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

A Multi-center Study of Deep Learning Diagnosis 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.

Study Overview

• Status: Completed
• Conditions: Breast Cancer

• Study Type: Observational
• Enrollment (Actual): 1200

Contacts and Locations

Study Locations

• China
  • Beijing
    • Beijing, Beijing, China, 100730
      • Peking Union Medical College Hospital

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 18 years and older (ADULT, OLDER_ADULT)
• Accepts Healthy Volunteers: No
• Genders Eligible for Study: Female

• Sampling Method: Probability Sample

Study Population

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

Description

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.

Study Plan

How is the study designed?

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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

Collaborators and Investigators

• Sponsor: Peking Union Medical College Hospital
• Collaborators: Xi'an Central Hospital; Fudan University; Shanghai Zhongshan Hospital; Peking University Third Hospital; Henan Cancer Hospital; West China Hospital; Tongji Hospital; Shengjing Hospital; The Affiliated Hospital of Qingdao University; Peking University Shougang Hospital; Ruijin Hospital; Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University; Beijing Chao Yang Hospital; Beijing Hospital; The First Affiliated Hospital of Zhengzhou University; Henan Provincial People's Hospital; First Hospital of China Medical University; The Second Affiliated Hospital of Harbin Medical University; Ningxia Medical University; The First Affiliated Hospital of Shanxi Medical University; First Hospital of Shijiazhuang City; Sichuan Provincial People's Hospital; Second Hospital of Jilin University; Qingdao Central Hospital; Chengde Central Hospital; Shandong Jining No.1 People's Hospital; Third Affiliated Hospital of Zhengzhou University; Gansu Cancer Hospital; First Hospital of Tsinghua University; Liaoning Cancer Hospital & Institute; Xinxiang Central Hospital; Peking University Aerospace Centre Hospital; Yan'an Hospital of Kunming City; Linyi Tumour Hospital; Beijing Zhongguancun Hospital; Beijing Anzhen Community Health Service Center; Qinghai Province Cancer Hospital; The Second Hospital of the West Coast New Area of Qingdao; Jiangsu Province People's Hospital; Gansu Jiugang Hospital

Study record dates

Study Major Dates

• Study Start (ACTUAL): January 1, 2019
• Primary Completion (ACTUAL): January 1, 2021
• Study Completion (ACTUAL): January 1, 2021

Study Registration Dates

• First Submitted: February 21, 2019
• First Submitted That Met QC Criteria: February 21, 2019
• First Posted (ACTUAL): February 22, 2019

Study Record Updates

• Last Update Posted (ACTUAL): March 26, 2021
• Last Update Submitted That Met QC Criteria: March 23, 2021
• Last Verified: March 1, 2021

More Information

Terms related to this study

• Additional Relevant MeSH Terms: Skin Diseases; Neoplasms; Neoplasms by Site; Breast Diseases; Breast Neoplasms
• Other Study ID Numbers: S-detect 2019

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No