The role of diffusion-weighted imaging in breast cancer: from diagnosis to treatment monitoring and follow-up.

Abstract

Diffusion-weighted imaging (DWI) is a fundamental non-contrast sequence in multiparametric breast magnetic resonance imaging (MRI), measuring water diffusion to assess tissue microstructure. The derived apparent diffusion coefficient (ADC) effectively discriminates between malignant lesions, which typically exhibit lower ADC due to high cellularity, and benign ones. Advanced models, such as intravoxel incoherent motion, diffusion kurtosis imaging, restriction spectrum imaging (RSI), and time-dependent DWI, provide further quantification of micro-perfusion and tissue heterogeneity. This review covers technical advancements in DWI (including optimized single-shot echo-planar imaging, ultra-high b-value DWI, and synthetic DWI), its diagnostic performance [RSI achieves an area under the curve (AUC) of up to 0.982; DWI combined with other MRI sequences boosts the AUC to 0.960], and its clinical applications (predicting neoadjuvant chemotherapy response, with an AUC of up to 0.840; correlating with biomarkers such as estrogen receptor, progesterone receptor, and Ki-67; and non-contrast screening with a 99.5% negative predictive value). Artificial intelligence (AI) and radiomics further enhance its utility, with two-dimensional convolutional neural networks performing on par with radiologists (AUC ≈ 0.88). Limitations include artifacts, parameter variability, and poor resolution. Future directions involve ultra-high field MRI and AI-driven analysis. DWI is pivotal for personalized breast cancer care, though standardized protocols and further refinement are needed for broader clinical adoption.