NCT03851497

Brief Summary

As the most common cancer expected to occur all over the world, breast cancer still faces with the unsatisfied diagnostic accuracy in US imaging. S-detect is a sophisticated CAD system for breast US imaging based on deep learning algorithms. E-breast is a software installed in US machines which automatically reveals tumor elastographic features. This multi-center study intends to further validate the diagnostic efficiency of S-detect and E-breast in opportunistic breast cancer screening populations in China. Our hypothesis is that S-detect and E-breast can increase the diagnostic accuracy and specificity as compared to routinely US examinations by doctors.

Trial Health

87
On Track

Trial Health Score

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

Enrollment
1,200

participants targeted

Target at P75+ for all trials

Timeline
Completed

Started Jan 2019

Geographic Reach
1 country

1 active site

Status
completed

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 1, 2019

Completed
2 months until next milestone

First Submitted

Initial submission to the registry

February 21, 2019

Completed
1 day until next milestone

First Posted

Study publicly available on registry

February 22, 2019

Completed
1.9 years until next milestone

Primary Completion

Last participant's last visit for primary outcome

January 1, 2021

Completed
Same day until next milestone

Study Completion

Last participant's last visit for all outcomes

January 1, 2021

Completed
Last Updated

March 26, 2021

Status Verified

March 1, 2021

Enrollment Period

2 years

First QC Date

February 21, 2019

Last Update Submit

March 23, 2021

Conditions

Breast Cancer

Outcome Measures

Primary Outcomes (1)

  • Benign or malignant lesions as determined by pathology

    The pathological diagnosis of benign or malignant lesions from surgery samples

    From 2019.1.1 to 2020.1.1

Eligibility Criteria

Age18 Years+
Sexfemale(Gender-based eligibility)
Gender Eligibility DetailsBiologically Female
Healthy VolunteersNo
Age GroupsAdult (18-64), Older Adult (65+)
Sampling MethodProbability Sample
Study Population

Asymptomatic female patients voluntarily asked for breast US examination in comprehensive hospitals for breast cancer screening.

You may qualify if:

  • Female over 18 years of age;
  • Had breast lesions detected by ultrasound.
  • No clinical symptoms such as nipple discharge, while breast lesions were not palpable.
  • Received breast surgery within one week of ultrasound examination.
  • Agreed to participant in this study and signed informed consent.

You may not qualify if:

  • Patients who had received a biopsy of breast lesion before the ultrasound examination.
  • Patients who were pregnant or lactating.
  • Patients who were undergoing neoadjuvant treatment.

Contact the study team to confirm eligibility.

Sponsors & Collaborators

Study Sites (1)

Peking Union Medical College Hospital

Beijing, Beijing Municipality, 100730, China

Location

MeSH Terms

Conditions

Breast Neoplasms

Condition Hierarchy (Ancestors)

Neoplasms by SiteNeoplasmsBreast DiseasesSkin DiseasesSkin and Connective Tissue Diseases

Study Design

Study Type
observational
Observational Model
COHORT
Time Perspective
PROSPECTIVE
Sponsor Type
OTHER
Responsible Party
SPONSOR

Study Record Dates

First Submitted

February 21, 2019

First Posted

February 22, 2019

Study Start

January 1, 2019

Primary Completion

January 1, 2021

Study Completion

January 1, 2021

Last Updated

March 26, 2021

Record last verified: 2021-03

Locations

CN(1)