: 10/14/2020] [Indexed: 12/17/2022]
Abstract
OBJECTIVES To evaluate the degree of variability in computer-assisted interpretation of low-dose chest CTs (LDCTs) among radiologists in a nationwide lung cancer screening (LCS) program, through comparison with a retrospective interpretation from a central laboratory. MATERIALS AND METHODS Consecutive baseline LDCTs (n = 3353) from a nationwide LCS program were investigated. In the institutional reading, 20 radiologists in 14 institutions interpreted LDCTs using computer-aided detection and semi-automated segmentation systems for lung nodules. In the retrospective central review, a single radiologist re-interpreted all LDCTs using the same system, recording any non-calcified nodules ≥ 3 mm without arbitrary rejection of semi-automated segmentation to minimize the intervention of radiologist's discretion. Positive results (requiring additional follow-up LDCTs or diagnostic procedures) were initially classified by the lung CT screening reporting and data system (Lung-RADS) during the interpretation, while the classifications based on the volumetric criteria from the Dutch-Belgian lung cancer screening trial (NELSON) were retrospectively applied. Variabilities in positive rates were assessed with coefficients of variation (CVs). RESULTS In the institutional reading, positive rates by the Lung-RADS ranged from 7.5 to 43.3%, and those by the NELSON ranged from 11.4 to 45.0% across radiologists. The central review exhibited higher positive rates by Lung-RADS (20.0% vs. 27.3%; p < .001) and the NELSON (23.1% vs. 37.0%; p < .001), and lower inter-institution variability (CV, 0.30 vs. 0.12, p = .003 by Lung-RADS; CV, 0.25 vs. 0.12, p = .014 by the NELSON) compared to the institutional reading. CONCLUSION Considerable inter-institution variability in the interpretation of LCS results is caused by different usage of the computer-assisted system. KEY POINTS • Considerable variability existed in the interpretation of screening LDCT among radiologists partly from the different usage of the computerized system. • A retrospective reading of low-dose chest CTs in the central laboratory resulted in reduced variability but an increased positive rate.
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Affiliation(s)
- Eui Jin Hwang
- Department of Radiology and Institute of Radiation Medicine, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul, 03080, South Korea
| | - Jin Mo Goo
- Department of Radiology and Institute of Radiation Medicine, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul, 03080, South Korea. .,Cancer Research Institute, Seoul National University, Seoul, South Korea.
| | - Hyae Young Kim
- Department of Radiology, National Cancer Center, Goyang, South Korea
| | - Soon Ho Yoon
- Department of Radiology and Institute of Radiation Medicine, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul, 03080, South Korea
| | - Gong Yong Jin
- Department of Radiology, Jeonbuk National University Hospital, Jeonju, South Korea
| | | | - Yeol Kim
- National Cancer Control Institute, National Cancer Center, Goyang, South Korea
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Yi J, Wang F, Qin YL, Wang Y, Lin Q, Xiao Y. [Correlation between compassion fatigue and workplace violence in emergency department nurses]. Zhonghua Lao Dong Wei Sheng Zhi Ye Bing Za Zhi 2020; 38:597-601. [PMID: 32892588 DOI: 10.3760/cma.j.cn121094-20190808-00334] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Objective: To investigate the workplace violence and compassion fatigue of nurses in emergency department and to explore the relationship between the two. Methods: The general information questionnaire, workplace violence scale and professional quality of life scale were used to investigate 957 emergency department nurses of 28 Level II hospitals and above. Results: The scores of each dimension of the professional quality of life scale for nurses in the emergency department were: compassion satisfaction score was 29.91±7.82, the burnout score was 26.63±5.66, and the second trauma score was 23.17±5.94. The total score of compassion fatigue is 49.80±10.42. The incidence of workplace violence was 77.6%. Workplace violence was negatively correlated with compassion satisfaction (r=-0.250, P<0.01) , and positively correlated with burnout, secondary trauma, and total compassion fatigue (r=0.349、0.340、0.384, P<0.01) . Whether there is only non-physical violence in the compassion satisfaction, burnout, secondary trauma, compassion fatigue total score is not statistically significant. Conclusion: Compassion fatigue is more serious in emergency department nurses, and the incidence of workplace violence is higher. Workplace violence has a positive effect on compassion fatigue. Nursing managers should actively prevent workplace violence and improve the working environment, thus reducing empathy fatigue.
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Affiliation(s)
- J Yi
- Chengdu University of Traditional Chinese Medicine, Chengdu 610075, China
| | - F Wang
- Affiliated Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu 610075, China
| | - Y L Qin
- Chengdu University of Traditional Chinese Medicine, Chengdu 610075, China
| | - Y Wang
- Chengdu University of Traditional Chinese Medicine, Chengdu 610075, China
| | - Q Lin
- Chengdu University of Traditional Chinese Medicine, Chengdu 610075, China
| | - Y Xiao
- Chengdu University of Traditional Chinese Medicine, Chengdu 610075, China
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