ObjectivesTo assess the feasibility of the mono-exponential, bi-exponential and stretched-exponential models in evaluating response of breast tumours to neoadjuvant chemotherapy (NACT) at 3 T.MethodsThirty-six female patients (median age 53, range 32–75 years) with invasive breast cancer undergoing NACT were enrolled for diffusion-weighted MRI (DW-MRI) prior to the start of treatment. For assessment of early response, changes in parameters were evaluated on mid-treatment MRI in 22 patients. DW-MRI was performed using eight b values (0, 30, 60, 90, 120, 300, 600, 900 s/mm2). Apparent diffusion coefficient (ADC), tissue diffusion coefficient (D
t), vascular fraction (ƒ), distributed diffusion coefficient (DDC) and alpha (α) parameters were derived. Then t tests compared the baseline and changes in parameters between response groups. Repeatability was assessed at inter- and intraobserver levels.ResultsAll patients underwent baseline MRI whereas 22 lesions were available at mid-treatment. At pretreatment, mean diffusion coefficients demonstrated significant differences between groups (p < 0.05). At mid-treatment, percentage increase in ADC and DDC showed significant differences between responders (49 % and 43 %) and non-responders (21 % and 32 %) (p = 0.03, p = 0.04). Overall, stretched-exponential parameters showed excellent repeatability.ConclusionDW-MRI is sensitive to baseline and early treatment changes in breast cancer using non-mono-exponential models, and the stretched-exponential model can potentially monitor such changes.Key points• Baseline diffusion coefficients demonstrated significant differences between complete pathological responders and non-responders.
• Increase in ADC and DDC at mid-treatment can discriminate responders and non-responders.
• The ƒ fraction at mid-treatment decreased in responders whereas increased in non-responders.
• The mono- and stretched-exponential models showed excellent inter- and intrarater repeatability.
• Treatment effects can potentially be assessed by non-mono-exponential diffusion models.
Electronic supplementary materialThe online version of this article (doi:10.1007/s00330-016-4630-x) contains supplementary material, which is available to authorized users.