Women With Breast CancerNCT03708978
Development of Artificial Intelligence System for Detection and Diagnosis of Breast Lesion Using Mammography
An observational study of Women With Breast Cancer, run by Peking University Cancer Hospital & Institute at 8 sites in China.
Trial summary
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. Read more about this study
- Who is it for?
- Ages 18 years and older, women only
- Conditions the study is for
- Women With Breast Cancer
- What is being tested?
- mammography
- How many people
- 5,809 participants
- Where
- 8 sitesChina
- Study dates
- 5 Apr 2018to 4 May 2020
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- Women only
- No healthy volunteers
- Adult, Older adult
Study population: Women with suspected Breast Lesion
Who may be able to join
- 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)
Who cannot join
- 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)
Trial locations and contacts
8 sites in 1 country.
8 sites in 1 country
China8 sites
Beijing Cancer Hospital
Beijing, Beijing Municipality, 100142
Beijing Chao Yang Women and Children's Health Hospital
Beijing, Beijing Municipality
Beijing Da Xing People's Hospital
Beijing, Beijing Municipality
Beijing Hang Tian Centre Hospital
Beijing, Beijing Municipality
Beijing Nan Jiao Cancer Hospital
Beijing, Beijing Municipality
Beijing Shi Jing Shan Hospital
Beijing, Beijing Municipality
Beijing Shun Yi Qu Hospital
Beijing, Beijing Municipality
Beijing Shun Yi Woman and Children Health Hospital
Beijing, Beijing Municipality
About this study
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.
Read the 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.
- Conditions
- Women With Breast Cancer
- Being tested
- mammography
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
mammographyDiagnostic test
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.
| Study group | Role | What they receive |
|---|---|---|
| mammography group | women who receives mammography because of suspected breast lesion(s) Receives: Diagnostic Test: mammography |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: 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
Measured: from the first mammography to 2-year-after mammography
Secondary outcomes (1)
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
Measured: from the first mammography to radiologist diagnosis (within 3 days after the mammography taken)
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted17 Oct 2018
- Study start5 Apr 2018
- Primary completion4 May 2020
- Study completion4 May 2020
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Women With Breast CancerStudies of the same condition, open and under way.
- More trials run by Peking University Cancer Hospital & InstituteThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Other
- Enrolment
- 5,809 participants (actual)
Sponsor and investigators
- Lead sponsor
- Peking University Cancer Hospital & Institute
- Sponsor type
- University, hospital or other
- Collaborators
- Peking University
- Responsible party
- Principal investigator, Sun Ying-Shi, Peking University Cancer Hospital & Institute
- Investigators
- Ying-Shi Sun, Professor, Study chair, Peking University Cancer Hospital & Institute
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT03708978
- Sponsor's protocol ID
- BCA-AI
- Status last verified
- Jul 2021
- First submitted
- 17 Apr 2018
- Last updated
- 27 Jul 2021
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 27 Jul 2021. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Development of Artificial Intelligence System for Detection and Diagnosis of Breast Lesion Using Mammography
- ClinicalTrials.gov ID
- NCT03708978
- Acronym / sponsor ID
- BCA-AI
- Conditions
- Women With Breast Cancer
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 5,809 participants (actual)
- Lead sponsor
- Peking University Cancer Hospital & InstituteUniversity, hospital or other
- Collaborators
- Peking University
- Study start
- 5 Apr 2018 (actual)
- Primary completion
- 4 May 2020 (actual)
- Study completion
- 4 May 2020 (actual)
- First posted
- 17 Oct 2018 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
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 design
- Observational model
- Cohort
- Time perspective
- Other
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| mammography group | – | Diagnostic Test: mammographywomen who receives mammography because of suspected breast lesion(s) |
Who may qualify
- Ages
- 18 years and older
- Sex
- Female only
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Women with suspected Breast Lesion
Key inclusion criteria (3 listed)
- 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)
Key exclusion criteria (3 listed)
- 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)
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| mammographyWhen 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. | Diagnostic test | mammography group |
Primary outcome
benign-malignant diagnosis accuracy
Time frame: from the first mammography to pathological result obtained(an average of 3 weeks if mammography BI-RADS 4 or 5 obtained)
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.
benign-malignant diagnosis accuracy
Time frame: from the first mammography to 2-year-after mammography
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
1 secondary outcome
lesion detection accuracy
Time frame: from the first mammography to radiologist diagnosis (within 3 days after the mammography taken)
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 27 Jul 2021.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Ying-Shi Sun, Professor study chair
Peking University Cancer Hospital & Institute
8 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Women With Breast Cancer, run by Peking University Cancer Hospital & Institute at 8 sites in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 3 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 27 Jul 2021. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 8 sites in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
benign-malignant diagnosis accuracy (time frame: from the first mammography to pathological result obtained(an average of 3 weeks if mammography BI-RADS 4 or 5 obtained)). The study lists 2 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 27 Jul 2021.