Conventional, time‐dependent, and continuous‐time random‐walk diffusion‐weighted imaging models in microstructural characterization of breast lesions at 3.0T: A prospective analysis

Abstract Background Advanced diffusion models have been introduced to improve characterization of tissue microstructure in breast cancer assessment. Purpose This study aimed to evaluate the diagnostic utility of monoexponential apparent diffusion coefficient (ADC), time‐dependent diffusion magnetic resonance imaging (td‐dMRI), and the Continuous‐Time Random‐Walk (CTRW) diffusion model for differentiating breast lesions and predicting Ki‐67 expression levels. Methods Fifty‐three consecutive patients with suspected breast lesions undergoing preoperative MRI were enrolled in this prospective investigation. Each participant underwent conventional diffusion‐weighted imaging (DWI), CTRW, and td‐dMRI acquisition. From conventional DWI, ADC mean , ADC min , and ADC max were extracted from two‐dimensional lesion regions of interest, and the intralesional ADC difference (ADC max − ADC min ) was computed. CTRW analysis involved whole‐lesion histograms to quantify temporal heterogeneity (α), spatial heterogeneity (β), and the anomalous diffusion coefficient (D). td‐dMRI data were fitted using the JOINT model to derive five microstructural parameters, with PGSE 50ms also obtained. Group comparisons of diffusion parameters between benign and malignant lesions were performed using Mann–Whitney U tests, followed by correlation analyses with Ki‐67. Bonferroni correction was applied to account for multiple testing, with p < 0.05 indicating statistical significance. Logistic regression was employed to combine significant parameters, and diagnostic performance was assessed via receiver operating characteristic (ROC) analysis. Results The td‐dMRI‐derived f in and cellularity, alongside various CTRW‐based histogram parameters, demonstrated statistically significant distinctions between benign and malignant breast lesions (all adjusted p < 0.05, Bonferroni correction). Among all evaluated models, the combined CTRW metrics yielded the highest area under the ROC curve (AUC) (0.975), indicating markedly improved diagnostic efficacy compared to conventional DWI (all p < 0.05). Diffusion metrics generated from ADC, α, and td‐dMRI maps were significantly associated with Ki‐67 expression ( ρ = 0.39–0.62, all p < 0.05). Conclusions Diffusion parameters derived from conventional DWI, td‐dMRI, and CTRW mapping demonstrate potential in characterizing breast lesion microstructure. Nevertheless, validation in larger cohorts remains necessary to substantiate their clinical utility.