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Alizadeh B, Behzadan AH. Scalable flood inundation mapping using deep convolutional networks and traffic signage. COMPUTATIONAL URBAN SCIENCE 2023. [DOI: 10.1007/s43762-023-00090-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/09/2023]
Abstract
AbstractFloods are one of the most prevalent and costliest natural hazards globally. The safe transit of people and goods during a flood event requires fast and reliable access to flood depth information with spatial granularity comparable to the road network. In this research, we propose to use crowdsourced photos of submerged traffic signs for street-level flood depth estimation and mapping. To this end, a deep convolutional neural network (CNN) is utilized to detect traffic signs in user-contributed photos, followed by comparing the lengths of the visible part of detected sign poles before and after the flood event. A tilt correction approach is also designed and implemented to rectify potential inaccuracy in pole length estimation caused by tilted stop signs in floodwaters. The mean absolute error (MAE) achieved for pole length estimation in pre- and post-flood photos is 1.723 and 2.846 in., respectively, leading to an MAE of 4.710 in. for flood depth estimation. The presented approach provides people and first responders with a reliable and geographically scalable solution for estimating and communicating real-time flood depth data at their locations.
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Hasan Z, Key S, Habib AR, Wong E, Aweidah L, Kumar A, Sacks R, Singh N. Convolutional Neural Networks in ENT Radiology: Systematic Review of the Literature. Ann Otol Rhinol Laryngol 2023; 132:417-430. [PMID: 35651308 DOI: 10.1177/00034894221095899] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
INTRODUCTION Convolutional neural networks (CNNs) represent a state-of-the-art methodological technique in AI and deep learning, and were specifically created for image classification and computer vision tasks. CNNs have been applied in radiology in a number of different disciplines, mostly outside otolaryngology, potentially due to a lack of familiarity with this technology within the otolaryngology community. CNNs have the potential to revolutionize clinical practice by reducing the time required to perform manual tasks. This literature search aims to present a comprehensive systematic review of the published literature with regard to CNNs and their utility to date in ENT radiology. METHODS Data were extracted from a variety of databases including PubMED, Proquest, MEDLINE Open Knowledge Maps, and Gale OneFile Computer Science. Medical subject headings (MeSH) terms and keywords were used to extract related literature from each databases inception to October 2020. Inclusion criteria were studies where CNNs were used as the main intervention and CNNs focusing on radiology relevant to ENT. Titles and abstracts were reviewed followed by the contents. Once the final list of articles was obtained, their reference lists were also searched to identify further articles. RESULTS Thirty articles were identified for inclusion in this study. Studies utilizing CNNs in most ENT subspecialties were identified. Studies utilized CNNs for a number of tasks including identification of structures, presence of pathology, and segmentation of tumors for radiotherapy planning. All studies reported a high degree of accuracy of CNNs in performing the chosen task. CONCLUSION This study provides a better understanding of CNN methodology used in ENT radiology demonstrating a myriad of potential uses for this exciting technology including nodule and tumor identification, identification of anatomical variation, and segmentation of tumors. It is anticipated that this field will continue to evolve and these technologies and methodologies will become more entrenched in our everyday practice.
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Affiliation(s)
- Zubair Hasan
- Faculty of Medicine and Health, University of Sydney, Camperdown, NSW, Australia
- Department of Otolaryngology - Head and Neck Surgery, Westmead Hospital, Westmead, NSW, Australia
| | - Seraphina Key
- Faculty of Medicine, Nursing and Health Sciences, Monash University, Clayton, VIC, Australia
| | - Al-Rahim Habib
- Faculty of Medicine and Health, University of Sydney, Camperdown, NSW, Australia
- Department of Otolaryngology - Head and Neck Surgery, Westmead Hospital, Westmead, NSW, Australia
- Department of Otolaryngology - Head and Neck Surgery, Princess Alexandra Hospital, Woolloongabba, QLD, Australia
| | - Eugene Wong
- Department of Otolaryngology - Head and Neck Surgery, Westmead Hospital, Westmead, NSW, Australia
| | - Layal Aweidah
- Faculty of Medicine, University of Notre Dame, Darlinghurst, NSW, Australia
| | - Ashnil Kumar
- School of Biomedical Engineering, Faculty of Engineering, University of Sydney, Darlington, NSW, Australia
| | - Raymond Sacks
- Faculty of Medicine and Health, University of Sydney, Camperdown, NSW, Australia
- Department of Otolaryngology - Head and Neck Surgery, Concord Hospital, Concord, NSW, Australia
| | - Narinder Singh
- Faculty of Medicine and Health, University of Sydney, Camperdown, NSW, Australia
- Department of Otolaryngology - Head and Neck Surgery, Westmead Hospital, Westmead, NSW, Australia
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Lian KM, Lin T. Diagnostic performance of the thyroid imaging reporting and data system improved by color-coded acoustic radiation force pulse imaging. JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2023; 31:511-523. [PMID: 36806542 DOI: 10.3233/xst-221359] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/19/2023]
Abstract
OBJECTIVE To explore the value of color-coded virtual touch tissue imaging (CCV) using acoustic radiation force pulse technology (ARFI) in diagnosing malignant thyroid nodules. METHODS Images including 189 thyroid nodules were collected as training samples and a binary logistic regression analysis was used to calculate regression coefficients for Thyroid Imaging Reporting and Data System (TI-RADS) and CCV. An integrated prediction model (TI-RADS+CCV) was then developed based on the regression coefficients. Another testing dataset involving 40 thyroid nodules was used to validate and compare the diagnostic performance of TI-RADS, CCV, and the integrated predictive models using the receiver operating characteristic (ROC) curves. RESULTS Both TI-RADS and CCV are independent predictors. The diagnostic performance advantage of CCV is insignificant compared to TI-RADS (P = 0.61). However, the diagnostic performance of the integrated prediction model is significantly higher than that of TI-RADS or CCV (all P < 0.05). Applying to the validation image dateset, the integrated predictive model yields an area under the curve (AUC) of 0.880. CONCLUSIONS Developing a new predictive model that integrates the regression coefficients calculated from TI-RADS and CCV enables to achieve the superior performance of thyroid nodule diagnosis to that of using TI-RADS or CCV alone.
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Affiliation(s)
- Kai-Mei Lian
- Department of Ultrasound, The First Affiliated Hospital of Shantou University Medical College, Shantou, Guangdong Province, China
| | - Teng Lin
- Department of Ultrasound, The First Affiliated Hospital of Shantou University Medical College, Shantou, Guangdong Province, China
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Zhang L, Qi Q, Li Q, Ren S, Liu S, Mao B, Li X, Wu Y, Yang L, Liu L, Li Y, Duan S, Zhang L. Ultrasomics prediction for cytokeratin 19 expression in hepatocellular carcinoma: A multicenter study. Front Oncol 2022; 12:994456. [PMID: 36119507 PMCID: PMC9478580 DOI: 10.3389/fonc.2022.994456] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Accepted: 08/10/2022] [Indexed: 11/16/2022] Open
Abstract
Objective The purpose of this study was to investigate the preoperative prediction of Cytokeratin (CK) 19 expression in patients with hepatocellular carcinoma (HCC) by machine learning-based ultrasomics. Methods We retrospectively analyzed 214 patients with pathologically confirmed HCC who received CK19 immunohistochemical staining. Through random stratified sampling (ratio, 8:2), patients from institutions I and II were divided into training dataset (n = 143) and test dataset (n = 36), and patients from institution III served as external validation dataset (n = 35). All gray-scale ultrasound images were preprocessed, and then the regions of interest were then manually segmented by two sonographers. A total of 1409 ultrasomics features were extracted from the original and derived images. Next, the intraclass correlation coefficient, variance threshold, mutual information, and embedded method were applied to feature dimension reduction. Finally, the clinical model, ultrasonics model, and combined model were constructed by eXtreme Gradient Boosting algorithm. Model performance was assessed by area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy. Results A total of 12 ultrasomics signatures were used to construct the ultrasomics models. In addition, 21 clinical features were used to construct the clinical model, including gender, age, Child-Pugh classification, hepatitis B surface antigen/hepatitis C virus antibody (positive/negative), cirrhosis (yes/no), splenomegaly (yes/no), tumor location, tumor maximum diameter, tumor number, alpha-fetoprotein, alanine aminotransferase, aspartate aminotransferase, alkaline phosphatase, glutamyl-transpeptidase, albumin, total bilirubin, conjugated bilirubin, creatinine, prothrombin time, fibrinogen, and international normalized ratio. The AUC of the ultrasomics model was 0.789 (0.621 – 0.907) and 0.787 (0.616 – 0.907) in the test and validation datasets, respectively. However, the performance of the combined model covering clinical features and ultrasomics signatures improved significantly. Additionally, the AUC (95% CI), sensitivity, specificity, and accuracy were 0.867 (0.712 – 0.957), 0.750, 0.875, 0.861, and 0.862 (0.703 – 0.955), 0.833, 0.862, and 0.857 in the test dataset and external validation dataset, respectively. Conclusion Ultrasomics signatures could be used to predict the expression of CK19 in HCC patients. The combination of clinical features and ultrasomics signatures showed excellent effects, which significantly improved prediction accuracy and robustness.
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Affiliation(s)
- Linlin Zhang
- Department of Ultrasound, Henan University People’s Hospital, Henan Provincial People’s Hospital, Zhengzhou University People’s Hospital, Zhengzhou, China
- Henan Engineering Technology Research Center of Ultrasonic Molecular Imaging and Nanotechnology, Henan Provincial People's Hospital, Zhengzhou, China
| | - Qinghua Qi
- Department of Ultrasound, First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Qian Li
- Department of Ultrasound, Henan Provincial Cancer Hospital, Zhengzhou, China
| | - Shanshan Ren
- Department of Ultrasound, Henan University People’s Hospital, Henan Provincial People’s Hospital, Zhengzhou University People’s Hospital, Zhengzhou, China
- Henan Engineering Technology Research Center of Ultrasonic Molecular Imaging and Nanotechnology, Henan Provincial People's Hospital, Zhengzhou, China
| | - Shunhua Liu
- Henan Engineering Technology Research Center of Ultrasonic Molecular Imaging and Nanotechnology, Henan Provincial People's Hospital, Zhengzhou, China
| | - Bing Mao
- Henan Engineering Technology Research Center of Ultrasonic Molecular Imaging and Nanotechnology, Henan Provincial People's Hospital, Zhengzhou, China
| | - Xin Li
- Department of Ultrasound, Henan University People’s Hospital, Henan Provincial People’s Hospital, Zhengzhou University People’s Hospital, Zhengzhou, China
- Henan Engineering Technology Research Center of Ultrasonic Molecular Imaging and Nanotechnology, Henan Provincial People's Hospital, Zhengzhou, China
| | - Yuejin Wu
- Department of Ultrasound, Henan University People’s Hospital, Henan Provincial People’s Hospital, Zhengzhou University People’s Hospital, Zhengzhou, China
- Henan Engineering Technology Research Center of Ultrasonic Molecular Imaging and Nanotechnology, Henan Provincial People's Hospital, Zhengzhou, China
| | - Lanling Yang
- Department of Ultrasound, Henan University People’s Hospital, Henan Provincial People’s Hospital, Zhengzhou University People’s Hospital, Zhengzhou, China
- Henan Engineering Technology Research Center of Ultrasonic Molecular Imaging and Nanotechnology, Henan Provincial People's Hospital, Zhengzhou, China
| | - Luwen Liu
- Department of Ultrasound, Henan University People’s Hospital, Henan Provincial People’s Hospital, Zhengzhou University People’s Hospital, Zhengzhou, China
- Henan Engineering Technology Research Center of Ultrasonic Molecular Imaging and Nanotechnology, Henan Provincial People's Hospital, Zhengzhou, China
| | - Yaqiong Li
- Henan Engineering Technology Research Center of Ultrasonic Molecular Imaging and Nanotechnology, Henan Provincial People's Hospital, Zhengzhou, China
| | - Shaobo Duan
- Henan Engineering Technology Research Center of Ultrasonic Molecular Imaging and Nanotechnology, Henan Provincial People's Hospital, Zhengzhou, China
- Department of Health Management, Henan University People’s Hospital, Henan Provincial People’s Hospital, Zhengzhou University People’s Hospital, Zhengzhou, China
- *Correspondence: Lianzhong Zhang, ; Shaobo Duan,
| | - Lianzhong Zhang
- Department of Ultrasound, Henan University People’s Hospital, Henan Provincial People’s Hospital, Zhengzhou University People’s Hospital, Zhengzhou, China
- Henan Engineering Technology Research Center of Ultrasonic Molecular Imaging and Nanotechnology, Henan Provincial People's Hospital, Zhengzhou, China
- *Correspondence: Lianzhong Zhang, ; Shaobo Duan,
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Cleere EF, Davey MG, O’Neill S, Corbett M, O’Donnell JP, Hacking S, Keogh IJ, Lowery AJ, Kerin MJ. Radiomic Detection of Malignancy within Thyroid Nodules Using Ultrasonography-A Systematic Review and Meta-Analysis. Diagnostics (Basel) 2022; 12:diagnostics12040794. [PMID: 35453841 PMCID: PMC9027085 DOI: 10.3390/diagnostics12040794] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Revised: 03/22/2022] [Accepted: 03/22/2022] [Indexed: 02/04/2023] Open
Abstract
Background: Despite investigation, 95% of thyroid nodules are ultimately benign. Radiomics is a field that uses radiological features to inform individualized patient care. We aimed to evaluate the diagnostic utility of radiomics in classifying undetermined thyroid nodules into benign and malignant using ultrasonography (US). Methods: A diagnostic test accuracy systematic review and meta-analysis was performed in accordance with PRISMA guidelines. Sensitivity, specificity, and area under curve (AUC) delineating benign and malignant lesions were recorded. Results: Seventy-five studies including 26,373 patients and 46,175 thyroid nodules met inclusion criteria. Males accounted for 24.6% of patients, while 75.4% of patients were female. Radiomics provided a pooled sensitivity of 0.87 (95% CI: 0.86−0.87) and a pooled specificity of 0.84 (95% CI: 0.84−0.85) for characterizing benign and malignant lesions. Using convolutional neural network (CNN) methods, pooled sensitivity was 0.85 (95% CI: 0.84−0.86) and pooled specificity was 0.82 (95% CI: 0.82−0.83); significantly lower than studies using non-CNN: sensitivity 0.90 (95% CI: 0.89−0.90) and specificity 0.88 (95% CI: 0.87−0.89) (p < 0.05). The diagnostic ability of radiologists and radiomics were comparable for both sensitivity (OR 0.98) and specificity (OR 0.95). Conclusions: Radiomic analysis using US provides a reproducible, reliable evaluation of undetermined thyroid nodules when compared to current best practice.
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Affiliation(s)
- Eoin F. Cleere
- The Lambe Institute for Translational Research, National University of Ireland, H91 YR71 Galway, Ireland; (M.G.D.); (A.J.L.); (M.J.K.)
- Department of Otolaryngology, Galway University Hospitals, H91 YR71 Galway, Ireland; (M.C.); (I.J.K.)
- Correspondence:
| | - Matthew G. Davey
- The Lambe Institute for Translational Research, National University of Ireland, H91 YR71 Galway, Ireland; (M.G.D.); (A.J.L.); (M.J.K.)
| | - Shane O’Neill
- Department of Breast and Endocrine Surgery, Galway University Hospitals, H91 YR71 Galway, Ireland;
| | - Mel Corbett
- Department of Otolaryngology, Galway University Hospitals, H91 YR71 Galway, Ireland; (M.C.); (I.J.K.)
| | - John P O’Donnell
- Department of Radiology, Galway University Hospitals, H91 YR71 Galway, Ireland;
| | - Sean Hacking
- Department of Pathology and Laboratory Medicine, Warren Alpert Medical School of Brown University, Providence, RI 02903, USA;
| | - Ivan J. Keogh
- Department of Otolaryngology, Galway University Hospitals, H91 YR71 Galway, Ireland; (M.C.); (I.J.K.)
| | - Aoife J. Lowery
- The Lambe Institute for Translational Research, National University of Ireland, H91 YR71 Galway, Ireland; (M.G.D.); (A.J.L.); (M.J.K.)
- Department of Breast and Endocrine Surgery, Galway University Hospitals, H91 YR71 Galway, Ireland;
| | - Michael J. Kerin
- The Lambe Institute for Translational Research, National University of Ireland, H91 YR71 Galway, Ireland; (M.G.D.); (A.J.L.); (M.J.K.)
- Department of Breast and Endocrine Surgery, Galway University Hospitals, H91 YR71 Galway, Ireland;
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Drug repurposing of adapalene for melanoma treatment. Pharm Pat Anal 2022; 11:9-14. [PMID: 35168402 DOI: 10.4155/ppa-2021-0021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Cancer drug repurposing is an attractive approach that leads to savings in time and investment. Adapalene, the first medical application of which was for the treatment of acne, has been described as a repurposing drug for the treatment of various types of cancer. Patent application CN111329851 describes the use of adapalene for the treatment of melanoma, by assays carried out on melanoma cell lines. Adapalene demonstrated antiproliferative activity in melanoma cell lines via S-phase arrest-dependent apoptosis mediated by DNA damage through an increase in the expression of p-ATM and p-chk2 and a decrease in the expression of p-BRCA1 and Rad51. Even though no evidence on efficacy and efficiency is shown in preclinical and clinical studies, CN111329851 patent shows that adapalene may be a repurposing drug for the treatment of melanoma.
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Abstract
Artificial intelligence (AI) has illuminated a clear path towards an evolving health-care system replete with enhanced precision and computing capabilities. Medical imaging analysis can be strengthened by machine learning as the multidimensional data generated by imaging naturally lends itself to hierarchical classification. In this Review, we describe the role of machine intelligence in image-based endocrine cancer diagnostics. We first provide a brief overview of AI and consider its intuitive incorporation into the clinical workflow. We then discuss how AI can be applied for the characterization of adrenal, pancreatic, pituitary and thyroid masses in order to support clinicians in their diagnostic interpretations. This Review also puts forth a number of key evaluation criteria for machine learning in medicine that physicians can use in their appraisals of these algorithms. We identify mitigation strategies to address ongoing challenges around data availability and model interpretability in the context of endocrine cancer diagnosis. Finally, we delve into frontiers in systems integration for AI, discussing automated pipelines and evolving computing platforms that leverage distributed, decentralized and quantum techniques.
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Affiliation(s)
| | - Ihab R Kamel
- Department of Imaging & Imaging Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Harrison X Bai
- Department of Imaging & Imaging Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
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Deep Learning in Cancer Diagnosis and Prognosis Prediction: A Minireview on Challenges, Recent Trends, and Future Directions. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2021; 2021:9025470. [PMID: 34754327 PMCID: PMC8572604 DOI: 10.1155/2021/9025470] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/20/2021] [Revised: 09/30/2021] [Accepted: 10/05/2021] [Indexed: 12/30/2022]
Abstract
Deep learning (DL) is a branch of machine learning and artificial intelligence that has been applied to many areas in different domains such as health care and drug design. Cancer prognosis estimates the ultimate fate of a cancer subject and provides survival estimation of the subjects. An accurate and timely diagnostic and prognostic decision will greatly benefit cancer subjects. DL has emerged as a technology of choice due to the availability of high computational resources. The main components in a standard computer-aided design (CAD) system are preprocessing, feature recognition, extraction and selection, categorization, and performance assessment. Reduction of costs associated with sequencing systems offers a myriad of opportunities for building precise models for cancer diagnosis and prognosis prediction. In this survey, we provided a summary of current works where DL has helped to determine the best models for the cancer diagnosis and prognosis prediction tasks. DL is a generic model requiring minimal data manipulations and achieves better results while working with enormous volumes of data. Aims are to scrutinize the influence of DL systems using histopathology images, present a summary of state-of-the-art DL methods, and give directions to future researchers to refine the existing methods.
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Shen YT, Chen L, Yue WW, Xu HX. Artificial intelligence in ultrasound. Eur J Radiol 2021; 139:109717. [PMID: 33962110 DOI: 10.1016/j.ejrad.2021.109717] [Citation(s) in RCA: 52] [Impact Index Per Article: 17.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2021] [Revised: 03/28/2021] [Accepted: 04/11/2021] [Indexed: 12/13/2022]
Abstract
Ultrasound (US), a flexible green imaging modality, is expanding globally as a first-line imaging technique in various clinical fields following with the continual emergence of advanced ultrasonic technologies and the well-established US-based digital health system. Actually, in US practice, qualified physicians should manually collect and visually evaluate images for the detection, identification and monitoring of diseases. The diagnostic performance is inevitably reduced due to the intrinsic property of high operator-dependence from US. In contrast, artificial intelligence (AI) excels at automatically recognizing complex patterns and providing quantitative assessment for imaging data, showing high potential to assist physicians in acquiring more accurate and reproducible results. In this article, we will provide a general understanding of AI, machine learning (ML) and deep learning (DL) technologies; We then review the rapidly growing applications of AI-especially DL technology in the field of US-based on the following anatomical regions: thyroid, breast, abdomen and pelvis, obstetrics heart and blood vessels, musculoskeletal system and other organs by covering image quality control, anatomy localization, object detection, lesion segmentation, and computer-aided diagnosis and prognosis evaluation; Finally, we offer our perspective on the challenges and opportunities for the clinical practice of biomedical AI systems in US.
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Affiliation(s)
- Yu-Ting Shen
- Department of Medical Ultrasound, Shanghai Tenth People's Hospital, Ultrasound Research and Education Institute, Tongji University School of Medicine, Tongji University Cancer Center, Shanghai Engineering Research Center of Ultrasound Diagnosis and Treatment, National Clnical Research Center of Interventional Medicine, Shanghai, 200072, PR China
| | - Liang Chen
- Department of Gastroenterology, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, 200072, PR China
| | - Wen-Wen Yue
- Department of Medical Ultrasound, Shanghai Tenth People's Hospital, Ultrasound Research and Education Institute, Tongji University School of Medicine, Tongji University Cancer Center, Shanghai Engineering Research Center of Ultrasound Diagnosis and Treatment, National Clnical Research Center of Interventional Medicine, Shanghai, 200072, PR China.
| | - Hui-Xiong Xu
- Department of Medical Ultrasound, Shanghai Tenth People's Hospital, Ultrasound Research and Education Institute, Tongji University School of Medicine, Tongji University Cancer Center, Shanghai Engineering Research Center of Ultrasound Diagnosis and Treatment, National Clnical Research Center of Interventional Medicine, Shanghai, 200072, PR China.
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