Development of Artificial Intelligence System for Detection and Diagnosis of Breast Lesion Using Mammography
2021年7月24日 更新者：Sun Ying-Shi、Peking University Cancer Hospital & Institute
This project aims to establish a comprehensive artificial intelligence system for detecting and qualitative diagnosing breast lesions.
Mammary images will be used to construct a diagnosis method based on deep learning.
The system is proposed to automatically analyze the type of mammary glands, automatically identify and mark all breast lesions on the mammography images, provide the malignancy probability judgment of the lesions, the BI-RADS classification and the clinical suggestion, and also automatically generate the structured diagnosis report.
研究概览
详细说明
This is a multi-center study.The project contains a retrospective part(3000 samples anticipated) and a prospective part(7000 samples anticipated).
In the retrospective part, investigators collected subjects with mammary images to design the deep learning method and construct a detective and diagnostic model for breast lesions.
In the prospective part, investigators validate the accuracy of the constructed deep learning method, and established artificial intelligence system focusing on mammary diagnosis.
Investigators will also explore the application pattern of the artificial intelligence system in clinical practice.
研究类型
观察性的
注册 (实际的)
5809
联系人和位置
本节提供了进行研究的人员的详细联系信息，以及有关进行该研究的地点的信息。
学习地点
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Beijing
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Beijing、Beijing、中国、100142
- Beijing Cancer Hospital
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Beijing、Beijing、中国
- Beijing Chao Yang Women and Children's Health Hospital
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Beijing、Beijing、中国
- Beijing Da Xing People's Hospital
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Beijing、Beijing、中国
- Beijing Hang Tian Centre Hospital
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Beijing、Beijing、中国
- Beijing Nan Jiao Cancer Hospital
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Beijing、Beijing、中国
- Beijing Shi Jing Shan Hospital
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Beijing、Beijing、中国
- Beijing Shun Yi Qu Hospital
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Beijing、Beijing、中国
- Beijing Shun Yi Woman and Children Health Hospital
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-
参与标准
研究人员寻找符合特定描述的人，称为资格标准。这些标准的一些例子是一个人的一般健康状况或先前的治疗。
资格标准
适合学习的年龄
18年 及以上 (成人、年长者)
接受健康志愿者
不
有资格学习的性别
女性
取样方法
概率样本
研究人群
Women with suspected Breast Lesion
描述
Inclusion Criteria:
- the X-ray images of the breast were complete
- the results of pathological diagnosis or more than 2 years of mammography follow-up were available
- subject signs informed consent(this item was only for prospective study cases)
Exclusion Criteria:
- there exists pathological diagnosis of breast lesions when receiving mammography
- there lacks pathological diagnosis or 2 years of mammography follow-up
- subject withdraws(this item was only for prospective study cases)
学习计划
本节提供研究计划的详细信息，包括研究的设计方式和研究的衡量标准。
研究是如何设计的？
设计细节
队列和干预
团体/队列 |
干预/治疗 |
|---|---|
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mammography group
women who receives mammography because of suspected breast lesion(s)
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When a woman comes to the clinic to receive mammography.
Then a radiologist will give a BI-RADS classification after reviewing the images.
If a BI-RADS 4/5 is obtained, the woman will receive pathological biopsy to ensure there is a benign or malignant lesion.
If a BI-RADS 3 is obtained, the woman will be followed up by a half-year interval until two year after the first mammography.
At each follow up, she will receive mammography.
If a BI-RADS 4/5 is obtained at follow up, she will receive pathological biopsy; if a BI-RADS 1/2/3 is obtained at follow up, she will be followed up by a half-year interval until two year.
If a BI-RADS 1/2 is obtained at the first mammography, the woman will receive a second mammography after two year.
During the study period, breast examination and results will be recorded for every subject.
Radiologists will give the diagnosis with and without AI support.
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研究衡量的是什么？
主要结果指标
结果测量 |
措施说明 |
大体时间 |
|---|---|---|
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benign-malignant diagnosis accuracy
大体时间：from the first mammography to pathological result obtained(an average of 3 weeks if mammography BI-RADS 4 or 5 obtained)
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the accuracy of the AI model, radiogist with AI support, radiologist alone for binary diagnosis of a benign or malignant breast lesion according to pathology.
If either one mammography of BI-RADS 4/5 in the first examination or during the two year' follow up examination is obtained,a pathological examination is performed, the lesion is judged benign or malignant according to pathological results.
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from the first mammography to pathological result obtained(an average of 3 weeks if mammography BI-RADS 4 or 5 obtained)
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benign-malignant diagnosis accuracy
大体时间：from the first mammography to 2-year-after mammography
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the accuracy of the AI model, radiogist with AI support, radiologist alone for binary diagnosis of a benign or malignant breast lesion according to follow up.
If a 2-year mammography of BI-RADS 1/2/3 is obtained, the lesion is considered benign.
If either one mammography of BI-RADS 4/5 during the two year is obtained,a pathological examination is performed to ensure the benign or malignant lesion
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from the first mammography to 2-year-after mammography
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次要结果测量
结果测量 |
措施说明 |
大体时间 |
|---|---|---|
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lesion detection accuracy
大体时间：from the first mammography to radiologist diagnosis (within 3 days after the mammography taken)
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the detection rate of the constructed deep learning method for detecting benign or malignant breast lesion according to radiologist's subjective diagnosis or follow up as reference.
If a radiologist suggests existence of a lesion at the first mammography or at each follow-up mammography during the 2-year period, it is considered that a lesion exists
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from the first mammography to radiologist diagnosis (within 3 days after the mammography taken)
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合作者和调查者
在这里您可以找到参与这项研究的人员和组织。
调查人员
- 学习椅：Ying-Shi Sun, Professor、Peking University Cancer Hospital & Institute
研究记录日期
这些日期跟踪向 ClinicalTrials.gov 提交研究记录和摘要结果的进度。研究记录和报告的结果由国家医学图书馆 (NLM) 审查，以确保它们在发布到公共网站之前符合特定的质量控制标准。
研究主要日期
学习开始 (实际的)
2018年4月5日
初级完成 (实际的)
2020年5月4日
研究完成 (实际的)
2020年5月4日
研究注册日期
首次提交
2018年4月17日
首先提交符合 QC 标准的
2018年10月12日
首次发布 (实际的)
2018年10月17日
研究记录更新
最后更新发布 (实际的)
2021年7月27日
上次提交的符合 QC 标准的更新
2021年7月24日
最后验证
2021年7月1日
更多信息
与本研究相关的术语
其他研究编号
- BCA-AI
计划个人参与者数据 (IPD)
计划共享个人参与者数据 (IPD)？
不
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研究美国 FDA 监管的药品
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