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Lu J, Deng Q, Chen Y, Liu W. Impact of perceived ease of use, organizational support mechanism, and industry competitive pressure on physicians' use of liver cancer screening technology in medical alliances. Front Public Health 2023; 11:1174334. [PMID: 37601185 PMCID: PMC10434768 DOI: 10.3389/fpubh.2023.1174334] [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: 02/26/2023] [Accepted: 07/17/2023] [Indexed: 08/22/2023] Open
Abstract
Background Liver cancer is one of the malignant tumors worldwide, while the prevention and control situation is grim at present, and the diffusion of its early screening technology still faces some challenges. This study aims to investigate the influencing mechanism of perceived ease of use, organizational support mechanism, and industry competitive pressure on hepatic early screening technologies use by physicians, so as to promote the wider use of corresponding technologies. Methods Under the theoretical guidance of technology-organization-environment framework and mindsponge theory, this study took hepatic contrast-enhanced ultrasound as an example, and conducted a cross-sectional questionnaire by randomly selecting physicians from Fujian and Jiangxi provinces in China with a high and low incidence of liver cancer, respectively. Structural equation modeling was used to determine the correlation among perceived ease of use, organizational support mechanism, and industry competitive pressure, as well as their impact on the physicians' behavior toward contrast-enhanced ultrasound use. Results The hypothesis model fits well with the data (χ2/df = 1.863, GFI = 0.937, AGFI = 0.908, RMSEA = 0.054, NFI = 0.959, IFI = 0.980, CFI = 0.980). Under technology-organization-environment framework, the perceived ease of use (β = 0.171, p < 0.05), organizational support mechanism (β = 0.423, p < 0.01), industry competitive pressure (β = 0.159, p < 0.05) significantly influenced physicians' use of hepatic contrast-enhanced ultrasound. Besides, perceived ease of use and organizational support mechanism (β = 0.216, p < 0.01), perceived ease of use and industry competitive pressure (β = 0.671, p < 0.01), organizational support mechanism and industry competitive pressure (β = 0.330, p < 0.01) were all associated significantly. Conclusion From the lens of information processing (mindsponge theory) and technology-organization-environment framework, this study clarified the social and psychological influencing mechanism of perceived ease of use, organizational support mechanism, and industry competitive pressure on physicians' use of hepatic contrast-enhanced ultrasound. The results will directly propose recommendations for expanding hepatic contrast-enhanced ultrasound utilization and indirectly promoting other appropriate and effective health technologies diffusion within the integrated health system.
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Affiliation(s)
| | | | | | - Wenbin Liu
- School of Health Management, Fujian Medical University, Fuzhou, China
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Jeong WK, Kang HJ, Choi SH, Park MS, Yu MH, Kim B, You MW, Lim S, Cho YS, Lee MW, Hwang JA, Lee JY, Kim JH, Joo I, Bae JS, Kim SY, Chung YE, Kim DH, Lee JM. Diagnosing Hepatocellular Carcinoma Using Sonazoid Contrast-Enhanced Ultrasonography: 2023 Guidelines From the Korean Society of Radiology and the Korean Society of Abdominal Radiology. Korean J Radiol 2023; 24:482-497. [PMID: 37271203 DOI: 10.3348/kjr.2023.0324] [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: 04/07/2023] [Accepted: 04/11/2023] [Indexed: 06/06/2023] Open
Abstract
Sonazoid, a second-generation ultrasound contrast agent, was introduced for the diagnosis of hepatic nodules. To clarify the issues with Sonazoid contrast-enhanced ultrasonography for the diagnosis of hepatocellular carcinoma (HCC), the Korean Society of Radiology and Korean Society of Abdominal Radiology collaborated on the guidelines. The guidelines are de novo, evidence-based, and selected using an electronic voting system for consensus. These include imaging protocols, diagnostic criteria for HCC, diagnostic value for lesions that are inconclusive on other imaging results, differentiation from non-HCC malignancies, surveillance of HCC, and treatment response after locoregional and systemic treatment for HCC.
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Affiliation(s)
- Woo Kyoung Jeong
- Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Hyo-Jin Kang
- Department of Radiology and Research Institute of Radiological Science, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Korea
| | - Sang Hyun Choi
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Mi-Suk Park
- Department of Radiology and Research Institute of Radiological Science, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Mi Hye Yu
- Department of Radiology, Konkuk University Hospital, Konkuk University College of Medicine, Seoul, Korea
| | - Bohyun Kim
- Department of Radiology, Seoul St. Mary Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Myung-Won You
- Department of Radiology, Kyung Hee University Hospital, Kyung Hee University College of Medicine, Seoul, Korea
| | - Sanghyeok Lim
- Department of Radiology, Soonchunhyang University Bucheon Hospital, Soonchunhyang University College of Medicine, Bucheon, Korea
| | - Young Seo Cho
- Department of Radiology, Hanyang University Guri Hospital, Hanyang University College of Medicine, Guri, Korea
| | - Min Woo Lee
- Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Jeong Ah Hwang
- Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Jae Young Lee
- Department of Radiology and Research Institute of Radiological Science, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Korea
| | - Jung Hoon Kim
- Department of Radiology and Research Institute of Radiological Science, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Korea
| | - Ijin Joo
- Department of Radiology and Research Institute of Radiological Science, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Korea
| | - Jae Seok Bae
- Department of Radiology and Research Institute of Radiological Science, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Korea
| | - So Yeon Kim
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Yong Eun Chung
- Department of Radiology and Research Institute of Radiological Science, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Dong Hwan Kim
- Department of Radiology, Seoul St. Mary Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Jeong Min Lee
- Department of Radiology and Research Institute of Radiological Science, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Korea.
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Mitrea D, Badea R, Mitrea P, Brad S, Nedevschi S. Hepatocellular Carcinoma Automatic Diagnosis within CEUS and B-Mode Ultrasound Images Using Advanced Machine Learning Methods. SENSORS (BASEL, SWITZERLAND) 2021; 21:2202. [PMID: 33801125 PMCID: PMC8004125 DOI: 10.3390/s21062202] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/21/2021] [Revised: 03/12/2021] [Accepted: 03/16/2021] [Indexed: 02/06/2023]
Abstract
Hepatocellular Carcinoma (HCC) is the most common malignant liver tumor, being present in 70% of liver cancer cases. It usually evolves on the top of the cirrhotic parenchyma. The most reliable method for HCC diagnosis is the needle biopsy, which is an invasive, dangerous method. In our research, specific techniques for non-invasive, computerized HCC diagnosis are developed, by exploiting the information from ultrasound images. In this work, the possibility of performing the automatic diagnosis of HCC within B-mode ultrasound and Contrast-Enhanced Ultrasound (CEUS) images, using advanced machine learning methods based on Convolutional Neural Networks (CNN), was assessed. The recognition performance was evaluated separately on B-mode ultrasound images and on CEUS images, respectively, as well as on combined B-mode ultrasound and CEUS images. For this purpose, we considered the possibility of combining the input images directly, performing feature level fusion, then providing the resulted data at the entrances of representative CNN classifiers. In addition, several multimodal combined classifiers were experimented, resulted by the fusion, at classifier, respectively, at the decision levels of two different branches based on the same CNN architecture, as well as on different CNN architectures. Various combination methods, and also the dimensionality reduction method of Kernel Principal Component Analysis (KPCA), were involved in this process. These results were compared with those obtained on the same dataset, when employing advanced texture analysis techniques in conjunction with conventional classification methods and also with equivalent state-of-the-art approaches. An accuracy above 97% was achieved when our new methodology was applied.
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Affiliation(s)
- Delia Mitrea
- Department of Computer Science, Faculty of Automation and Computer Science, Technical University of Cluj-Napoca, Baritiu Street, No. 26-28, 400027 Cluj-Napoca, Romania; (D.M.); (P.M.); (S.N.)
| | - Radu Badea
- Medical Imaging Department, Iuliu Hatieganu University of Medicine and Pharmacy, Cluj-Napoca, Babes Street, No. 8, 400012 Cluj-Napoca, Romania;
- Regional Institute of Gastroenterology and Hepatology, Iuliu Hatieganu University of Medicine and Pharmacy, Cluj-Napoca, 19-21 Croitorilor Street, 400162 Cluj-Napoca, Romania
| | - Paulina Mitrea
- Department of Computer Science, Faculty of Automation and Computer Science, Technical University of Cluj-Napoca, Baritiu Street, No. 26-28, 400027 Cluj-Napoca, Romania; (D.M.); (P.M.); (S.N.)
| | - Stelian Brad
- Department of Design Engineering and Robotics, Faculty of Machine Building, Technical University of Cluj-Napoca, Muncii Boulevard, No. 103-105, 400641 Cluj-Napoca, Romania
| | - Sergiu Nedevschi
- Department of Computer Science, Faculty of Automation and Computer Science, Technical University of Cluj-Napoca, Baritiu Street, No. 26-28, 400027 Cluj-Napoca, Romania; (D.M.); (P.M.); (S.N.)
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Abstract
BACKGROUND Numerous studies have reported that contrast ultrasound (CU) can be utilized for diagnosis in patients with liver cancer (LC) accurately. However, no systematic review has addressed to assess its diagnostic impact on patients with LC. Thus, this systematic review will investigate the accurate of CU diagnosis on LC. METHODS A comprehensive literature search for relevant studies will be performed in the Cochrane Library, EMBASE, MEDILINE, Web of Science, PSYCINFO, Cumulative Index to Nursing and Allied Health Literature, Allied and Complementary Medicine Database, Chinese Biomedical Literature Database, and China National Knowledge Infrastructure from inceptions to the March 10, 2019. All case-controlled studies investigating the impacts of CU diagnosis on LC will be included in this study. Two researchers will independently carry out study selection, quality assessment, and data extraction. The quality will be assessed by using Quality Assessment of Diagnostic Accuracy Studies tool. Statistical analysis will be conducted by RevMan V.5.3 (Cochrane Community, London, UK) and Stata V.12.0 software (Stata Corp, College Station). RESULTS This study will present the accuracy of CU diagnosis for patients with LC through the assessment of sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratio of CU. CONCLUSION The findings of this study will summarize the current evidence for accuracy of CU diagnosis in patients with LC. SYSTEMATIC REVIEW REGISTRATION PROSPERO CRD42019127108.
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Affiliation(s)
- Hong-bin Guo
- Department of Ultrasound, Second Affiliated Hospital of Xi’an Medical College, Xi’an
| | - Jun-hu Wang
- Department of Ultrasound Diagnosis, Yan’an People's Hospital, Yan’an, China
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