Non-Contrast Breast MRI Diagnosis and Risk Stratification Using DWI-Generated Synthetic Contrast Enhancement

May 19, 2026 updated by: Wang Yi, Peking University People's Hospital

Development and Clinical Validation of a Diffusion-Weighted Imaging-Based Synthetic Contrast-Enhanced MRI System for Non-Contrast Breast Cancer Diagnosis and Risk Stratification

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.

Study Overview

Status

Not yet recruiting

Conditions

Intervention / Treatment

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 Type

Observational

Enrollment (Estimated)

12000

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Adult
  • Older Adult

Accepts Healthy Volunteers

Yes

Sampling Method

Probability 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.

Description

Inclusion Criteria:

  1. Complete breast MRI data;
  2. Negative pathology biopsy results or negative follow-up examinations for at least 12 months for non-cancer cases;
  3. Positive biopsy results that meet the requirements for the pathological subtype of cancer for cancer cases;
  4. Original data that can be used to verify clinical status, including radiological and pathological reports;

Exclusion Criteria:

  1. Partial mastectomy or puncture biopsy on the diseased side of the breast prior to breast MRI examination;
  2. Poor image quality;
  3. Implants in the affected breast;

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

Cohorts and Interventions

Group / Cohort
Intervention / Treatment
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
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.
Diagnostic test: Non-contrast breast MRI diagnostic model
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.
Diagnostic test: Non-contrast breast MRI diagnostic model
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.
Diagnostic test: Non-contrast breast MRI diagnostic model
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.
Diagnostic test: Non-contrast breast MRI diagnostic model
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.
Diagnostic test: Non-contrast breast MRI diagnostic model
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.
Diagnostic test: Non-contrast breast MRI diagnostic model
An integrated AI model capable of generating synthetic contrast-enhanced images and distinguishing between benign and malignant lesions, as well as performing risk stratification

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
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.
Baseline

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Peking University People's Hospital

Publications and helpful links

The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.

General Publications

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Estimated)

May 1, 2026

Primary Completion (Estimated)

December 1, 2026

Study Completion (Estimated)

May 1, 2027

Study Registration Dates

First Submitted

May 12, 2026

First Submitted That Met QC Criteria

May 19, 2026

First Posted (Actual)

May 20, 2026

Study Record Updates

Last Update Posted (Actual)

May 20, 2026

Last Update Submitted That Met QC Criteria

May 19, 2026

Last Verified

May 1, 2026

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • 2026-Ζ-52

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

UNDECIDED

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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