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

NCT ID: NCT03887611

Last Updated: 2019-03-26

Study Results

Results pending

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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Recruitment Status

UNKNOWN

Total Enrollment

2000 participants

Study Classification

OBSERVATIONAL

Study Start Date

2019-01-18

Study Completion Date

2020-03-18

Brief Summary

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The application of computer-aided diagnosis (CAD) technology "S-DetectTM" enables qualitative and quantitative automated analysis of ultrasound images to obtain objective, repeatable and more accurate diagnostic results. The Elastic Contrast Factor (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-DetectTM technology for benign and malignant thyroid nodules and evaluate the consistency of ultrasound S-DetectTM technology and sonographer in the differential diagnosis of thyroid nodules and explore the differential diagnosis value of Samsung ultrasound ECI technology for benign and malignant thyroid nodules.

Detailed Description

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Thyroid nodules are one of the most common nodular lesions in adults, and their incidence is increasing. Clinical studies have found that about 10%-15% of adults have thyroid nodules, most of which are benign nodules, only 7% of thyroid nodules tend to be malignant, but if not treated promptly, 5% of patients will still from benign to malignant. The incidence of thyroid cancer increases with age. According to the National Cancer Research Center, about 64,300 new cases of thyroid cancer occurred in the world in 2016, and about 1,980 died of thyroid cancer. Timely and accurate detection of thyroid nodules and differentiation of benign and malignant are important for improving clinical treatment and improving patient prognosis.

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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Thyroid Nodule

Study Design

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Observational Model Type

CASE_ONLY

Study Time Perspective

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

Intervention Type DEVICE

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

Intervention Type DEVICE

Eligibility Criteria

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Inclusion Criteria

1. Had thyroid 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 thyroid 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.
Minimum Eligible Age

18 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

Yes

Sponsors

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Xinhua Hospital, Shanghai Jiao Tong University School of Medicine

OTHER

Sponsor Role collaborator

Wuhan Hospital of Traditional Chinese Medicine

OTHER

Sponsor Role collaborator

Macheng People's Hospital

UNKNOWN

Sponsor Role collaborator

Huangshi Central Hospital

OTHER

Sponsor Role collaborator

Affiliated Hospital of Jiangsu University

OTHER

Sponsor Role collaborator

The First People's Hospital of Yichang

UNKNOWN

Sponsor Role collaborator

Yichang Second People's Hospital

OTHER

Sponsor Role collaborator

Xiangyang Central Hospital

OTHER

Sponsor Role collaborator

The Second Hospital of Anhui Medical University

OTHER

Sponsor Role collaborator

Anqing People's Hospital

UNKNOWN

Sponsor Role collaborator

Huainan People's Hospital

UNKNOWN

Sponsor Role collaborator

Taizhou Hospital

OTHER

Sponsor Role collaborator

Wenzhou Central Hospital

OTHER

Sponsor Role collaborator

Xuzhou First People's Hospital

UNKNOWN

Sponsor Role collaborator

The Central Hospital of Lishui City

OTHER

Sponsor Role collaborator

Huai'an First People's Hospital

OTHER

Sponsor Role collaborator

WISCO General Hospital

UNKNOWN

Sponsor Role collaborator

Jiangxia District First People's Hospital

UNKNOWN

Sponsor Role collaborator

Enshi State Central Hospital

UNKNOWN

Sponsor Role collaborator

Lianyungang Third People's Hospital

UNKNOWN

Sponsor Role collaborator

First People's Hospital of Xianyang

OTHER

Sponsor Role collaborator

Xin-Wu Cui

OTHER

Sponsor Role lead

Responsible Party

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Xin-Wu Cui

Professor

Responsibility Role SPONSOR_INVESTIGATOR

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

Site Status RECRUITING

Countries

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China

Central Contacts

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Li-Qiang Zhou, MD

Role: CONTACT

15387076275

Facility Contacts

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Xin-Wu Cui, PhD,MD

Role: primary

15927103161

You-Bin Deng, PhD,MD

Role: backup

13871197838

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.

Reference Type RESULT
PMID: 28071987 (View on PubMed)

Other Identifiers

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2019(S074)

Identifier Type: -

Identifier Source: org_study_id

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