Breast CancerNCT04996615
Artificial Intelligence Analysis for Magnetic Resonance Imaging in Screening Breast Cancer in High-risk Women
An observational study of Breast Cancer and Magnetic Resonance Imaging, run by Peking University People's Hospital at 2 sites in China.
Trial summary
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… Read more about this study
- Who is it for?
- Any age, women only
- Conditions the study is for
- Breast Cancer, Magnetic Resonance Imaging
- How many people
- 5,000 participants
- Where
- 2 sitesChina
- Study dates
- 1 Sep 2021to 30 Sep 2025
- 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 Any age
- Women only
- No healthy volunteers
- Child, Adult, Older adult
Study population: 23
Who may be able to join
- 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.
Who cannot join
- 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
Trial locations and contacts
2 sites in 1 country.
China2 sites, 2 recruiting
- Recruiting
Peking university people's hospital
Beijing, Beijing Municipality, 100044
Yuan peng, 13671287670, 13671287670@163.com
Shu Wang
- Recruiting
Peking University People's Hospital
Beijing, Beijing Municipality, 100044
yuan peng, MD, 86+010-88324010, 13671287670@163.com
About this study
Official title: Peking University People's Hospital Breast Center
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.
- Conditions
- Breast Cancer, Magnetic Resonance Imaging
- Being tested
- no intervention
- Keywords
- breast cancer, screening, high risk women, magnetic resonance imaging, deep learning
- Medical subject headings
- Breast Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
no interventionOther
no intervention
| Study group | Role | What they receive |
|---|---|---|
| high risk population | women at high risk of breast cancer undergoing enhanced MRI Receives: Other: no intervention |
What is being measured
The primary outcome is the main question the study is designed to answer.
screening yield
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.
Measured: 5 years
Secondary outcomes (1)
The accuracy of radiologists and deep learning models
compare the sensitivity，specificity， positive predictive value and negative predictive value of breast tumor detection by radiologists and deep learning models.
Measured: 5 years
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted9 Aug 2021
- Study start1 Sep 2021
- Primary completion30 Sep 2025 (estimated)
- Study completion30 Sep 2025 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Breast CancerStudies of the same condition, open and under way.
- More trials run by Peking University People's HospitalThe 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
- Prospective
- Enrolment
- 5,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Peking University People's Hospital
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Shu Wang, Peking University People's Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04996615
- Sponsor's protocol ID
- AI-BMRI
- Status last verified
- Nov 2022
- First submitted
- 21 Jul 2021
- Last updated
- 16 Nov 2022
- Results posted
- No
- Data monitoring committee
- No
- 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 16 Nov 2022. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Peking University People's Hospital Breast Center
- ClinicalTrials.gov ID
- NCT04996615
- Acronym / sponsor ID
- AI-BMRI
- Conditions
- Breast Cancer, Magnetic Resonance Imaging
- Keywords
- breast cancer, screening, high risk women, magnetic resonance imaging, deep learning
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 5,000 participants (estimated)
- Lead sponsor
- Peking University People's HospitalUniversity, hospital or other
- Study start
- 1 Sep 2021 (actual)
- Primary completion
- 30 Sep 2025 (estimated)
- Study completion
- 30 Sep 2025 (estimated)
- First posted
- 9 Aug 2021 (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.
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.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| high risk population | – | Other: no interventionwomen at high risk of breast cancer undergoing enhanced MRI |
Who may qualify
- Sex
- Female only
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
- Study population
- 23
Key inclusion criteria (3 listed)
- 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.
Key exclusion criteria (4 listed)
- 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
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 |
|---|---|---|
| no interventionno intervention | Other | high risk population |
Primary outcome
screening yield
Time frame: 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.
1 secondary outcome
The accuracy of radiologists and deep learning models
Time frame: 5 years
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 16 Nov 2022.
- 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.
2 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Breast Cancer and Magnetic Resonance Imaging, run by Peking University People's Hospital at 2 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 4 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: status not confirmed, as of the sponsor's last update on 16 Nov 2022. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 2 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?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact shu wang, doctor (86+010-88324010, shuwang@pkuph.edu.cn), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
screening yield (time frame: 5 years). 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 16 Nov 2022.