NCT03887598

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.

Trial Health

43
At Risk

Trial Health Score

Automated assessment based on enrollment pace, timeline, and geographic reach

Trial has exceeded expected completion date
Enrollment
2,000

participants targeted

Target at P75+ for all trials

Timeline
Completed

Started Jan 2019

Geographic Reach
1 country

1 active site

Status
unknown

Health score is calculated from publicly available data and should be used for screening purposes only.

Trial Relationships

Click on a node to explore related trials.

Study Timeline

Key milestones and dates

Study Start

First participant enrolled

January 18, 2019

Completed
2 months until next milestone

First Submitted

Initial submission to the registry

March 21, 2019

Completed
4 days until next milestone

First Posted

Study publicly available on registry

March 25, 2019

Completed
10 months until next milestone

Primary Completion

Last participant's last visit for primary outcome

January 18, 2020

Completed
1 month until next milestone

Study Completion

Last participant's last visit for all outcomes

February 18, 2020

Completed
Last Updated

March 26, 2019

Status Verified

March 1, 2019

Enrollment Period

1 year

First QC Date

March 21, 2019

Last Update Submit

March 23, 2019

Conditions

Breast Cancer

Keywords

Artificial Intelligence; Ultrasound; Breast nodule; ECI

Outcome Measures

Primary Outcomes (2)

  • Benign or malignant lesions as determined by pathology

    The pathological diagnosis of benign or malignant lesions from surgery samples

    Before surgery or biopsy

  • Elastic ratio

    Clear ECI value

    Before surgery or biopsy

Study Arms (1)

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

Interventions

Ultrasound diagnosisDEVICE

Ultrasound diagnosis of lesions with Samsung S-Detect and ECI technology

breast nodule

Eligibility Criteria

Age18 Years+
Sexfemale(Gender-based eligibility)
Gender Eligibility DetailsEligibility is based on gender
Healthy VolunteersYes
Age GroupsAdult (18-64), Older Adult (65+)
Sampling MethodNon-Probability Sample
Study Population

Patients with breast nodules in large tertiary hospitals

You may qualify if:

  • Had breast lesions detected by ultrasound
  • Age 18 or older
  • Upcoming FNAB or surgery
  • Signing informed consent

You may not qualify if:

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

Contact the study team to confirm eligibility.

Sponsors & Collaborators

Study Sites (1)

Xin-Wu Cui

Wuhan, Hubei, 430030, China

RECRUITING

Related Publications (5)

  • 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.

    PMID: 28992680RESULT

  • 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.

    PMID: 24034748RESULT

  • 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.

    PMID: 29681007RESULT

  • 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.

    PMID: 27184656RESULT

  • 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.

    PMID: 35066633DERIVED

MeSH Terms

Conditions

Breast Neoplasms

Interventions

Ultrasonography

Condition Hierarchy (Ancestors)

Neoplasms by SiteNeoplasmsBreast DiseasesSkin DiseasesSkin and Connective Tissue Diseases

Intervention Hierarchy (Ancestors)

Diagnostic ImagingDiagnostic Techniques and ProceduresDiagnosis

Study Officials

  • Xin-Wu Cui, PhD,MD

    Tongji Hospital

    STUDY CHAIR

  • You-Bin Deng, PhD,MD

    Tongji Hospital

    STUDY CHAIR

Central Study Contacts

Li-Qiang Zhou, MD

CONTACT

15387076275zlq_1118@hust.edu.cn

Study Design

Study Type
observational
Observational Model
CASE ONLY
Time Perspective
PROSPECTIVE
Sponsor Type
OTHER
Responsible Party
SPONSOR INVESTIGATOR
PI Title
Professor

Study Record Dates

First Submitted

March 21, 2019

First Posted

March 25, 2019

Study Start

January 18, 2019

Primary Completion

January 18, 2020

Study Completion

February 18, 2020

Last Updated

March 26, 2019

Record last verified: 2019-03

Data Sharing

IPD Sharing
Will not share

Not public because of the personal information of the participants

Locations

CN(1)