Application of Ultrasound Artificial Intelligence and Elastography in Differential Diagnosis of Breast Nodules
NCT ID: NCT03887598
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
2020-02-18
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 breast ultrasound to assist in morphological analysis based on the Breast Imaging Reporting and Data System (BI-RADS) description and final assessment.This provides a new way to identify the benign and malignant breast nodules.
The E-Breast 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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breast nodule
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 breast surgery or breast 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
FEMALE
Yes
Sponsors
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Xinhua Hospital, Shanghai Jiao Tong University School of Medicine
OTHER
Taizhou Hospital
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
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
First People's Hospital of Jiangxia District, Wuhan City
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 JH, Kang BJ, Baek JE, Lee HS, Kim SH. Application of computer-aided diagnosis in breast ultrasound interpretation: improvements in diagnostic performance according to reader experience. Ultrasonography. 2018 Jul;37(3):217-225. doi: 10.14366/usg.17046. Epub 2017 Aug 14.
Kowal M, Filipczuk P, Obuchowicz A, Korbicz J, Monczak R. Computer-aided diagnosis of breast cancer based on fine needle biopsy microscopic images. Comput Biol Med. 2013 Oct;43(10):1563-72. doi: 10.1016/j.compbiomed.2013.08.003. Epub 2013 Aug 19.
Di Segni M, de Soccio V, Cantisani V, Bonito G, Rubini A, Di Segni G, Lamorte S, Magri V, De Vito C, Migliara G, Bartolotta TV, Metere A, Giacomelli L, de Felice C, D'Ambrosio F. Automated classification of focal breast lesions according to S-detect: validation and role as a clinical and teaching tool. J Ultrasound. 2018 Jun;21(2):105-118. doi: 10.1007/s40477-018-0297-2. Epub 2018 Apr 21.
Kim K, Song MK, Kim EK, Yoon JH. Clinical application of S-Detect to breast masses on ultrasonography: a study evaluating the diagnostic performance and agreement with a dedicated breast radiologist. Ultrasonography. 2017 Jan;36(1):3-9. doi: 10.14366/usg.16012. Epub 2016 Apr 14.
Wei Q, Yan YJ, Wu GG, Ye XR, Jiang F, Liu J, Wang G, Wang Y, Song J, Pan ZP, Hu JH, Jin CY, Wang X, Dietrich CF, Cui XW. The diagnostic performance of ultrasound computer-aided diagnosis system for distinguishing breast masses: a prospective multicenter study. Eur Radiol. 2022 Jun;32(6):4046-4055. doi: 10.1007/s00330-021-08452-1. Epub 2022 Jan 23.
Other Identifiers
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2019(S073)
Identifier Type: -
Identifier Source: org_study_id
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