Breast NeoplasmsNCT07598084
Non-Contrast Breast MRI Diagnosis and Risk Stratification Using DWI-Generated Synthetic Contrast Enhancement
An observational study of Breast Neoplasms and Artificial Intelligence (AI), run by Peking University People's Hospital.
Trial summary
This study is conducted under the ethics-approved project titled "Artificial Intelligence Solution for Simplifying the Diagnostic Workflow of Breast MRI''.The goal of this observational study is to develop an integrated breast MRI system that uses diffusion-weighted imaging (DWI) to create synthetic contrast-enhanced… Read more about this study
- Who is it for?
- Ages 18 years and olderHealthy volunteers can take part
- Conditions the study is for
- Breast Neoplasms, Artificial Intelligence (AI), Magnetic Resonance Imaging (MRI)
- What is being tested?
- Non-contrast breast MRI diagnostic model
- How many people
- 12,000 participants
- Where
- No sites listed yet
- Study dates
- Jun 2026to May 2027
- Phase
- Observational
- 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 18 years and older
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Study population: Participants who underwent breast MR examinations at five institutions from 2014 to 2024 were enrolled. A test cohort prospectively collected at Peking University People's Hospital Health Examination Center, was enrolled to assess the robustness of the model.
Who may be able to join
- Complete breast MRI data;
- Negative pathology biopsy results or negative follow-up examinations for at least 12 months for non-cancer cases;
- Positive biopsy results that meet the requirements for the pathological subtype of cancer for cancer cases;
- Original data that can be used to verify clinical status, including radiological and pathological reports;
Who cannot join
- Partial mastectomy or puncture biopsy on the diseased side of the breast prior to breast MRI examination;
- Poor image quality;
- Implants in the affected breast;
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: Artificial Intelligence Solution for Simplifying the Diagnostic Workflow of Breast MRI: Development and Clinical Validation of a Diffusion-Weighted Imaging-Based Synthetic Contrast-Enhanced MRI System for Non-Contrast Breast Cancer Diagnosis and Risk Stratification
This study is conducted under the ethics-approved project titled "Artificial Intelligence Solution for Simplifying the Diagnostic Workflow of Breast MRI''.The goal of this observational study is to develop an integrated breast MRI system that uses diffusion-weighted imaging (DWI) to create synthetic contrast-enhanced images. This system aims to diagnose and screen for breast cancer without the need for contrast agents, while using a generated risk score to perform imaging-based triage and risk stratification.
Participants will include people aged 18 and older who require a breast MRI either for evaluation of a suspicious finding or for high-risk screening.
Read more
This study seeks to answer two main questions:
- Can synthetic contrast-enhanced images generated from DWI match real contrast-enhanced images in their ability to distinguish benign from malignant breast lesions?
- Can the risk score derived from DWI-based synthetic images enable imaging-level risk stratification, allowing people at lower risk to avoid contrast agent injection? Researchers will compare the quality of synthetic images against real contrast-enhanced images and will recruit radiologists to assess how well these images perform for diagnostic and screening tasks. MRI data from participants undergoing breast MRI will be used to train, validate, and test this integrated system.
Read the detailed description
We selected "other" in Time Perspective. This study will retrospectively collect MRI data from patients who underwent breast MRI at multiple centers between 2014 and 2024. We will also prospectively enroll MRI data from multiple centers for testing to assess the model's robustness.
- Conditions
- Breast Neoplasms, Artificial Intelligence (AI), Magnetic Resonance Imaging (MRI), Diffusion Magnetic Resonance Imaging, Deep Learning
- Being tested
- Non-contrast breast MRI diagnostic model
- Keywords
- Breast, Magnetic Resonance Imaging, Artificial Intelligence, 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.
Non-contrast breast MRI diagnostic modelDiagnostic test
An integrated AI model capable of generating synthetic contrast-enhanced images and distinguishing between benign and malignant lesions, as well as performing risk stratification
| Study group | Role | What they receive |
|---|---|---|
| Training cohort | Participants were retrospectively collected from Peking university people's hospital. All participants have completed the MRI examination and have available images for evaluation. Receives: Diagnostic Test: Non-contrast breast MRI diagnostic model | |
| External test cohort A | Participants were retrospectively collected from Center A. All participants have completed the MRI examination and have available images for evaluation. All enrolled data will be used for the model testing. Receives: Diagnostic Test: Non-contrast breast MRI diagnostic model | |
| External test cohort B | Participants were retrospectively collected from center B. All participants have completed the MRI examination and have available images for evaluation. All enrolled data will be used for the model testing. Receives: Diagnostic Test: Non-contrast breast MRI diagnostic model | |
| External test cohort C | Participants were retrospectively collected from center C. All participants have completed the MRI examination and have available images for evaluation. All enrolled data will be used for the model testing. Receives: Diagnostic Test: Non-contrast breast MRI diagnostic model | |
| External test cohort D | Participants were retrospectively collected from center D. All participants have completed the MRI examination and have available images for evaluation. All enrolled data will be used for the model testing. Receives: Diagnostic Test: Non-contrast breast MRI diagnostic model | |
| External test cohort E | Participants were retrospectively collected from center E. All participants have completed the MRI examination and have available images for evaluation. All enrolled data will be used for the model testing. Receives: Diagnostic Test: Non-contrast breast MRI diagnostic model | |
| External test cohort F | Participants were prospectively enrolled from Center F. All participants will undergo MRI examination and have available images for evaluation. All enrolled data will be used for the model testing. | |
| External test cohort G | Participants were prospectively enrolled from Peking University People's Hospital. All participants will undergo MRI examination and have images available for evaluation. All enrolled data will be used for the model testing. Receives: Diagnostic Test: Non-contrast breast MRI diagnostic model |
What is being measured
The primary outcome is the main question the study is designed to answer.
MRI examination
A multi-parameter contrast-enhanced breast MRI examination was performed, including fat-suppressed T2-weighted imaging, diffusion-weighted imaging, dynamic contrast-enhanced sequences, and fat-suppressed T1-weighted imaging.
Measured: Baseline
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted20 May 2026
- Study startJun 2026 (estimated)
- Primary completionDec 2026 (estimated)
- Study completionMay 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (13)Papers the sponsor has linked to this study.
- 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
- Other
- Enrolment
- 12,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Peking University People's Hospital
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Wang Yi, Peking University People's Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07598084
- Sponsor's protocol ID
- 2026-Ζ-52
- Status last verified
- May 2026
- First submitted
- 12 May 2026
- Last updated
- 9 Jun 2026
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- Individual data sharing
- Undecided
Publications
Wang P, Wang H, Nie P, Dang Y, Liu R, Qu M, Wang J, Mu G, Jia T, Shang L, Zhu K, Feng J, Chen B. Enabling AI-Generated Content for Gadolinium-Free Contrast-Enhanced Breast Magnetic Resonance Imaging. J Magn Reson Imaging. 2025 Mar;61(3):1232-1243. doi: 10.1002/jmri.29528. Epub 2024 Jul 25.
PubMed 39052258Chung M, Calabrese E, Mongan J, Ray KM, Hayward JH, Kelil T, Sieberg R, Hylton N, Joe BN, Lee AY. Deep Learning to Simulate Contrast-enhanced Breast MRI of Invasive Breast Cancer. Radiology. 2023 Mar;306(3):e239004. doi: 10.1148/radiol.239004. No abstract available.
PubMed 36803003Youn I, Biswas D, Hippe DS, Winter AM, Kazerouni AS, Javid SH, Lee JM, Rahbar H, Partridge SC. Diagnostic Performance of Point-of-Care Apparent Diffusion Coefficient Measures to Reduce Biopsy in Breast Lesions at MRI: Clinical Validation. Radiology. 2024 Feb;310(2):e232313. doi: 10.1148/radiol.232313.
PubMed 38349238Witowski J, Heacock L, Reig B, Kang SK, Lewin A, Pysarenko K, Patel S, Samreen N, Rudnicki W, Luczynska E, Popiela T, Moy L, Geras KJ. Improving breast cancer diagnostics with deep learning for MRI. Sci Transl Med. 2022 Sep 28;14(664):eabo4802. doi: 10.1126/scitranslmed.abo4802. Epub 2022 Sep 28.
PubMed 36170446Gao Y, Zeng S, Xu X, Li H, Yao S, Song K, Li X, Chen L, Tang J, Xing H, Yu Z, Zhang Q, Zeng S, Yi C, Xie H, Xiong X, Cai G, Wang Z, Wu Y, Chi J, Jiao X, Qin Y, Mao X, Chen Y, Jin X, Mo Q, Chen P, Huang Y, Shi Y, Wang J, Zhou Y, Ding S, Zhu S, Liu X, Dong X, Cheng L, Zhu L, Cheng H, Cha L, Hao Y, Jin C, Zhang L, Zhou P, Sun M, Xu Q, Chen K, Gao Z, Zhang X, Ma Y, Liu Y, Xiao L, Xu L, Peng L, Hao Z, Yang M, Wang Y, Ou H, Jia Y, Tian L, Zhang W, Jin P, Tian X, Huang L, Wang Z, Liu J, Fang T, Yan D, Cao H, Ma J, Li X, Zheng X, Lou H, Song C, Li R, Wang S, Li W, Zheng X, Chen J, Li G, Chen R, Xu C, Yu R, Wang J, Xu S, Kong B, Xie X, Ma D, Gao Q. Deep learning-enabled pelvic ultrasound images for accurate diagnosis of ovarian cancer in China: a retrospective, multicentre, diagnostic study. Lancet Digit Health. 2022 Mar;4(3):e179-e187. doi: 10.1016/S2589-7500(21)00278-8.
PubMed 35216752Amornsiripanitch N, Bickelhaupt S, Shin HJ, Dang M, Rahbar H, Pinker K, Partridge SC. Diffusion-weighted MRI for Unenhanced Breast Cancer Screening. Radiology. 2019 Dec;293(3):504-520. doi: 10.1148/radiol.2019182789. Epub 2019 Oct 8.
PubMed 31592734Baltzer A, Dietzel M, Kaiser CG, Baltzer PA. Combined reading of Contrast Enhanced and Diffusion Weighted Magnetic Resonance Imaging by using a simple sum score. Eur Radiol. 2016 Mar;26(3):884-91. doi: 10.1007/s00330-015-3886-x. Epub 2015 Jun 27.
PubMed 26115653Rahbar H, Zhang Z, Chenevert TL, Romanoff J, Kitsch AE, Hanna LG, Harvey SM, Moy L, DeMartini WB, Dogan B, Yang WT, Wang LC, Joe BN, Oh KY, Neal CH, McDonald ES, Schnall MD, Lehman CD, Comstock CE, Partridge SC. Utility of Diffusion-weighted Imaging to Decrease Unnecessary Biopsies Prompted by Breast MRI: A Trial of the ECOG-ACRIN Cancer Research Group (A6702). Clin Cancer Res. 2019 Mar 15;25(6):1756-1765. doi: 10.1158/1078-0432.CCR-18-2967. Epub 2019 Jan 15.
PubMed 30647080Lawson MB, Partridge SC, Hippe DS, Rahbar H, Lam DL, Lee CI, Lowry KP, Scheel JR, Parsian S, Li I, Biswas D, Bryant ML, Lee JM. Comparative Performance of Contrast-enhanced Mammography, Abbreviated Breast MRI, and Standard Breast MRI for Breast Cancer Screening. Radiology. 2023 Aug;308(2):e230576. doi: 10.1148/radiol.230576.
PubMed 37581498Kuhl CK. Abbreviated Breast MRI: State of the Art. Radiology. 2024 Mar;310(3):e221822. doi: 10.1148/radiol.221822.
PubMed 38530181Berg WA, Zhang Z, Lehrer D, Jong RA, Pisano ED, Barr RG, Bohm-Velez M, Mahoney MC, Evans WP 3rd, Larsen LH, Morton MJ, Mendelson EB, Farria DM, Cormack JB, Marques HS, Adams A, Yeh NM, Gabrielli G; ACRIN 6666 Investigators. Detection of breast cancer with addition of annual screening ultrasound or a single screening MRI to mammography in women with elevated breast cancer risk. JAMA. 2012 Apr 4;307(13):1394-404. doi: 10.1001/jama.2012.388.
PubMed 22474203Kuhl CK, Strobel K, Bieling H, Leutner C, Schild HH, Schrading S. Supplemental Breast MR Imaging Screening of Women with Average Risk of Breast Cancer. Radiology. 2017 May;283(2):361-370. doi: 10.1148/radiol.2016161444. Epub 2017 Feb 21.
PubMed 28221097Mann RM, Kuhl CK, Moy L. Contrast-enhanced MRI for breast cancer screening. J Magn Reson Imaging. 2019 Aug;50(2):377-390. doi: 10.1002/jmri.26654. Epub 2019 Jan 18.
PubMed 30659696
Study at a glance
From the registry record, last updated by the sponsor on 9 Jun 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Artificial Intelligence Solution for Simplifying the Diagnostic Workflow of Breast MRI: Development and Clinical Validation of a Diffusion-Weighted Imaging-Based Synthetic Contrast-Enhanced MRI System for Non-Contrast Breast Cancer Diagnosis and Risk Stratification
- ClinicalTrials.gov ID
- NCT07598084
- Acronym / sponsor ID
- 2026-Ζ-52
- Conditions
- Breast Neoplasms, Artificial Intelligence (AI), Magnetic Resonance Imaging (MRI), Diffusion Magnetic Resonance Imaging, Deep Learning
- Keywords
- Breast, Magnetic Resonance Imaging, Artificial Intelligence, Deep learning
- Study type
- Observational
- Recruitment status
- Not yet recruiting
- Enrolment
- 12,000 participants (estimated)
- Lead sponsor
- Peking University People's HospitalUniversity, hospital or other
- Study start
- Jun 2026 (estimated)
- Primary completion
- Dec 2026 (estimated)
- Study completion
- May 2027 (estimated)
- First posted
- 20 May 2026 (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 study is conducted under the ethics-approved project titled "Artificial Intelligence Solution for Simplifying the Diagnostic Workflow of Breast MRI''.The goal of this observational study is to develop an integrated breast MRI system that uses diffusion-weighted imaging (DWI) to create synthetic contrast-enhanced images. This system aims to diagnose and screen for breast cancer without the need for contrast agents, while using a generated risk score to perform imaging-based triage and risk stratification.
Participants will include people aged 18 and older who require a breast MRI either for evaluation of a suspicious finding or for high-risk screening.
This study seeks to answer two main questions:
- Can synthetic contrast-enhanced images generated from DWI match real contrast-enhanced images in their ability to distinguish benign from malignant breast lesions?
- Can the risk score derived from DWI-based synthetic images enable imaging-level risk stratification, allowing people at lower risk to avoid contrast agent injection? Researchers will compare the quality of synthetic images against real contrast-enhanced images and will recruit radiologists to assess how well these images perform for diagnostic and screening tasks. MRI data from participants undergoing breast MRI will be used to train, validate, and test this integrated system.
Detailed description
We selected "other" in Time Perspective. This study will retrospectively collect MRI data from patients who underwent breast MRI at multiple centers between 2014 and 2024. We will also prospectively enroll MRI data from multiple centers for testing to assess the model's robustness.
Study design
- Observational model
- Cohort
- Time perspective
- Other
- Number of arms
- 8
| Arm | Type | Receives |
|---|---|---|
| Training cohort | – | Diagnostic Test: Non-contrast breast MRI diagnostic modelParticipants were retrospectively collected from Peking university people's hospital. All participants have completed the MRI examination and have available images for evaluation. |
| External test cohort A | – | Diagnostic Test: Non-contrast breast MRI diagnostic modelParticipants were retrospectively collected from Center A. All participants have completed the MRI examination and have available images for evaluation. All enrolled data will be used for the model testing. |
| External test cohort B | – | Diagnostic Test: Non-contrast breast MRI diagnostic modelParticipants were retrospectively collected from center B. All participants have completed the MRI examination and have available images for evaluation. All enrolled data will be used for the model testing. |
| External test cohort C | – | Diagnostic Test: Non-contrast breast MRI diagnostic modelParticipants were retrospectively collected from center C. All participants have completed the MRI examination and have available images for evaluation. All enrolled data will be used for the model testing. |
| External test cohort D | – | Diagnostic Test: Non-contrast breast MRI diagnostic modelParticipants were retrospectively collected from center D. All participants have completed the MRI examination and have available images for evaluation. All enrolled data will be used for the model testing. |
| External test cohort E | – | Diagnostic Test: Non-contrast breast MRI diagnostic modelParticipants were retrospectively collected from center E. All participants have completed the MRI examination and have available images for evaluation. All enrolled data will be used for the model testing. |
| External test cohort F | – | –Participants were prospectively enrolled from Center F. All participants will undergo MRI examination and have available images for evaluation. All enrolled data will be used for the model testing. |
| External test cohort G | – | Diagnostic Test: Non-contrast breast MRI diagnostic modelParticipants were prospectively enrolled from Peking University People's Hospital. All participants will undergo MRI examination and have images available for evaluation. All enrolled data will be used for the model testing. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- Study population
- Participants who underwent breast MR examinations at five institutions from 2014 to 2024 were enrolled. A test cohort prospectively collected at Peking University People's Hospital Health Examination Center, was enrolled to assess the robustness of the model.
Key inclusion criteria (4 listed)
- Complete breast MRI data;
- Negative pathology biopsy results or negative follow-up examinations for at least 12 months for non-cancer cases;
- Positive biopsy results that meet the requirements for the pathological subtype of cancer for cancer cases;
- Original data that can be used to verify clinical status, including radiological and pathological reports;
Key exclusion criteria (3 listed)
- Partial mastectomy or puncture biopsy on the diseased side of the breast prior to breast MRI examination;
- Poor image quality;
- Implants in the affected breast;
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 breast MRI diagnostic modelAn integrated AI model capable of generating synthetic contrast-enhanced images and distinguishing between benign and malignant lesions, as well as performing risk stratification | Diagnostic test | External test cohort A, External test cohort B, External test cohort C, External test cohort D, External test cohort E, External test cohort G, Training cohort |
Primary outcome
MRI examination
Time frame: Baseline
A multi-parameter contrast-enhanced breast MRI examination was performed, including fat-suppressed T2-weighted imaging, diffusion-weighted imaging, dynamic contrast-enhanced sequences, and fat-suppressed T1-weighted imaging.
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 9 Jun 2026.
- Background: PubMed 39052258Wang P, Wang H, Nie P, Dang Y, Liu R, Qu M, Wang J, Mu G, Jia T, Shang L, Zhu K, Feng J, Chen B. Enabling AI-Generated Content for Gadolinium-Free Contrast-Enhanced Breast Magnetic Resonance Imaging. J Magn Reson Imaging. 2025 Mar;61(3):1232-1243. doi: 10.1002/jmri.29528. Epub 2024 Jul 25.
- Background: PubMed 36803003Chung M, Calabrese E, Mongan J, Ray KM, Hayward JH, Kelil T, Sieberg R, Hylton N, Joe BN, Lee AY. Deep Learning to Simulate Contrast-enhanced Breast MRI of Invasive Breast Cancer. Radiology. 2023 Mar;306(3):e239004. doi: 10.1148/radiol.239004. No abstract available.
- Background: PubMed 38349238Youn I, Biswas D, Hippe DS, Winter AM, Kazerouni AS, Javid SH, Lee JM, Rahbar H, Partridge SC. Diagnostic Performance of Point-of-Care Apparent Diffusion Coefficient Measures to Reduce Biopsy in Breast Lesions at MRI: Clinical Validation. Radiology. 2024 Feb;310(2):e232313. doi: 10.1148/radiol.232313.
- Background: PubMed 36170446Witowski J, Heacock L, Reig B, Kang SK, Lewin A, Pysarenko K, Patel S, Samreen N, Rudnicki W, Luczynska E, Popiela T, Moy L, Geras KJ. Improving breast cancer diagnostics with deep learning for MRI. Sci Transl Med. 2022 Sep 28;14(664):eabo4802. doi: 10.1126/scitranslmed.abo4802. Epub 2022 Sep 28.
- Background: PubMed 35216752Gao Y, Zeng S, Xu X, Li H, Yao S, Song K, Li X, Chen L, Tang J, Xing H, Yu Z, Zhang Q, Zeng S, Yi C, Xie H, Xiong X, Cai G, Wang Z, Wu Y, Chi J, Jiao X, Qin Y, Mao X, Chen Y, Jin X, Mo Q, Chen P, Huang Y, Shi Y, Wang J, Zhou Y, Ding S, Zhu S, Liu X, Dong X, Cheng L, Zhu L, Cheng H, Cha L, Hao Y, Jin C, Zhang L, Zhou P, Sun M, Xu Q, Chen K, Gao Z, Zhang X, Ma Y, Liu Y, Xiao L, Xu L, Peng L, Hao Z, Yang M, Wang Y, Ou H, Jia Y, Tian L, Zhang W, Jin P, Tian X, Huang L, Wang Z, Liu J, Fang T, Yan D, Cao H, Ma J, Li X, Zheng X, Lou H, Song C, Li R, Wang S, Li W, Zheng X, Chen J, Li G, Chen R, Xu C, Yu R, Wang J, Xu S, Kong B, Xie X, Ma D, Gao Q. Deep learning-enabled pelvic ultrasound images for accurate diagnosis of ovarian cancer in China: a retrospective, multicentre, diagnostic study. Lancet Digit Health. 2022 Mar;4(3):e179-e187. doi: 10.1016/S2589-7500(21)00278-8.
- Background: PubMed 31592734Amornsiripanitch N, Bickelhaupt S, Shin HJ, Dang M, Rahbar H, Pinker K, Partridge SC. Diffusion-weighted MRI for Unenhanced Breast Cancer Screening. Radiology. 2019 Dec;293(3):504-520. doi: 10.1148/radiol.2019182789. Epub 2019 Oct 8.
- Background: PubMed 26115653Baltzer A, Dietzel M, Kaiser CG, Baltzer PA. Combined reading of Contrast Enhanced and Diffusion Weighted Magnetic Resonance Imaging by using a simple sum score. Eur Radiol. 2016 Mar;26(3):884-91. doi: 10.1007/s00330-015-3886-x. Epub 2015 Jun 27.
- Background: PubMed 30647080Rahbar H, Zhang Z, Chenevert TL, Romanoff J, Kitsch AE, Hanna LG, Harvey SM, Moy L, DeMartini WB, Dogan B, Yang WT, Wang LC, Joe BN, Oh KY, Neal CH, McDonald ES, Schnall MD, Lehman CD, Comstock CE, Partridge SC. Utility of Diffusion-weighted Imaging to Decrease Unnecessary Biopsies Prompted by Breast MRI: A Trial of the ECOG-ACRIN Cancer Research Group (A6702). Clin Cancer Res. 2019 Mar 15;25(6):1756-1765. doi: 10.1158/1078-0432.CCR-18-2967. Epub 2019 Jan 15.
- Background: PubMed 37581498Lawson MB, Partridge SC, Hippe DS, Rahbar H, Lam DL, Lee CI, Lowry KP, Scheel JR, Parsian S, Li I, Biswas D, Bryant ML, Lee JM. Comparative Performance of Contrast-enhanced Mammography, Abbreviated Breast MRI, and Standard Breast MRI for Breast Cancer Screening. Radiology. 2023 Aug;308(2):e230576. doi: 10.1148/radiol.230576.
- Background: PubMed 38530181Kuhl CK. Abbreviated Breast MRI: State of the Art. Radiology. 2024 Mar;310(3):e221822. doi: 10.1148/radiol.221822.
- Background: PubMed 22474203Berg WA, Zhang Z, Lehrer D, Jong RA, Pisano ED, Barr RG, Bohm-Velez M, Mahoney MC, Evans WP 3rd, Larsen LH, Morton MJ, Mendelson EB, Farria DM, Cormack JB, Marques HS, Adams A, Yeh NM, Gabrielli G; ACRIN 6666 Investigators. Detection of breast cancer with addition of annual screening ultrasound or a single screening MRI to mammography in women with elevated breast cancer risk. JAMA. 2012 Apr 4;307(13):1394-404. doi: 10.1001/jama.2012.388.
- Background: PubMed 28221097Kuhl CK, Strobel K, Bieling H, Leutner C, Schild HH, Schrading S. Supplemental Breast MR Imaging Screening of Women with Average Risk of Breast Cancer. Radiology. 2017 May;283(2):361-370. doi: 10.1148/radiol.2016161444. Epub 2017 Feb 21.
- Individual participant dataSharing planned: Undecided
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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?
An observational study of Breast Neoplasms and Artificial Intelligence (AI), run by Peking University People's Hospital.
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 4 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: opening soon, as of the sponsor's last update on 9 Jun 2026. 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 HAOQUAN CHEN, MD (+86 010-88325811, CHENHAOQUANSZ@163.COM), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
MRI examination (time frame: Baseline). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT07598084, which also lists 13 linked publications.
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 9 Jun 2026.