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NCT Number: NCT07598084

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

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.

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Key information

About this study

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.

Who can participate

Healthy volunteers accepted: Yes

Only the study team can determine whether someone qualifies for participation.

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;

Treatment and study plan

Non-contrast breast MRI diagnostic model

Diagnostic 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

Primary outcomes

  1. 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.

Study contacts

Contact information is provided by the study sponsor or research team.

HAOQUAN CHEN, MD

CONTACT

[email protected]

+86 010-88325811

Sponsors and collaborators

Lead sponsor

Peking University People's Hospital

Other

Registry information

Official study title

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

Important dates

Study start
2026
Primary completion
2026
Study completion
2027
First posted
May 20, 2026
Registry last updated
Jun 9, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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