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

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.

Conditions

• Breast Cancer

Sponsors & Collaborators

• Peking University Third Hospital

  collaborator OTHER

• Beijing Hospital

  collaborator OTHER_GOV

• Beijing Chao Yang Hospital

  collaborator OTHER

• Beijing Zhongguancun Hospital

  collaborator UNKNOWN

• Peking University Aerospace Center Hospital

  collaborator OTHER

• Beijing Anzhen Community Health Service Center

  collaborator UNKNOWN

• First Hospital of Tsinghua University

  collaborator OTHER

• Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University

  collaborator OTHER

• The First Affiliated Hospital of Zhengzhou University

  collaborator OTHER

• Henan Provincial People's Hospital

  collaborator OTHER

• Third Affiliated Hospital of Zhengzhou University

  collaborator OTHER

• Xinxiang Central Hospital

  collaborator OTHER

• Henan Cancer Hospital

  collaborator OTHER_GOV

• First Hospital of China Medical University

  collaborator OTHER

• Shengjing Hospital

  collaborator OTHER

• Liaoning Cancer Hospital & Institute

  collaborator OTHER

• West China Hospital

  collaborator OTHER

• Sichuan Provincial People's Hospital

  collaborator OTHER

• Yan'an Hospital of Kunming City

  collaborator UNKNOWN

• Xi'an Central Hospital

  collaborator OTHER

• Ningxia Medical University

  collaborator OTHER

• First Hospital of Shijiazhuang City

  collaborator OTHER

• Chengde Central Hospital

  collaborator OTHER_GOV

• Qinghai Province Cancer Hospital

  collaborator UNKNOWN

• Gansu Cancer Hospital

  collaborator OTHER

• Shanghai Zhongshan Hospital

  collaborator OTHER

• Ruijin Hospital

  collaborator OTHER

• The Affiliated Hospital of Qingdao University

  collaborator OTHER

• Qingdao Central Hospital

  collaborator OTHER

• Jining First People's Hospital

  collaborator OTHER

• Linyi Tumour Hospital

  collaborator OTHER

• The First Affiliated Hospital of Shanxi Medical University

  collaborator OTHER

• The Second Affiliated Hospital of Harbin Medical University

  collaborator OTHER

• Second Hospital of Jilin University

  collaborator OTHER

• The Second Hospital of the West Coast New Area of Qingdao

  collaborator UNKNOWN

• Fudan University

  collaborator OTHER

• Tongji Hospital

  collaborator OTHER

• Jiangsu Province People's Hospital

  collaborator UNKNOWN

• Peking University Shougang Hospital

  collaborator OTHER

• Gansu Jiugang Hospital

  collaborator UNKNOWN

• Peking Union Medical College Hospital

  lead OTHER

Eligibility

Min Age

18 Years

Sex

FEMALE

Healthy Volunteers

No

Timeline & Regulatory

Start

2019-01-01

Primary Completion

2021-01-01

Completion

2021-01-01

Countries

• China

Study Locations