Non-Contrast Breast MRI Diagnosis and Risk Stratification Using DWI-Generated Synthetic Contrast Enhancement
5. juni 2026 oppdatert av: 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.
Studieoversikt
Status
Har ikke rekruttert ennå
Forhold
Intervensjon / Behandling
Detaljert beskrivelse
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.
Studietype
Observasjonsmessig
Registrering (Antatt)
12000
Kontakter og plasseringer
Denne delen inneholder kontaktinformasjon for de som utfører studien, og informasjon om hvor denne studien blir utført.
Studiekontakt
- Navn: HAOQUAN CHEN, MD
- Telefonnummer: +86 010-88325811
- E-post: CHENHAOQUANSZ@163.COM
Deltakelseskriterier
Forskere ser etter personer som passer til en bestemt beskrivelse, kalt kvalifikasjonskriterier. Noen eksempler på disse kriteriene er en persons generelle helsetilstand eller tidligere behandlinger.
Kvalifikasjonskriterier
Alder som er kvalifisert for studier
- Voksen
- Eldre voksen
Tar imot friske frivillige
Ja
Prøvetakingsmetode
Sannsynlighetsprøve
Studiepopulasjon
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.
Beskrivelse
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;
Studieplan
Denne delen gir detaljer om studieplanen, inkludert hvordan studien er utformet og hva studien måler.
Hvordan er studiet utformet?
Designdetaljer
Kohorter og intervensjoner
Gruppe / Kohort |
Intervensjon / Behandling |
|---|---|
|
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
|
Hva måler studien?
Primære resultatmål
Resultatmål |
Tiltaksbeskrivelse |
Tidsramme |
|---|---|---|
|
MRI examination
Tidsramme: 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
|
Samarbeidspartnere og etterforskere
Det er her du vil finne personer og organisasjoner som er involvert i denne studien.
Publikasjoner og nyttige lenker
Den som er ansvarlig for å legge inn informasjon om studien leverer frivillig disse publikasjonene. Disse kan handle om alt relatert til studiet.
Generelle publikasjoner
- 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.
Studierekorddatoer
Disse datoene sporer fremdriften for innsending av studieposter og sammendragsresultater til ClinicalTrials.gov. Studieposter og rapporterte resultater gjennomgås av National Library of Medicine (NLM) for å sikre at de oppfyller spesifikke kvalitetskontrollstandarder før de legges ut på det offentlige nettstedet.
Studer hoveddatoer
Studiestart (Antatt)
1. juni 2026
Primær fullføring (Antatt)
1. desember 2026
Studiet fullført (Antatt)
1. mai 2027
Datoer for studieregistrering
Først innsendt
12. mai 2026
Først innsendt som oppfylte QC-kriteriene
19. mai 2026
Først lagt ut (Faktiske)
20. mai 2026
Oppdateringer av studieposter
Sist oppdatering lagt ut (Faktiske)
9. juni 2026
Siste oppdatering sendt inn som oppfylte QC-kriteriene
5. juni 2026
Sist bekreftet
1. mai 2026
Mer informasjon
Begreper knyttet til denne studien
Ytterligere relevante MeSH-vilkår
Andre studie-ID-numre
- 2026-Ζ-52
Plan for individuelle deltakerdata (IPD)
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Legemiddel- og utstyrsinformasjon, studiedokumenter
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