Application of Ultrasound Artificial Intelligence and Elastography in Differential Diagnosis of Thyroid Nodules
NCT ID: NCT03887611
Last Updated: 2019-03-26
Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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UNKNOWN
2000 participants
OBSERVATIONAL
2019-01-18
2020-03-18
Brief Summary
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Detailed Description
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S-Detect technology is a computer-aided (CAD) system recently developed by Samsung Medical Center for thyroid ultrasound to assist in morphological analysis based on the Thyroid Imaging Reporting and Data System (TI-RADS) description and final assessment.This provides a new way to identify the benign and malignant thyroid nodules.
The ECI technique, unlike conventional strain-elastic imaging technology, performs an elastic analysis of the entire two-dimensional image. Moreover, when measuring the elastic ratio, it is only necessary to place a region of interest (ROI) at the nodule. Compared with the average elasticity of the surrounding area, it is more reflective of the elastic ratio of the mass to the surrounding tissue.
Conditions
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Study Design
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CASE_ONLY
PROSPECTIVE
Study Groups
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thyroid nodules
Those with one or more breast nodules, age 18 or older, upcoming FNAB or surgery and signed informed consent.Those without adverse effects on the test or threatening other candidates, such as mental illness, pregnancy, poor ultrasound image quality, history of thyroid surgery or thyroid biopsy, simple cystic nodules, calcification, excessive mass or too small, the S-DetectTM system can not identify the boundary of the tumor, the basic information is incomplete.
Ultrasound diagnosis
Ultrasound diagnosis of lesions with Samsung S-Detect and ECI technology
Interventions
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Ultrasound diagnosis
Ultrasound diagnosis of lesions with Samsung S-Detect and ECI technology
Eligibility Criteria
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Inclusion Criteria
2. Age 18 or older
3. Upcoming FNAB or surgery
4. Signing informed consent
Exclusion Criteria
2. Can not cooperate with the test operation
3. Patients who were pregnant or lactating
4. Patients who were undergoing neoadjuvant treatment.
18 Years
ALL
Yes
Sponsors
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Xinhua Hospital, Shanghai Jiao Tong University School of Medicine
OTHER
Wuhan Hospital of Traditional Chinese Medicine
OTHER
Macheng People's Hospital
UNKNOWN
Huangshi Central Hospital
OTHER
Affiliated Hospital of Jiangsu University
OTHER
The First People's Hospital of Yichang
UNKNOWN
Yichang Second People's Hospital
OTHER
Xiangyang Central Hospital
OTHER
The Second Hospital of Anhui Medical University
OTHER
Anqing People's Hospital
UNKNOWN
Huainan People's Hospital
UNKNOWN
Taizhou Hospital
OTHER
Wenzhou Central Hospital
OTHER
Xuzhou First People's Hospital
UNKNOWN
The Central Hospital of Lishui City
OTHER
Huai'an First People's Hospital
OTHER
WISCO General Hospital
UNKNOWN
Jiangxia District First People's Hospital
UNKNOWN
Enshi State Central Hospital
UNKNOWN
Lianyungang Third People's Hospital
UNKNOWN
First People's Hospital of Xianyang
OTHER
Xin-Wu Cui
OTHER
Responsible Party
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Xin-Wu Cui
Professor
Principal Investigators
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Xin-Wu Cui, PhD,MD
Role: STUDY_CHAIR
Tongji Hospital
You-Bin Deng, PhD,MD
Role: STUDY_CHAIR
Tongji Hospital
Locations
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Xin-Wu Cui
Wuhan, Hubei, China
Countries
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Central Contacts
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Facility Contacts
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References
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Choi YJ, Baek JH, Park HS, Shim WH, Kim TY, Shong YK, Lee JH. A Computer-Aided Diagnosis System Using Artificial Intelligence for the Diagnosis and Characterization of Thyroid Nodules on Ultrasound: Initial Clinical Assessment. Thyroid. 2017 Apr;27(4):546-552. doi: 10.1089/thy.2016.0372. Epub 2017 Feb 28.
Other Identifiers
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2019(S074)
Identifier Type: -
Identifier Source: org_study_id
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