NCT05243121招募中不适用
Peking University People's Hospital Radiology
适应症
试验速览
- 阶段
- 不适用
- 状态
- 招募中
- 入组人数
- 5,000
- 试验地点
- 1
- 主要终点
- Breast Cancer Screening
研究概览
简要总结
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.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Prospective
入排标准
- 性别
- Female
- 接受健康志愿者
- 否
入选标准
- •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
排除标准
- •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
结局指标
主要结局
Breast Cancer Screening
时间窗: 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.
次要结局
- The accuracy of radiologists and deep learning models(5 years)
- Health economics(5 years)
研究者
WangYi
Director
Peking University People's Hospital
研究点 (1)
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