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

Breast Cancer Clinical Trial

Official title:

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

Clinical Trial Details — Status: Completed

Administrative data

• NCT number: NCT03851497
• Other study ID #: S-detect 2019
• Status: Completed
• Start date: January 1, 2019
• Est. completion date: January 1, 2021

Study information

• Verified date: March 2021
• Source: Peking Union Medical College Hospital
• Contact: n/a
• Is FDA regulated: No
• Study type: Observational

Clinical Trial 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.

Recruitment information / eligibility

• Status: Completed
• Enrollment: 1200
• Est. completion date: January 1, 2021
• Est. primary completion date: January 1, 2021
• Accepts healthy volunteers: No
• Gender: Female
• Age group: 18 Years and older

Eligibility

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.

Related Conditions & MeSH terms

• Breast Cancer
• Breast Neoplasms

Locations

Country	Name	City	State
China	Peking Union Medical College Hospital	Beijing	Beijing

Sponsors (41)

Lead Sponsor

Collaborator

Peking Union Medical College Hospital

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, Liaoning Cancer Hospital & Institute, Linyi Tumour Hospital, Ningxia Medical University, Peking University Aerospace Centre Hospital, Peking University Shougang Hospital, Peking University Third Hospital, Qingdao Central Hospital, Qinghai Province Cancer Hospital, Ruijin Hospital, Second Hospital of Jilin University, Shandong Jining No.1 People's Hospital, 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 Hospital of Kunming City

Country where clinical trial is conducted

China, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	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