Breast Cancer Clinical Trial
Official title:
Peking University People's Hospital Breast Center
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.
Status | Recruiting |
Enrollment | 5000 |
Est. completion date | September 30, 2025 |
Est. primary completion date | September 30, 2025 |
Accepts healthy volunteers | No |
Gender | Female |
Age group | N/A and older |
Eligibility | 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 |
Country | Name | City | State |
---|---|---|---|
China | Peking university people's hospital | Beijing | Bei Jing |
China | Peking University People's Hospital | Beijing | Beijing |
Lead Sponsor | Collaborator |
---|---|
Peking University People's Hospital |
China,
Type | Measure | Description | Time frame | Safety issue |
---|---|---|---|---|
Primary | screening yield | 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. | 5 years | |
Secondary | The accuracy of radiologists and deep learning models | compare the sensitivity,specificity, positive predictive value and negative predictive value of breast tumor detection by radiologists and deep learning models. | 5 years |
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