Breast Cancer DetectionB-MRI-AINCT07205276
AI-Based Self-Supervised Learning Model Using Non-Contrast Breast MRI for Early Screening and Clinical Utility Evaluation
A study testing Non-contrast multiparametric breast MRI with AI-based radiomics analysis and Standard radiologist reading of non-contrast multiparametric breast MRI in people with Breast Cancer Detection and Early Detection of Cancer, run by Second Affiliated Hospital, Zhejiang University, School of Medicine.
Trial summary
Breast cancer is the most common malignant disease among women worldwide, with rising incidence and younger age at onset in China. Early detection is critical for improving survival, yet current screening methods such as mammography and ultrasound show limited sensitivity in Chinese women, particularly those with… Read more about this study
- Who is it for?
- Ages 30 to 70 years, women only
- Conditions the study is for
- Breast Cancer Detection, Early Detection of Cancer, AI (Artificial Intelligence)
- What is being tested?
- Non-contrast multiparametric breast MRI with AI-based radiomics analysis and Standard radiologist reading of non-contrast multiparametric breast MRI
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 30,000 participants
- Where
- No sites listed yet
- Study dates
- 1 Oct 2025to 1 Dec 2027
- Phase
- Interventional
- Are there results?
- Not yet: the study is under way
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 30 to 70 years
- Women only
- No healthy volunteers
- Adult, Older adult
Who may be able to join
- Female, age 30-70 years
- Completed breast MRI scan, including at least T2WI, DWI, and ADC sequences
- Multimodal data acquired within the same time window (≤90 days)
- A clear clinical outcome: pathologically confirmed or ≥12-24 months of negative follow-up
- The time window between imaging examination and outcome determination was ≤90 days
- Signed informed consent
Who cannot join
- Absolute contraindications to MRI (pacemaker, cochlear implant, ocular metal foreign body, etc.)
- Pregnant or lactating women
- Recent history of breast surgery/radiotherapy (≤6 months) or imaging after neoadjuvant therapy
- Substandard image quality (severe motion artifact, signal-to-noise ratio below threshold)
- Incomplete clinical data or time window exceeded
- Known breast cancer metastasis or recurrence
Trial locations and contacts
No sites are listed yet. Contact the study team or a site near you to ask about taking part.
About this study
Official title: Construction of an Early Breast Cancer Screening Warning Model Based on Self-supervised Learning With Plain MRI Scans and Prospective Clinical Utility Evaluation
Breast cancer is the most common malignant disease among women worldwide, with rising incidence and younger age at onset in China. Early detection is critical for improving survival, yet current screening methods such as mammography and ultrasound show limited sensitivity in Chinese women, particularly those with dense breast tissue. Contrast-enhanced MRI offers higher diagnostic performance but its use is limited by high costs, safety concerns with gadolinium-based contrast agents, and limited accessibility.
This investigator-initiated trial aims to evaluate the clinical application of non-contrast multiparametric MRI, combined with advanced artificial intelligence algorithms, for the early detection and diagnosis of breast cancer. The study will collect MRI imaging data from multiple centers and integrate radiomic features across T2-weighted imaging, diffusion-weighted imaging, and apparent diffusion coefficient maps. A deep learning-based model will be developed and validated to improve lesion detection, differential diagnosis, and risk stratification.
The ultimate goal of this project is to establish a safe, accurate, and scalable breast cancer screening pathway suitable for Chinese women. By reducing dependence on invasive procedures and contrast agents, and by leveraging AI for standardization and efficiency, this approach may significantly improve early detection rates and contribute to better patient outcomes.
Read the detailed description
This is a prospective, investigator-initiated clinical study designed to evaluate the role of radiomics and artificial intelligence in non-invasive, early detection and diagnosis of breast cancer. While mammography and ultrasound are widely used as first-line screening methods, their sensitivity and specificity remain suboptimal in Chinese women, particularly in individuals with dense breast tissue. Contrast-enhanced MRI has demonstrated superior diagnostic performance, but its clinical utility is limited due to high costs, safety concerns related to gadolinium deposition, and limited availability in population-based screening programs.
To address these challenges, this study will focus on non-contrast multiparametric breast MRI, including T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and apparent diffusion coefficient (ADC) mapping. Imaging data will be prospectively collected from multiple clinical sites. A radiomics pipeline will be established to extract high-dimensional features characterizing lesion morphology, texture, and diffusion properties. Furthermore, an artificial intelligence-based model, developed using deep learning and self-supervised learning frameworks, will be trained and validated for lesion detection, classification, and risk prediction.
The primary aim of this trial is to construct and validate an imaging biomarker for early breast cancer detection based on non-contrast MRI and AI. Secondary objectives include evaluation of diagnostic accuracy compared with conventional imaging modalities, analysis of model performance across different molecular subtypes of breast cancer, and exploration of its potential application in predicting treatment response and clinical outcomes.
The expected outcome of this study is to provide robust evidence supporting the clinical feasibility of AI-guided non-contrast MRI as a safe, cost-effective, and scalable tool for early breast cancer screening in Chinese women. This work has the potential to optimize screening strategies, reduce unnecessary invasive procedures, and ultimately improve patient prognosis.
- Being tested
- Non-contrast multiparametric breast MRI with AI-based radiomics analysis, Standard radiologist reading of non-contrast multiparametric breast MRI
- Keywords
- Breast MRI, Non-contrast MRI, Radiomics, Deep Learning, Breast Cancer Screening
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Non-contrast multiparametric breast MRI with AI-based radiomics analysisDiagnostic test
Participants will receive standardized non-contrast multiparametric breast MRI scans (T2WI, DWI, ADC). Imaging features will be extracted and analyzed using artificial intelligence-based radiomics and deep learning algorithms to improve early detection and diagnosis of breast cancer.
Standard radiologist reading of non-contrast multiparametric breast MRIDiagnostic test
Imaging data interpreted by trained radiologists following routine clinical practice, without AI assistance.
| Study group | Role | What they receive |
|---|---|---|
| Breast Cancer/Suspected Cases | Experimental | Participants will undergo non-contrast multiparametric breast MRI, including T2-weighted imaging, diffusion-weighted imaging (DWI), and apparent diffusion coefficient (ADC) mapping. Imaging data will be analyzed using radiomics and AI-based algorithms for breast cancer detection and diagnosis. Receives: Diagnostic Test: Non-contrast multiparametric breast MRI with AI-based radiomics analysis |
| Standard Radiologist Reading | Active comparator | Participants undergo standardized non-contrast multiparametric breast MRI (T2WI, DWI, ADC). Imaging data are interpreted by radiologists without AI assistance, representing the current standard of care Receives: Diagnostic Test: Standard radiologist reading of non-contrast multiparametric breast MRI |
What is being measured
The primary outcome is the main question the study is designed to answer.
Diagnostic accuracy of AI-based non-contrast multiparametric MRI for breast cancer detection
Diagnostic performance of the AI-based radiomics model using non-contrast multiparametric breast MRI (T2WI, DWI, ADC) will be evaluated. The performance will be compared against the reference standard (histopathology or follow-up imaging).
Measured: Within 12 months of study enrollment
Secondary outcomes (1)
Sensitivity and specificity stratified by breast cancer molecular subtype
Evaluate model performance in subgroups defined by ER, PR, HER2, and Ki-67 status
Measured: Within 12 months of enrollment
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted3 Oct 2025
- Study start1 Oct 2025 (estimated)
- Primary completion1 Oct 2027 (estimated)
- Study completion1 Dec 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Breast Cancer DetectionStudies 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
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Diagnosis
- Allocation
- Not applicable
- Intervention model
- Parallel groups
- Model description
- All enrolled participants will undergo non-contrast multiparametric MRI. Imaging data will be analyzed using radiomics and AI-based algorithms. There is no comparator or randomization, as this is a single-arm diagnostic performance study
- Masking
- None (open label)
- Enrolment
- 30,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Second Affiliated Hospital, Zhejiang University, School of Medicine
- Sponsor type
- University, hospital or other
- Collaborators
- Alibaba DAMO Academy
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07205276
- Sponsor's protocol ID
- SAHZhejiangU-20250916
- Other IDs
- 2025-0736
- Acronym
- B-MRI-AI
- Status last verified
- Sep 2025
- First submitted
- 25 Sep 2025
- Last updated
- 3 Oct 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Yes: Individual participant data (IPD) underlying the results will be made available after publication, upon reasonable request to the corresponding investigator. De-identified MRI imaging data and associated clinical annotations will be shared through a controlled access repository.
Study at a glance
From the registry record, last updated by the sponsor on 3 Oct 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Construction of an Early Breast Cancer Screening Warning Model Based on Self-supervised Learning With Plain MRI Scans and Prospective Clinical Utility Evaluation
- ClinicalTrials.gov ID
- NCT07205276
- Acronym / sponsor ID
- B-MRI-AI / SAHZhejiangU-20250916
- Keywords
- Breast MRI, Non-contrast MRI, Radiomics, Deep Learning, Breast Cancer Screening
- Study type
- Interventional
- Recruitment status
- Not yet recruiting
- Enrolment
- 30,000 participants (estimated)
- Lead sponsor
- Second Affiliated Hospital, Zhejiang University, School of MedicineUniversity, hospital or other
- Collaborators
- Alibaba DAMO Academy
- Study start
- 1 Oct 2025 (estimated)
- Primary completion
- 1 Oct 2027 (estimated)
- Study completion
- 1 Dec 2027 (estimated)
- First posted
- 3 Oct 2025 (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.
Breast cancer is the most common malignant disease among women worldwide, with rising incidence and younger age at onset in China. Early detection is critical for improving survival, yet current screening methods such as mammography and ultrasound show limited sensitivity in Chinese women, particularly those with dense breast tissue. Contrast-enhanced MRI offers higher diagnostic performance but its use is limited by high costs, safety concerns with gadolinium-based contrast agents, and limited accessibility.
This investigator-initiated trial aims to evaluate the clinical application of non-contrast multiparametric MRI, combined with advanced artificial intelligence algorithms, for the early detection and diagnosis of breast cancer. The study will collect MRI imaging data from multiple centers and integrate radiomic features across T2-weighted imaging, diffusion-weighted imaging, and apparent diffusion coefficient maps. A deep learning-based model will be developed and validated to improve lesion detection, differential diagnosis, and risk stratification.
The ultimate goal of this project is to establish a safe, accurate, and scalable breast cancer screening pathway suitable for Chinese women. By reducing dependence on invasive procedures and contrast agents, and by leveraging AI for standardization and efficiency, this approach may significantly improve early detection rates and contribute to better patient outcomes.
Detailed description
This is a prospective, investigator-initiated clinical study designed to evaluate the role of radiomics and artificial intelligence in non-invasive, early detection and diagnosis of breast cancer. While mammography and ultrasound are widely used as first-line screening methods, their sensitivity and specificity remain suboptimal in Chinese women, particularly in individuals with dense breast tissue. Contrast-enhanced MRI has demonstrated superior diagnostic performance, but its clinical utility is limited due to high costs, safety concerns related to gadolinium deposition, and limited availability in population-based screening programs.
To address these challenges, this study will focus on non-contrast multiparametric breast MRI, including T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and apparent diffusion coefficient (ADC) mapping. Imaging data will be prospectively collected from multiple clinical sites. A radiomics pipeline will be established to extract high-dimensional features characterizing lesion morphology, texture, and diffusion properties. Furthermore, an artificial intelligence-based model, developed using deep learning and self-supervised learning frameworks, will be trained and validated for lesion detection, classification, and risk prediction.
The primary aim of this trial is to construct and validate an imaging biomarker for early breast cancer detection based on non-contrast MRI and AI. Secondary objectives include evaluation of diagnostic accuracy compared with conventional imaging modalities, analysis of model performance across different molecular subtypes of breast cancer, and exploration of its potential application in predicting treatment response and clinical outcomes.
The expected outcome of this study is to provide robust evidence supporting the clinical feasibility of AI-guided non-contrast MRI as a safe, cost-effective, and scalable tool for early breast cancer screening in Chinese women. This work has the potential to optimize screening strategies, reduce unnecessary invasive procedures, and ultimately improve patient prognosis.
Study design
- Primary purpose
- Diagnosis
- Allocation
- Not applicable
- Intervention model
- Parallel groups
- Masking
- None (open label)
- Number of arms
- 2
- Model description
- All enrolled participants will undergo non-contrast multiparametric MRI. Imaging data will be analyzed using radiomics and AI-based algorithms. There is no comparator or randomization, as this is a single-arm diagnostic performance study
| Arm | Type | Receives |
|---|---|---|
| Breast Cancer/Suspected Cases | Experimental | Diagnostic Test: Non-contrast multiparametric breast MRI with AI-based radiomics analysisParticipants will undergo non-contrast multiparametric breast MRI, including T2-weighted imaging, diffusion-weighted imaging (DWI), and apparent diffusion coefficient (ADC) mapping. Imaging data will be analyzed using radiomics and AI-based algorithms for breast cancer detection and diagnosis. |
| Standard Radiologist Reading | Active comparator | Diagnostic Test: Standard radiologist reading of non-contrast multiparametric breast MRIParticipants undergo standardized non-contrast multiparametric breast MRI (T2WI, DWI, ADC). Imaging data are interpreted by radiologists without AI assistance, representing the current standard of care |
Who may qualify
- Ages
- 30 to 70 years
- Sex
- Female only
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (6 listed)
Key exclusion criteria (6 listed)
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 |
|---|---|---|
| Non-contrast multiparametric breast MRI with AI-based radiomics analysisParticipants will receive standardized non-contrast multiparametric breast MRI scans (T2WI, DWI, ADC). Imaging features will be extracted and analyzed using artificial intelligence-based radiomics and deep learning algorithms to improve early detection and diagnosis of breast cancer. | Diagnostic test | Breast Cancer/Suspected Cases |
| Standard radiologist reading of non-contrast multiparametric breast MRIImaging data interpreted by trained radiologists following routine clinical practice, without AI assistance. | Diagnostic test | Standard Radiologist Reading |
Primary outcome
Diagnostic accuracy of AI-based non-contrast multiparametric MRI for breast cancer detection
Time frame: Within 12 months of study enrollment
Diagnostic performance of the AI-based radiomics model using non-contrast multiparametric breast MRI (T2WI, DWI, ADC) will be evaluated. The performance will be compared against the reference standard (histopathology or follow-up imaging).
1 secondary outcome
Sensitivity and specificity stratified by breast cancer molecular subtype
Time frame: Within 12 months of enrollment
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 3 Oct 2025.
- Individual participant dataSharing planned: Yes. Individual participant data (IPD) underlying the results will be made available after publication, upon reasonable request to the corresponding investigator. De-identified MRI imaging data and associated clinical annotations will be shared through a controlled access repository.
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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.
Questions about this trial
What is this clinical trial studying?
A study testing Non-contrast multiparametric breast MRI with AI-based radiomics analysis and Standard radiologist reading of non-contrast multiparametric breast MRI in people with Breast Cancer Detection and Early Detection of Cancer, run by Second Affiliated Hospital, Zhejiang University, School of Medicine.
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 6 inclusion and 6 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: opening soon, as of the sponsor's last update on 3 Oct 2025. Status can change between updates, so confirm with the study contact before making plans.
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?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Chao Ni, Doctor (+86 13989463951, drnichao@zju.edu.cn), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Diagnostic accuracy of AI-based non-contrast multiparametric MRI for breast cancer detection (time frame: Within 12 months of study enrollment). 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 3 Oct 2025.