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

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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Key information

Conditions

Breast Cancer Breast Diseases Breast Neoplasms Neoplasms Neoplasms by Site Skin Diseases Skin and Connective Tissue Diseases

Age range

18 year and older

Sex eligibility

Female

Study type

Observational

Primary location

Peking Union Medical College Hospital

Beijing, Beijing Municipality, 100730, China

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

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.

Treatment and study plan

Primary outcomes

Benign or malignant lesions as determined by pathology

Time frame: From 2019.1.1 to 2020.1.1

The pathological diagnosis of benign or malignant lesions from surgery samples

Sponsors and collaborators

Lead sponsor

Peking Union Medical College Hospital

Other

Collaborators

Beijing Anzhen Community Health Service Center
Beijing Chao Yang Hospital
Beijing Hospital
Beijing Zhongguancun Hospital
Chengde Central Hospital
First Hospital of China Medical University
First Hospital of Shijiazhuang City
First Hospital of Tsinghua University
Fudan University
Gansu Cancer Hospital
Gansu Jiugang Hospital
Henan Cancer Hospital
Henan Provincial People's Hospital
Jiangsu Province People's Hospital
Jining First People's Hospital
Liaoning Cancer Hospital & Institute
Linyi Tumour Hospital
Ningxia Medical University
Peking University Aerospace Center Hospital
Peking University Shougang Hospital
Peking University Third Hospital
Qingdao Central Hospital
Qinghai Province Fifth People's Hospital
Ruijin Hospital
Second Hospital of Jilin University
Shanghai Zhongshan Hospital
Shengjing Hospital
Sichuan Provincial People's Hospital
Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
The Affiliated Hospital of Qingdao University
The First Affiliated Hospital of Shanxi Medical University
The First Affiliated Hospital of Zhengzhou University
The Second Affiliated Hospital of Harbin Medical University
The Second Hospital of the West Coast New Area of Qingdao
Third Affiliated Hospital of Zhengzhou University
Tongji Hospital
West China Hospital
Xi'an Central Hospital
Xinxiang Central Hospital
Yan'an Affiliated Hospital of Kunming Medical University

Registry information

Official study title

A Multi-center Study of Deep Learning Diagnosis and Ultrasound Elastography in Opportunistic Screening of Breast Cancer

Important dates

Study start: 2019
Primary completion: 2021
Study completion: 2021
First posted: Feb 22, 2019
Registry last updated: Mar 26, 2021

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

Published trials that share one or more normalized conditions with this study.

Completed