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Application of Ultrasound Artificial Intelligence and Elastography in Differential Diagnosis of Breast Nodules

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ClinicalTrials.gov Identifier: NCT03887598
Recruitment Status : Recruiting
First Posted : March 25, 2019
Last Update Posted : March 26, 2019
Sponsor:
Collaborators:
Xinhua Hospital, Shanghai Jiao Tong University School of Medicine
Taizhou Hospital
Wuhan Hospital of Traditional Chinese and Western Medicine
Macheng People's Hospital
Huangshi Central Hospital
Affiliated Hospital of Jiangsu University
The First People's Hospital of Yichang
The Second People's Hospital of Yichang
Xiangyang Central Hospital
the Second Affiliated Hospital of Anhui Medical University
Anqing People's Hospital
Huainan People's Hospital
Wenzhou Central Hospital
Xuzhou First People's Hospital
The Central Hospital of Lishui City
Huai'an First People's Hospital
WISCO General Hospital
First People's Hospital of Jiangxia District, Wuhan City
Enshi State Central Hospital
Lianyungang Third People's Hospital
Xiangyang First People's Hospital
Information provided by (Responsible Party):
Xin-Wu Cui, Tongji Hospital

Brief Summary:
The application of computer-aided diagnosis (CAD) technology "S-Detect" enables qualitative and quantitative automated analysis of ultrasound images to obtain objective, repeatable and more accurate diagnostic results. The Elastic Contrast Index (ECI) technique, unlike conventional strain-elastic imaging techniques, can evaluate the elastic distribution in the region of interest. The purpose of the study was to evaluate the differential diagnosis value of ultrasound S-Detect technology for benign and malignant breast nodules and evaluate the differential diagnosis consistency of the ultrasound S-Detect technique and the examiner for benign and malignant breast nodules and explore the differential diagnosis value of Samsung ultrasound elastic contrast Index (ECI) technique for benign and malignant breast nodules.

Condition or disease Intervention/treatment
Breast Cancer Device: Ultrasound diagnosis

Detailed Description:

Breast cancer is the most common malignancy in women and the second leading cause of cancer deaths worldwide. Therefore, early detection of breast cancer and timely treatment are of great significance for controlling and reducing breast cancer mortality. Breast ultrasound is an adjunct to extensive use in the detection of breast cancer, but ultrasound is highly technically dependent on the examiner, and the results are greatly influenced by the subjective nature of the examiner, adding unnecessary surgery and puncture, which causes great problems for clinicians and patients.Moreover, the value of conventional ultrasound in the differential diagnosis of breast mass is still limited, and the emergence of new technologies such as artificial intelligence and elastography has improved the accuracy of ultrasound diagnosis to varying degrees.

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.


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Study Type : Observational
Estimated Enrollment : 2000 participants
Observational Model: Case-Only
Time Perspective: Prospective
Official Title: A Multi-center Study of Differential Diagnosis Breast Nodules by Ultrasound Artificial Intelligence and Ultrasound Elastography
Actual Study Start Date : January 18, 2019
Estimated Primary Completion Date : January 18, 2020
Estimated Study Completion Date : February 18, 2020

Resource links provided by the National Library of Medicine

MedlinePlus related topics: Ultrasound

Group/Cohort Intervention/treatment
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.
Device: Ultrasound diagnosis
Ultrasound diagnosis of lesions with Samsung S-Detect and ECI technology




Primary Outcome Measures :
  1. Benign or malignant lesions as determined by pathology [ Time Frame: Before surgery or biopsy ]
    The pathological diagnosis of benign or malignant lesions from surgery samples

  2. Elastic ratio [ Time Frame: Before surgery or biopsy ]
    Clear ECI value



Information from the National Library of Medicine

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Ages Eligible for Study:   18 Years and older   (Adult, Older Adult)
Sexes Eligible for Study:   Female
Gender Based Eligibility:   Yes
Gender Eligibility Description:   Eligibility is based on gender
Accepts Healthy Volunteers:   Yes
Sampling Method:   Non-Probability Sample
Study Population
Patients with breast nodules in large tertiary hospitals
Criteria

Inclusion Criteria:

  1. Had breast lesions detected by ultrasound
  2. Age 18 or older
  3. Upcoming FNAB or surgery
  4. Signing informed consent

Exclusion Criteria:

  1. Patients who had received a biopsy of breast lesion before the ultrasound examination
  2. Can not cooperate with the test operation
  3. Patients who were pregnant or lactating
  4. Patients who were undergoing neoadjuvant treatment.

Information from the National Library of Medicine

To learn more about this study, you or your doctor may contact the study research staff using the contact information provided by the sponsor.

Please refer to this study by its ClinicalTrials.gov identifier (NCT number): NCT03887598


Contacts
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Contact: Li-Qiang Zhou, MD 15387076275 zlq_1118@hust.edu.cn

Locations
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China, Hubei
Xin-Wu Cui Recruiting
Wuhan, Hubei, China, 430030
Contact: Xin-Wu Cui, PhD,MD    15927103161    cuixinwu@live.cn   
Contact: You-Bin Deng, PhD,MD    13871197838    ybdeng2007@hotmail.com   
Sponsors and Collaborators
Xin-Wu Cui
Xinhua Hospital, Shanghai Jiao Tong University School of Medicine
Taizhou Hospital
Wuhan Hospital of Traditional Chinese and Western Medicine
Macheng People's Hospital
Huangshi Central Hospital
Affiliated Hospital of Jiangsu University
The First People's Hospital of Yichang
The Second People's Hospital of Yichang
Xiangyang Central Hospital
the Second Affiliated Hospital of Anhui Medical University
Anqing People's Hospital
Huainan People's Hospital
Wenzhou Central Hospital
Xuzhou First People's Hospital
The Central Hospital of Lishui City
Huai'an First People's Hospital
WISCO General Hospital
First People's Hospital of Jiangxia District, Wuhan City
Enshi State Central Hospital
Lianyungang Third People's Hospital
Xiangyang First People's Hospital
Investigators
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Study Chair: Xin-Wu Cui, PhD,MD Tongji Hospital
Study Chair: You-Bin Deng, PhD,MD Tongji Hospital

Publications of Results:
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Responsible Party: Xin-Wu Cui, Professor, Tongji Hospital
ClinicalTrials.gov Identifier: NCT03887598     History of Changes
Other Study ID Numbers: 2019(S073)
First Posted: March 25, 2019    Key Record Dates
Last Update Posted: March 26, 2019
Last Verified: March 2019
Individual Participant Data (IPD) Sharing Statement:
Plan to Share IPD: No
Plan Description: Not public because of the personal information of the participants

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Studies a U.S. FDA-regulated Drug Product: No
Studies a U.S. FDA-regulated Device Product: Yes
Device Product Not Approved or Cleared by U.S. FDA: No
Pediatric Postmarket Surveillance of a Device Product: No
Product Manufactured in and Exported from the U.S.: Yes

Keywords provided by Xin-Wu Cui, Tongji Hospital:
Artificial Intelligence; Ultrasound; Breast nodule; ECI