Development of Artificial Intelligence System for Detection and Diagnosis of Breast Lesion Using Mammography

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.

Study Overview

• Status: Completed
• Conditions: Women With Breast Cancer
• Intervention / Treatment: Diagnostic test: mammography

Detailed Description

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.

• Study Type: Observational
• Enrollment (Actual): 5809

Contacts and Locations

Study Locations

• China
  • Beijing
    • Beijing, Beijing, China, 100142
      • Beijing Cancer Hospital
    • Beijing, Beijing, China
      • Beijing Chao Yang Women and Children's Health Hospital
    • Beijing, Beijing, China
      • Beijing Da Xing People's Hospital
    • Beijing, Beijing, China
      • Beijing Hang Tian Centre Hospital
    • Beijing, Beijing, China
      • Beijing Nan Jiao Cancer Hospital
    • Beijing, Beijing, China
      • Beijing Shi Jing Shan Hospital
    • Beijing, Beijing, China
      • Beijing Shun Yi Qu Hospital
    • Beijing, Beijing, China
      • Beijing Shun Yi Woman and Children Health Hospital

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 18 years and older (Adult, Older Adult)
• Accepts Healthy Volunteers: No
• Genders Eligible for Study: Female

• Sampling Method: Probability Sample

Study Population

Women with suspected Breast Lesion

Description

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)

Study Plan

How is the study designed?

Number of groups / cohorts

1

Cohorts and Interventions

Group / Cohort

Intervention / Treatment

mammography group; women who receives mammography because of suspected breast lesion(s)

Diagnostic test: mammography; 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.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
benign-malignant diagnosis accuracy	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.	from the first mammography to pathological result obtained(an average of 3 weeks if mammography BI-RADS 4 or 5 obtained)
benign-malignant diagnosis accuracy	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	from the first mammography to 2-year-after mammography

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
lesion detection accuracy	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	from the first mammography to radiologist diagnosis (within 3 days after the mammography taken)

Collaborators and Investigators

• Sponsor: Peking University Cancer Hospital & Institute
• Collaborators: Peking University
• Investigators: Study Chair: Ying-Shi Sun, Professor, Peking University Cancer Hospital & Institute

Study record dates

Study Major Dates

• Study Start (Actual): April 5, 2018
• Primary Completion (Actual): May 4, 2020
• Study Completion (Actual): May 4, 2020

Study Registration Dates

• First Submitted: April 17, 2018
• First Submitted That Met QC Criteria: October 12, 2018
• First Posted (Actual): October 17, 2018

Study Record Updates

• Last Update Posted (Actual): July 27, 2021
• Last Update Submitted That Met QC Criteria: July 24, 2021
• Last Verified: July 1, 2021

More Information

Terms related to this study

• Other Study ID Numbers: BCA-AI

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No