BackgroundThe continuous‐time random‐walk (CTRW) diffusion model to evaluate breast cancer prognosis is rarely reported.PurposeTo investigate the correlations between apparent diffusion coefficient (ADC) and CTRW‐specific parameters with prognostic factors and molecular subtypes of breast cancer.Study TypeRetrospective.PopulationOne hundred fifty‐seven women (median age, 50 years; range, 26–81 years) with histopathology‐confirmed breast cancer.Field Strength/SequenceSimultaneous multi‐slice readout‐segmented echo‐planar imaging at 3.0T.AssessmentThe histogram metrics of ADC, anomalous diffusion coefficient (D), temporal diffusion heterogeneity (α), and spatial diffusion heterogeneity (β) were calculated for whole‐tumor volume. Associations between histogram metrics and prognostic factors (estrogen receptor [ER], progesterone receptor [PR], human epidermal growth factor receptor 2 [HER2], and Ki‐67 proliferation index), axillary lymph node metastasis (ALNM), and tumor grade were assessed. The performance of histogram metrics, both alone and in combination, for differentiating molecular subtypes (HER2‐positive, Luminal or triple negative) was also assessed.Statistical TestsComparisons were made using Mann–Whitney test between different prognostic factor statuses and molecular subtypes. Receiver operating characteristic curve analysis was used to assess the performance of mean and median histogram metrics in differentiating the molecular subtypes. A P value <0.05 was considered statistically significant.ResultsThe histogram metrics of ADC, D, and α differed significantly between ER‐positive and ER‐negative status, and between PR‐positive and PR‐negative status. The histogram metrics of ADC, D, α, and β were also significantly different between the HER2‐positive and HER2‐negative subgroups, and between ALNM‐positive and ALNM‐negative subgroups. The histogram metrics of α and β significantly differed between high and low Ki‐67 proliferation subgroups, and between histological grade subgroups. The combination of α_mean and β_mean achieved the highest performance (AUC = 0.702) to discriminate the Luminal and HER2‐positive subtypes.Data ConclusionWhole‐tumor histogram analysis of the CTRW model has potential to provide additional information on the prognosis and intrinsic subtyping classification of breast cancer.Evidence Level4Technical EfficacyStage 2