Non-Contrast Breast MRI Diagnosis and Risk Stratification Using DWI-Generated Synthetic Contrast Enhancement
2026年6月5日 更新者：Wang Yi、Peking University People's Hospital
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.
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.
研究概览
地位
尚未招聘
详细说明
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.
研究类型
观察性的
注册 (估计的)
12000
联系人和位置
本节提供了进行研究的人员的详细联系信息，以及有关进行该研究的地点的信息。
学习联系方式
- 姓名：HAOQUAN CHEN, MD
- 电话号码：+86 010-88325811
- 邮箱：CHENHAOQUANSZ@163.COM
参与标准
研究人员寻找符合特定描述的人，称为资格标准。这些标准的一些例子是一个人的一般健康状况或先前的治疗。
资格标准
适合学习的年龄
- 成人
- 年长者
接受健康志愿者
是的
取样方法
概率样本
研究人群
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.
描述
Inclusion Criteria:
- 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;
Exclusion Criteria:
- 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;
学习计划
本节提供研究计划的详细信息，包括研究的设计方式和研究的衡量标准。
研究是如何设计的？
设计细节
队列和干预
团体/队列 |
干预/治疗 |
|---|---|
|
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.
|
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 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.
|
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 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.
|
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 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.
|
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 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.
|
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 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.
|
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 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.
|
An integrated AI model capable of generating synthetic contrast-enhanced images and distinguishing between benign and malignant lesions, as well as performing risk stratification
|
研究衡量的是什么？
主要结果指标
结果测量 |
措施说明 |
大体时间 |
|---|---|---|
|
MRI examination
大体时间：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.
|
Baseline
|
合作者和调查者
在这里您可以找到参与这项研究的人员和组织。
出版物和有用的链接
负责输入研究信息的人员自愿提供这些出版物。这些可能与研究有关。
一般刊物
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- Kuhl CK. Abbreviated Breast MRI: State of the Art. Radiology. 2024 Mar;310(3):e221822. doi: 10.1148/radiol.221822.
- 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.
- 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.
研究记录日期
这些日期跟踪向 ClinicalTrials.gov 提交研究记录和摘要结果的进度。研究记录和报告的结果由国家医学图书馆 (NLM) 审查，以确保它们在发布到公共网站之前符合特定的质量控制标准。
研究主要日期
学习开始 (估计的)
2026年6月1日
初级完成 (估计的)
2026年12月1日
研究完成 (估计的)
2027年5月1日
研究注册日期
首次提交
2026年5月12日
首先提交符合 QC 标准的
2026年5月19日
首次发布 (实际的)
2026年5月20日
研究记录更新
最后更新发布 (实际的)
2026年6月9日
上次提交的符合 QC 标准的更新
2026年6月5日
最后验证
2026年5月1日
更多信息
此信息直接从 clinicaltrials.gov 网站检索，没有任何更改。如果您有任何更改、删除或更新研究详细信息的请求，请联系 register@clinicaltrials.gov. clinicaltrials.gov 上实施更改，我们的网站上也会自动更新.