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

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.

• Inclusion Criteria:

• Female, age 30-70 years
• Completed breast MRI scan, including at least T2WI, DWI, and ADC sequences
• Multimodal data acquired within the same time window (≤90 days)
• A clear clinical outcome: pathologically confirmed or ≥12-24 months of negative follow-up
• The time window between imaging examination and outcome determination was ≤90 days
• Signed informed consent
• Exclusion Criteria:

• Absolute contraindications to MRI (pacemaker, cochlear implant, ocular metal foreign body, etc.)
• Pregnant or lactating women
• Recent history of breast surgery/radiotherapy (≤6 months) or imaging after neoadjuvant therapy
• Substandard image quality (severe motion artifact, signal-to-noise ratio below threshold)
• Incomplete clinical data or time window exceeded
• Known breast cancer metastasis or recurrence