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Application of Deep-learning and Ultrasound Elastography in Opportunistic Screening of Breast Cancer

Last Updated: 2021-03-26

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

COMPLETED

Total Enrollment

1200 participants

Study Classification

OBSERVATIONAL

Study Start Date

2019-01-01

Study Completion Date

2021-01-01

Brief Summary

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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.

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Detailed Description

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Conditions

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Breast Cancer

Study Design

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Observational Model Type

COHORT

Study Time Perspective

PROSPECTIVE

Eligibility Criteria

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Inclusion Criteria

* 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.

Exclusion Criteria

* 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.

Minimum Eligible Age

18 Years

Eligible Sex

FEMALE

Accepts Healthy Volunteers