Breast NeoplasmsNCT05243121
Artificial Intelligence Analysis for Magnetic Resonance Imaging in Screening and Diagnosis of Breast Cancer
An observational study of Breast Neoplasms and Magnetic Resonance Imaging, run by Peking University People's Hospital at 1 site 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 mass in Chinese women, which can shorten the… Read more about this study
- Who is it for?
- Any age, women only
- Conditions the study is for
- Breast Neoplasms, Magnetic Resonance Imaging
- What is being tested?
- MRI
- How many people
- 5,000 participants
- Where
- 1 siteChina
- Study dates
- 28 Feb 2022to 30 Sep 2025
- How long will I take part?
- 6 Months
- 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: Clinically symptomatic patients (defined as palpable masses, nipple discharge, asymmetric thickening or nodules, and abnormal skin changes according to the guidelines) should be examined by BMRI at the judgment of the clinician.
Who may be able to join
- Patients with clinical symptoms (define as palpable mass, nipple discharge, asymmetric thickening or nodules, and abnormal skin changes)
- Patients undergoing full sequence BMRI examination
- 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.
- There are contraindications for breast-enhanced MRI examinations such as allergy to contrast agents.
- A prosthesis is implanted in the affected breast.
- Patients during lactation or pregnancy
Trial locations and contacts
1 site in 1 country.
China1 site, 1 recruiting
- Recruiting
Peking university people's hospital
Beijing, Beijing Municipality, 100044
Yi Wang, doctor, 13331183901, wangyi@pkuph.edu.cn
About this study
Official title: Peking University People's Hospital Radiology
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 mass in Chinese women, which can shorten the examination time and avoid enhanced imaging while ensuring the accuracy of the diagnosis.
- Conditions
- Breast Neoplasms, Magnetic Resonance Imaging
- Being tested
- MRI
- Keywords
- breast neoplasms, magnetic resonance imaging, screening, deep learning, machine 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.
MRIDiagnostic test
undergoing enhanced MRI
| Study group | Role | What they receive |
|---|---|---|
| Clinically symptomatic patients | Clinically symptomatic patients (defined as palpable masses, nipple discharge, asymmetric thickening or nodules, and abnormal skin changes according to the guidelines) should be examined by BMRI at the judgment of the clinician. Receives: Diagnostic Test: MRI |
What is being measured
The primary outcome is the main question the study is designed to answer.
Breast Cancer Screening
Compare the area under the curve of the deep learning model of the BMRI full sequence, contrast-enhanced and non-contrast-enhanced sequence in the diagnosis of breast cancer.
Measured: 5 years
Secondary outcomes (2)
The accuracy of radiologists and deep learning models
Under the conditions of BMRI full sequence, contrast-enhanced and non-contrast-enhanced sequences, compare the sensitivity, specificity, positive predictive value and negative predictive value of breast tumor detection by radiologists and deep learning models.
Measured: 5 years
Health economics
Compare the examination time, reading time and cost of BMRI full sequence, contrast-enhanced and non-contrast-enhanced sequences.
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 posted16 Feb 2022
- Study start28 Feb 2022 (estimated)
- 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 NeoplasmsStudies of the same condition, open and under way.
- 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)
- Target duration
- 6 Months
Sponsor and investigators
- Lead sponsor
- Peking University People's Hospital
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, WangYi, Peking University People's Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05243121
- Sponsor's protocol ID
- AI-BMRI-S
- Status last verified
- Feb 2022
- First submitted
- 12 Sep 2021
- Last updated
- 16 Feb 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 Feb 2022. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Peking University People's Hospital Radiology
- ClinicalTrials.gov ID
- NCT05243121
- Acronym / sponsor ID
- AI-BMRI-S
- Conditions
- Breast Neoplasms, Magnetic Resonance Imaging
- Keywords
- breast neoplasms, magnetic resonance imaging, screening, deep learning, machine 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
- 28 Feb 2022 (estimated)
- Primary completion
- 30 Sep 2025 (estimated)
- Study completion
- 30 Sep 2025 (estimated)
- First posted
- 16 Feb 2022 (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 mass in 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
- Target follow-up
- 6 Months
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Clinically symptomatic patients | – | Diagnostic Test: MRIClinically symptomatic patients (defined as palpable masses, nipple discharge, asymmetric thickening or nodules, and abnormal skin changes according to the guidelines) should be examined by BMRI at the judgment of the clinician. |
Who may qualify
- Sex
- Female only
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
- Study population
- Clinically symptomatic patients (defined as palpable masses, nipple discharge, asymmetric thickening or nodules, and abnormal skin changes according to the guidelines) should be examined by BMRI at the judgment of the clinician.
Key inclusion criteria (3 listed)
- Patients with clinical symptoms (define as palpable mass, nipple discharge, asymmetric thickening or nodules, and abnormal skin changes)
- Patients undergoing full sequence BMRI examination
- 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.
- There are contraindications for breast-enhanced MRI examinations such as allergy to contrast agents.
- A prosthesis is implanted in the affected breast.
- 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 |
|---|---|---|
| MRIundergoing enhanced MRI | Diagnostic test | Clinically symptomatic patients |
Primary outcome
Breast Cancer Screening
Time frame: 5 years
Compare the area under the curve of the deep learning model of the BMRI full sequence, contrast-enhanced and non-contrast-enhanced sequence in the diagnosis of breast cancer.
2 secondary outcomes
The accuracy of radiologists and deep learning models
Time frame: 5 years
Health economics
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 Feb 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.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Breast Neoplasms and Magnetic Resonance Imaging, run by Peking University People's Hospital at 1 site 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 Feb 2022. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site 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 Yi Wang, doctor (86+010-88325813, wangyi@pkuph.edu.cn), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Breast Cancer Screening (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 Feb 2022; AzTrials last refreshed its copy on 30 Oct 2026.