Breast Neoplasms
Conditions
Brief summary
This multi-center study intends to evaluate the value of the detection and differential diagnosis of breast mass using deep learning AI-based real-time ultrasound examination.
Detailed description
As the most common cancer expected to occur all over the world, extensive population screening plays a very important role in the early diagnosis and prognosis of the breast cancer. X-ray and ultrasound are the most commonly used screening methods, and ultrasound is especially important for Asian women with dense breasts. However, ultrasound is greatly affected by the operator's skill and experience, and the diagnostic accuracy varies greatly. Artificial intelligence (AI) is a new method emerging in recent years, active in many medical fields and can effectively improve the diagnostic efficiency. However, previous researches on the application of AI in ultrasound are focused on single or multi-modality static ultrasound images. This multi-center study intends to evaluate the value of the detection and differential diagnosis of breast mass using deep learning AI-based real-time ultrasound examination.
Interventions
DEVICEYizhun BUSMS
During the breast scanning, Yizhun BUSMS uses different color box to identify the breast lesion, and the box color indicates the risk grade of the lesion.
Sponsors
Peking Union Medical College Hospital
Peking University Cancer Hospital & Institute
Peking University Third Hospital
Guangdong Provincial Hospital of Traditional Chinese Medicine
Third Affiliated Hospital of Zhengzhou University
Hebei Medical University Fourth Hospital
Henan Cancer Hospital
Shanxi Province Cancer Hospital
First Affiliated Hospital Xi'an Jiaotong University
Chongqing University Cancer Hospital
Anqing Hospital affiliated to Anhui Medical University
Qinhuangdao Maternal and Child Health Care Hospital
The First Affiliated Hospital of Xiamen University
Anyang Tumor Hospital
The Third Affiliated Hospital of Jinzhou Medical University
General Hospital of Jincheng Coal Industry Group
Suzhou First People's Hospital
Peking University
Cancer Institute and Hospital, Chinese Academy of Medical Sciences
Study design
Observational model
OTHER
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
FEMALE
Healthy volunteers
No
Inclusion criteria
1. Females who undergo ultrasound examination for a complaint of breast lesion; 2. The breast lesion that will obtain definite pathological diagnosis or follow-up at least two years.
Exclusion criteria
1. The breast lesion that has received CNB or FNA; 2. The breast cancer patient who has received neoadjuvant chemotherapy.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Diagnostic performance of breast mass using deep learning AI-based real-time ultrasound examination | 12 months | Pathology as a gold standard, to evaluate the diagnostic performance (sensitivity, specificity and accuracy) |
Countries
China
Contacts
Primary ContactYong Wang
Outcome results
None listed