In this chapter, we describe two new methodologies: (1) application of high-frequency ultrasound spectroscopy for in vivo detection of cancer cell death in small animal models, and (2) extension of ultrasound spectroscopy to the lower frequency range (i.e., 1-10 MHz range) for the detection of cell death in vivo in preclinical and clinical settings. Experiments using tumor xenografts in mice and cancer treatments based on chemotherapy are described. Finally, we describe how one can detect cancer response to treatment in patients noninvasively early (within 1 week of treatment initiation) using low-frequency ultrasound spectroscopic imaging and advanced machine learning techniques. Color-coded images of ultrasound spectroscopic parameters, or parametric images, permit the delineation of areas of dead cells versus viable cells using high ultrasound frequencies, and the delineation of areas of therapy response in patient tumors using clinically relevant ultrasound frequencies. Depending on the desired resolution, parametric ultrasound images can be computed and displayed within minutes to hours after ultrasound examination for cell death. A noninvasive and express method of cancer response detection using ultrasound spectroscopy provides a framework for personalized medicine with regards to the treatment planning of refractory patients resulting in substantial improvements in patient survival.
Keywords: Breast cancer response prediction; Computer-aided theragnosis; Machine learning; Quantitative ultrasound; Texture analysis.