Application of Deep-learning and Ultrasound Elastography in Opportunistic Screening of Breast Cancer
- Conditions
- Breast Cancer
- Registration Number
- NCT03851497
- Lead Sponsor
- Peking Union Medical College Hospital
- Brief 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.
- Detailed Description
Not available
Recruitment & Eligibility
- Status
- COMPLETED
- Sex
- Female
- Target Recruitment
- 1200
- 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.
- 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 & Design
- Study Type
- OBSERVATIONAL
- Study Design
- Not specified
- Primary Outcome Measures
Name Time Method Benign or malignant lesions as determined by pathology From 2019.1.1 to 2020.1.1 The pathological diagnosis of benign or malignant lesions from surgery samples
- Secondary Outcome Measures
Name Time Method
Trial Locations
- Locations (1)
Peking Union Medical College Hospital
🇨🇳Beijing, Beijing, China