Peking University People's Hospital Breast Center
Trial Snapshot
- Phase
- Not Applicable
- Status
- Recruiting
- Enrollment
- 5,000
- Locations
- 2
- Primary Endpoint
- screening yield
Study Overview
Brief Summary
Use Convolutional Neural Networks Analysis for Classification of Contrast-enhancing Lesions at Multiparametric Breast MRI. Build an abbreviated protocal, and investigate whether an abbreviated protocol was suitable for breast magnetic resonance imaging screening for breast cancer in high-risk Chinese women, which can shorten the examination time and avoid enhanced imaging while ensuring the accuracy of the diagnosis.
Study Design
- Study Type
- Observational
- Observational Model
- Cohort
- Time Perspective
- Prospective
Eligibility Criteria
- Sex
- Female
- Accepts Healthy Volunteers
- No
Inclusion Criteria
- •Patients undergoing full sequence BMRI examination
- •Written informed consent and complete the clinical data questionnaire
- •Through the follow-up database, at least 6 months of follow-up results can be obtained to determine whether the diagnosis result is negative/benign/malignant; for patients who need pathological biopsy, the pathological biopsy results shall prevail to determine the lesion benign/malignant.
Exclusion Criteria
- •The breast had received radiotherapy, chemotherapy, biology and other treatments before BMRI.
- •Signs or symptoms of breast disease
- •There are contraindications for breast-enhanced MRI examinations such as allergy to contrast agents.
- •Patients during lactation or pregnancy
Outcomes
Primary Outcomes
screening yield
Time Frame: 5 years
compare the rates of detection of breast cancers in the screening of high-risk populations between the Breast MRI full sequence, contrast-enhanced and non-contrast-enhanced sequence.
Secondary Outcomes
- The accuracy of radiologists and deep learning models(5 years)
Investigators
Shu Wang
Director of Breast Center
Peking University People's Hospital
