Spectrum analysis of ultrasonic radio-frequency echo signals has proven to be an effective means of characterizing tissues of the eye and liver, thrombi, plaque, etc. Such characterization can be of value in detecting, differentiating, and monitoring disease. In some clinical applications, linear methods of tissue classification cannot adequately differentiate among the various manifestations of cancerous and non-cancerous tissue; in these cases, non-linear methods, such as neural-networks, are required for tissue typing. Combining spectrum-analysis methods for quantitatively characterizing tissue properties with neural-network methods for classifying tissue, a powerful new means of guiding biopsies, targeting therapy, and monitoring treatment may be available. Current studies are investigating potential applications of these methods that use novel tissue-typing images presented in two and three dimensions. Results to date show significant sensitivity improvements of possible benefit in cancer detection and effective tissue-type imaging that promise improved means of planning and monitoring treatment of prostate cancer.