Breast Neoplasms Clinical Trial
Official title:
Peking University People's Hospital Radiology
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 mass in 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 with clinical symptoms (define as palpable mass, nipple discharge, asymmetric thickening or nodules, and abnormal skin changes) - Patients undergoing full sequence BMRI examination - 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. - There are contraindications for breast-enhanced MRI examinations such as allergy to contrast agents. - A prosthesis is implanted in the affected breast. - Patients during lactation or pregnancy |
Country | Name | City | State |
---|---|---|---|
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 | Breast Cancer Screening | Compare the area under the curve of the deep learning model of the BMRI full sequence, contrast-enhanced and non-contrast-enhanced sequence in the diagnosis of breast cancer. | 5 years | |
Secondary | The accuracy of radiologists and deep learning models | Under the conditions of BMRI full sequence, contrast-enhanced and non-contrast-enhanced sequences, compare the sensitivity, specificity, positive predictive value and negative predictive value of breast tumor detection by radiologists and deep learning models. | 5 years | |
Secondary | Health economics | Compare the examination time, reading time and cost of BMRI full sequence, contrast-enhanced and non-contrast-enhanced sequences. | 5 years |
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