1. A method of predicting prostate cancer progression, comprising: (a) obtaining prostate cells from a subject; (b) analyzing predictive parameters in the prostate cells, wherein the predictive parameters are nuclear morphometric descriptors, including: object sum optical density, picograms of DNA, contrast, correlation, sum average, sum variance, difference variance, difference entropy, information measure B, product moment, standard deviation, and DNA ploidy; and (c) predicting cancer progression by statistical analysis of the predictive parameters, where the statistical analysis is logistic regression, discriminate analysis, recursive partitioning, neural network, or classification and regression tree analysis.