We are using backscatter-based approaches to improve diagnostics for cancer detection and identifying the response of breast cancer patients to chemotherapy. Backscatter approaches include estimation of scatterer properties, like the effective scatterer diameter (ESD) and effective acoustic concentration (EAC), from the backscatter coefficient and from envelope statistics. Figure 1 shows an image of a set of tumors using ESD image maps. We are integrating QUS on a novel tomography breast scanner (QT Imaging). Figure 2 shows an image of a tissue-mimicking phantom scanned in a QT scanner with full angular compounding to reduce the variance of QUS estimates. We are also developing techniques using deep learning on raw backscattered RF ultrasound data to classify tissues. We have developed transfer functions between scanner settings and between systems to transfer classifiers from one setting/system to another. Our collaborators include Dr. Czarnota from Sunnybrook in Toronto and QT Imaging. (Last Updated: 12/19/2022).