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AI-Based Self-Supervised Learning Model Using Non-Contrast Breast MRI for Early Screening and Clinical Utility Evaluation

NCT07205276 · Status: NOT_YET_RECRUITING · Phase: NA · Type: INTERVENTIONAL · Enrollment: 30000

Last updated 2025-10-03

No results posted yet for this study

Summary

Breast cancer is the most common malignant disease among women worldwide, with rising incidence and younger age at onset in China. Early detection is critical for improving survival, yet current screening methods such as mammography and ultrasound show limited sensitivity in Chinese women, particularly those with dense breast tissue. Contrast-enhanced MRI offers higher diagnostic performance but its use is limited by high costs, safety concerns with gadolinium-based contrast agents, and limited accessibility.

This investigator-initiated trial aims to evaluate the clinical application of non-contrast multiparametric MRI, combined with advanced artificial intelligence algorithms, for the early detection and diagnosis of breast cancer. The study will collect MRI imaging data from multiple centers and integrate radiomic features across T2-weighted imaging, diffusion-weighted imaging, and apparent diffusion coefficient maps. A deep learning-based model will be developed and validated to improve lesion detection, differential diagnosis, and risk stratification.

The ultimate goal of this project is to establish a safe, accurate, and scalable breast cancer screening pathway suitable for Chinese women. By reducing dependence on invasive procedures and contrast agents, and by leveraging AI for standardization and efficiency, this approach may significantly improve early detection rates and contribute to better patient outcomes.

Conditions

  • Breast Cancer Detection
  • Early Detection of Cancer
  • AI (Artificial Intelligence)

Interventions

DIAGNOSTIC_TEST

Non-contrast multiparametric breast MRI with AI-based radiomics analysis

Participants will receive standardized non-contrast multiparametric breast MRI scans (T2WI, DWI, ADC). Imaging features will be extracted and analyzed using artificial intelligence-based radiomics and deep learning algorithms to improve early detection and diagnosis of breast cancer.

DIAGNOSTIC_TEST

Standard radiologist reading of non-contrast multiparametric breast MRI

Imaging data interpreted by trained radiologists following routine clinical practice, without AI assistance.

Sponsors & Collaborators

  • Alibaba DAMO Academy

    collaborator UNKNOWN

  • Second Affiliated Hospital, School of Medicine, Zhejiang University

    lead OTHER

Study Design

Allocation
NA
Purpose
DIAGNOSTIC
Masking
NONE
Model
PARALLEL

Eligibility

Min Age
30 Years
Max Age
70 Years
Sex
FEMALE
Healthy Volunteers
No

Timeline & Regulatory

Start
2025-10-01
Primary Completion
2027-10-01
Completion
2027-12-01

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT07205276 on ClinicalTrials.gov