Non-Contrast Breast MRI Diagnosis and Risk Stratification Using DWI-Generated Synthetic Contrast Enhancement
5 juin 2026 mis à jour par: 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.
Aperçu de l'étude
Statut
Pas encore de recrutement
Les conditions
Intervention / Traitement
Description détaillée
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.
Type d'étude
Observationnel
Inscription (Estimé)
12000
Contacts et emplacements
Cette section fournit les coordonnées de ceux qui mènent l'étude et des informations sur le lieu où cette étude est menée.
Coordonnées de l'étude
- Nom: HAOQUAN CHEN, MD
- Numéro de téléphone: +86 010-88325811
- E-mail: CHENHAOQUANSZ@163.COM
Critères de participation
Les chercheurs recherchent des personnes qui correspondent à une certaine description, appelée critères d'éligibilité. Certains exemples de ces critères sont l'état de santé général d'une personne ou des traitements antérieurs.
Critère d'éligibilité
Âges éligibles pour étudier
- Adulte
- Adulte plus âgé
Accepte les volontaires sains
Oui
Méthode d'échantillonnage
Échantillon de probabilité
Population étudiée
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.
La description
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;
Plan d'étude
Cette section fournit des détails sur le plan d'étude, y compris la façon dont l'étude est conçue et ce que l'étude mesure.
Comment l'étude est-elle conçue ?
Détails de conception
Cohortes et interventions
Groupe / Cohorte |
Intervention / Traitement |
|---|---|
|
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
|
Que mesure l'étude ?
Principaux critères de jugement
Mesure des résultats |
Description de la mesure |
Délai |
|---|---|---|
|
MRI examination
Délai: 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
|
Collaborateurs et enquêteurs
C'est ici que vous trouverez les personnes et les organisations impliquées dans cette étude.
Parrainer
Publications et liens utiles
La personne responsable de la saisie des informations sur l'étude fournit volontairement ces publications. Il peut s'agir de tout ce qui concerne l'étude.
Publications générales
- 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.
Dates d'enregistrement des études
Ces dates suivent la progression des dossiers d'étude et des soumissions de résultats sommaires à ClinicalTrials.gov. Les dossiers d'étude et les résultats rapportés sont examinés par la Bibliothèque nationale de médecine (NLM) pour s'assurer qu'ils répondent à des normes de contrôle de qualité spécifiques avant d'être publiés sur le site Web public.
Dates principales de l'étude
Début de l'étude (Estimé)
1 juin 2026
Achèvement primaire (Estimé)
1 décembre 2026
Achèvement de l'étude (Estimé)
1 mai 2027
Dates d'inscription aux études
Première soumission
12 mai 2026
Première soumission répondant aux critères de contrôle qualité
19 mai 2026
Première publication (Réel)
20 mai 2026
Mises à jour des dossiers d'étude
Dernière mise à jour publiée (Réel)
9 juin 2026
Dernière mise à jour soumise répondant aux critères de contrôle qualité
5 juin 2026
Dernière vérification
1 mai 2026
Plus d'information
Termes liés à cette étude
Mots clés
Termes MeSH pertinents supplémentaires
Autres numéros d'identification d'étude
- 2026-Ζ-52
Plan pour les données individuelles des participants (IPD)
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INDÉCIS
Informations sur les médicaments et les dispositifs, documents d'étude
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Ces informations ont été extraites directement du site Web clinicaltrials.gov sans aucune modification. Si vous avez des demandes de modification, de suppression ou de mise à jour des détails de votre étude, veuillez contacter register@clinicaltrials.gov. Dès qu'un changement est mis en œuvre sur clinicaltrials.gov, il sera également mis à jour automatiquement sur notre site Web .