NCT07598084

Brief Summary

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.

Trial Health

65
Monitor

Trial Health Score

Automated assessment based on enrollment pace, timeline, and geographic reach

Enrollment
12,000

participants targeted

Target at P75+ for all trials

Timeline
11mo left

Started May 2026

Shorter than P25 for all trials

Status
not yet recruiting

Health score is calculated from publicly available data and should be used for screening purposes only.

Trial Relationships

Click on a node to explore related trials.

Study Timeline

Key milestones and dates

Study Progress6%
May 2026May 2027

Study Start

First participant enrolled

May 1, 2026

Completed
11 days until next milestone

First Submitted

Initial submission to the registry

May 12, 2026

Completed
8 days until next milestone

First Posted

Study publicly available on registry

May 20, 2026

Completed
7 months until next milestone

Primary Completion

Last participant's last visit for primary outcome

December 1, 2026

Expected
5 months until next milestone

Study Completion

Last participant's last visit for all outcomes

May 1, 2027

Last Updated

May 20, 2026

Status Verified

May 1, 2026

Enrollment Period

7 months

First QC Date

May 12, 2026

Last Update Submit

May 19, 2026

Conditions

Breast NeoplasmsArtificial Intelligence (AI)Magnetic Resonance Imaging (MRI)Diffusion Magnetic Resonance ImagingDeep Learning

Keywords

BreastMagnetic Resonance ImagingArtificial IntelligenceDeep learning

Outcome Measures

Primary Outcomes (1)

  • 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.

    Baseline

Study Arms (8)

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.

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.

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.

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.

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.

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.

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.

Diagnostic Test: Non-contrast breast MRI diagnostic model

Interventions

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

External test cohort AExternal test cohort BExternal test cohort CExternal test cohort DExternal test cohort EExternal test cohort GTraining cohort

Eligibility Criteria

Age18 Years+
Sexall
Healthy VolunteersYes
Age GroupsAdult (18-64), Older Adult (65+)
Sampling MethodProbability Sample
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.

You may qualify if:

  • 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;

You may not qualify if:

  • 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;

Contact the study team to confirm eligibility.

Sponsors & Collaborators

Related Publications (13)

  • 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.

    PMID: 39052258BACKGROUND

  • Chung 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.

    PMID: 36803003BACKGROUND

  • Youn 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.

    PMID: 38349238BACKGROUND

  • Witowski 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.

    PMID: 36170446BACKGROUND

  • Gao 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.

    PMID: 35216752BACKGROUND

  • Amornsiripanitch 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.

    PMID: 31592734BACKGROUND

  • Baltzer 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.

    PMID: 26115653BACKGROUND

  • Rahbar 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.

    PMID: 30647080BACKGROUND

  • Lawson 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.

    PMID: 37581498BACKGROUND

  • Kuhl CK. Abbreviated Breast MRI: State of the Art. Radiology. 2024 Mar;310(3):e221822. doi: 10.1148/radiol.221822.

    PMID: 38530181BACKGROUND

  • Berg 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.

    PMID: 22474203BACKGROUND

  • Kuhl 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.

    PMID: 28221097BACKGROUND

  • Mann 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.

    PMID: 30659696BACKGROUND

MeSH Terms

Conditions

Breast Neoplasms

Condition Hierarchy (Ancestors)

Neoplasms by SiteNeoplasmsBreast DiseasesSkin DiseasesSkin and Connective Tissue Diseases

Central Study Contacts

HAOQUAN CHEN, MD

CONTACT

+86 010-88325811CHENHAOQUANSZ@163.COM

Study Design

Study Type
observational
Observational Model
COHORT
Time Perspective
OTHER
Sponsor Type
OTHER
Responsible Party
PRINCIPAL INVESTIGATOR
PI Title
Professor

Study Record Dates

First Submitted

May 12, 2026

First Posted

May 20, 2026

Study Start

May 1, 2026

Primary Completion (Estimated)

December 1, 2026

Study Completion (Estimated)

May 1, 2027

Last Updated

May 20, 2026

Record last verified: 2026-05