NCT04996615招募中不适用
Peking University People's Hospital Breast Center
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
- 2
- 主要终点
- screening yield
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 cancer in high-risk 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 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.
排除标准
- •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
结局指标
主要结局
screening yield
时间窗: 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.
次要结局
- The accuracy of radiologists and deep learning models(5 years)
研究者
Shu Wang
Director of Breast Center
Peking University People's Hospital
研究点 (2)
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