Radiofrequency ultrasound data were collected from 60 breast cancer patients prior to treatment and at during the onset of their several-month treatment, using a clinical ultrasound scanner operating a ~7 MHz linear array probe. ACE, SAS, spectral, and BSC parameters were computed from 2 × 2 mm RF segments within the tumor region of interest (ROI) and averaged over all segments to obtain a mean value for the ROI. The results were separated into two groups—responders and non-responders—based on the ultimate clinical/pathologic response based on residual tumor size and tumor cellularity. Using a single parameter approach, the best prediction of response was achieved using the ACE parameter (76% accuracy at week 1). In general, more favorable classifications were achieved using spectral parameter combinations (82% accuracy at week 8), compared to BSC parameter combinations (73% accuracy). Using the multi-parameter approach, the best prediction was achieved using the set [MBF, SS, SAS, ACE] and by combining week 1 QUS data with week 4 QUS data to predict the response at week 4, providing accuracy as high as 91%. The proposed QUS method may potentially provide early response information and guide cancer therapies on an individual patient basis.