The aim of this study was to investigate the potential of texture analysis, applied to dynamic contrast-enhanced MRI (DCE-MRI), to predict the clinical and pathological response to neoadjuvant chemotherapy (NAC) in patients with locally advanced breast cancer (LABC) before NAC is started. Fifty-eight patients with LABC were classified on the basis of their clinical response according to the Response Evaluation Criteria in Solid Tumors (RECIST) guidelines after four cycles of NAC, and according to their pathological response after surgery. T1 -weighted DCE-MRI with a temporal resolution of 1 min was acquired on a 3-T Siemens Trio scanner using a dedicated four-channel breast coil before the onset of treatment. Each lesion was segmented semi-automatically using the 2-min post-contrast subtracted image. Sixteen texture features were obtained at each non-subtracted post-contrast time point using a gray level co-occurrence matrix. Appropriate statistical analyses were performed and false discovery rate-based q values were reported to correct for multiple comparisons. Statistically significant results were found at 1-3 min post-contrast for various texture features for the prediction of both the clinical and pathological response. In particular, eight texture features were found to be statistically significant at 2 min post-contrast, the most significant feature yielding an area under the curve (AUC) of 0.77 for response prediction for stable disease versus complete responders after four cycles of NAC. In addition, four texture features were found to be significant at the same time point, with an AUC of 0.69 for response prediction using the most significant feature for classification based on the pathological response. Our results suggest that texture analysis could provide clinicians with additional information to increase the accuracy of prediction of an individual response before NAC is started.

• T2W MRI-derived textural features correlate significantly with Gleason score and ADC. • T2W MRI-derived textural features differentiate Gleason score 3+4 from 4+3 cancers. • T2W image textural features could augment tumour characterization.

Geometric distortion correction in DWI of the breast results in higher similarity of DWI to anatomical non-EPI T2 -weighted images and would potentially allow for a more reliable lesion segmentation mapping among different MRI modalities.