Development of Artificial Intelligence System for Detection and Diagnosis of Breast Lesion Using Mammography
- Conditions
- Women With Breast Cancer
- Registration Number
- NCT03708978
- Lead Sponsor
- Peking University Cancer Hospital & Institute
- Brief Summary
This project aims to establish a comprehensive artificial intelligence system for detecting and qualitative diagnosing breast lesions. Mammary images will be used to construct a diagnosis method based on deep learning. The system is proposed to automatically analyze the type of mammary glands, automatically identify and mark all breast lesions on the mammography images, provide the malignancy probability judgment of the lesions, the BI-RADS classification and the clinical suggestion, and also automatically generate the structured diagnosis report.
- Detailed Description
This is a multi-center study.The project contains a retrospective part(3000 samples anticipated) and a prospective part(7000 samples anticipated). In the retrospective part, investigators collected subjects with mammary images to design the deep learning method and construct a detective and diagnostic model for breast lesions. In the prospective part, investigators validate the accuracy of the constructed deep learning method, and established artificial intelligence system focusing on mammary diagnosis. Investigators will also explore the application pattern of the artificial intelligence system in clinical practice.
Recruitment & Eligibility
- Status
- COMPLETED
- Sex
- Female
- Target Recruitment
- 5809
- the X-ray images of the breast were complete
- the results of pathological diagnosis or more than 2 years of mammography follow-up were available
- subject signs informed consent(this item was only for prospective study cases)
- there exists pathological diagnosis of breast lesions when receiving mammography
- there lacks pathological diagnosis or 2 years of mammography follow-up
- subject withdraws(this item was only for prospective study cases)
Study & Design
- Study Type
- OBSERVATIONAL
- Study Design
- Not specified
- Primary Outcome Measures
Name Time Method benign-malignant diagnosis accuracy from the first mammography to 2-year-after mammography the accuracy of the AI model, radiogist with AI support, radiologist alone for binary diagnosis of a benign or malignant breast lesion according to follow up. If a 2-year mammography of BI-RADS 1/2/3 is obtained, the lesion is considered benign. If either one mammography of BI-RADS 4/5 during the two year is obtained,a pathological examination is performed to ensure the benign or malignant lesion
- Secondary Outcome Measures
Name Time Method lesion detection accuracy from the first mammography to radiologist diagnosis (within 3 days after the mammography taken) the detection rate of the constructed deep learning method for detecting benign or malignant breast lesion according to radiologist's subjective diagnosis or follow up as reference. If a radiologist suggests existence of a lesion at the first mammography or at each follow-up mammography during the 2-year period, it is considered that a lesion exists
Trial Locations
- Locations (8)
Beijing Cancer Hospital
🇨🇳Beijing, Beijing, China
Beijing Chao Yang Women and Children's Health Hospital
🇨🇳Beijing, Beijing, China
Beijing Da Xing People's Hospital
🇨🇳Beijing, Beijing, China
Beijing Hang Tian Centre Hospital
🇨🇳Beijing, Beijing, China
Beijing Nan Jiao Cancer Hospital
🇨🇳Beijing, Beijing, China
Beijing Shi Jing Shan Hospital
🇨🇳Beijing, Beijing, China
Beijing Shun Yi Qu Hospital
🇨🇳Beijing, Beijing, China
Beijing Shun Yi Woman and Children Health Hospital
🇨🇳Beijing, Beijing, China
Beijing Cancer Hospital🇨🇳Beijing, Beijing, China