Breast Neoplasms, Magnetic Resonance Imaging
Conditions
Keywords
breast neoplasms, magnetic resonance imaging, screening, deep learning, machine learning
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 mass in Chinese women, which can shorten the examination time and avoid enhanced imaging while ensuring the accuracy of the diagnosis.
Interventions
DIAGNOSTIC_TESTMRI
undergoing enhanced MRI
Sponsors
Peking University People's Hospital
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
FEMALE
Healthy volunteers
No
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
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Breast Cancer Screening | 5 years | 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. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| The accuracy of radiologists and deep learning models | 5 years | 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. |
| Health economics | 5 years | Compare the examination time, reading time and cost of BMRI full sequence, contrast-enhanced and non-contrast-enhanced sequences. |
Countries
China
Contacts
Primary ContactYi Wang, doctor
Outcome results
None listed