An interventional study of Non-contrast multiparametric breast MRI with AI-based radiomics analysis and Standard radiologist reading of non-contrast multiparametric breast MRI in Breast Cancer Detection, Early Detection of Cancer and AI (Artificial Intelligence), sponsored by Second Affiliated Hospital, School of Medicine, Zhejiang University. Not yet recruiting. Open to female participants aged 30 Years to 70 Years. Per ClinicalTrials.gov, last updated 2025-10-03.
Sponsored by Second Affiliated Hospital, School of Medicine, Zhejiang University · Not applicable, Interventional, and Diagnostic
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.
This is a prospective, investigator-initiated clinical study designed to evaluate the role of radiomics and artificial intelligence in non-invasive, early detection and diagnosis of breast cancer. While mammography and ultrasound are widely used as first-line screening methods, their sensitivity and specificity remain suboptimal in Chinese women, particularly in individuals with dense breast tissue. Contrast-enhanced MRI has demonstrated superior diagnostic performance, but its clinical utility is limited due to high costs, safety concerns related to gadolinium deposition, and limited availability in population-based screening programs.
To address these challenges, this study will focus on non-contrast multiparametric breast MRI, including T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and apparent diffusion coefficient (ADC) mapping. Imaging data will be prospectively collected from multiple clinical sites. A radiomics pipeline will be established to extract high-dimensional features characterizing lesion morphology, texture, and diffusion properties. Furthermore, an artificial intelligence-based model, developed using deep learning and self-supervised learning frameworks, will be trained and validated for lesion detection, classification, and risk prediction.
The primary aim of this trial is to construct and validate an imaging biomarker for early breast cancer detection based on non-contrast MRI and AI. Secondary objectives include evaluation of diagnostic accuracy compared with conventional imaging modalities, analysis of model performance across different molecular subtypes of breast cancer, and exploration of its potential application in predicting treatment response and clinical outcomes.
The expected outcome of this study is to provide robust evidence supporting the clinical feasibility of AI-guided non-contrast MRI as a safe, cost-effective, and scalable tool for early breast cancer screening in Chinese women. This work has the potential to optimize screening strategies, reduce unnecessary invasive procedures, and ultimately improve patient prognosis.
Second Affiliated Hospital, School of Medicine, Zhejiang University is the lead sponsor of 1,058 studies on the registry; 511 are open to participants now.
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Female, age 30-70 years
Absolute contraindications to MRI (pacemaker, cochlear implant, ocular metal foreign body, etc.)
Participants will undergo non-contrast multiparametric breast MRI, including T2-weighted imaging, diffusion-weighted imaging (DWI), and apparent diffusion coefficient (ADC) mapping. Imaging data will be analyzed using radiomics and AI-based algorithms for breast cancer detection and diagnosis.
Diagnostic Test: Non-contrast multiparametric breast MRI with AI-based radiomics analysis
Participants undergo standardized non-contrast multiparametric breast MRI (T2WI, DWI, ADC). Imaging data are interpreted by radiologists without AI assistance, representing the current standard of care
Diagnostic Test: Standard radiologist reading of non-contrast multiparametric breast MRI
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.
Imaging data interpreted by trained radiologists following routine clinical practice, without AI assistance.
Diagnostic accuracy of AI-based non-contrast multiparametric MRI for breast cancer detection
Diagnostic performance of the AI-based radiomics model using non-contrast multiparametric breast MRI (T2WI, DWI, ADC) will be evaluated. The performance will be compared against the reference standard (histopathology or follow-up imaging).
Time frame: Within 12 months of study enrollment
Sensitivity and specificity stratified by breast cancer molecular subtype
Evaluate model performance in subgroups defined by ER, PR, HER2, and Ki-67 status
Time frame: Within 12 months of enrollment
No study locations are listed for this record.
Plan to share: Yes — Individual participant data (IPD) underlying the results will be made available after publication, upon reasonable request to the corresponding investigator. De-identified MRI imaging data and associated clinical annotations will be shared through a controlled access repository.
Supporting information: Study protocol, Sap, Analytic code
No publications or documents are linked to this record.
This study is not yet recruiting, as verified in Sep 2025. You cannot join it, but the record below documents what was studied.
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Second Affiliated Hospital, School of Medicine, Zhejiang University