Prediction of prostate cancer progression by analysis of selected predictive parameters

A method for predicting the recurrence of prostate cancer following radical prostatectomy comprising the steps of:

• (a) obtaining prostate cells from a subject;
• (b) generating nuclear morphometric descriptors for the cells, 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 the recurrence of prostate cancer in the cell samples by statistical analysis of the nuclear morphometric descriptors, where the statistical analysis is logistic regression, discriminate analysis, recursive partitioning, neural network, or classification and regression tree analysis.

A method of predicting the occurrence of fatal metastatic prostate disease comprising the steps of:

• (a) obtaining prostate cells from a subject;
• (b) generating nuclear morphometric descriptors for the cells, including:
• object sumn optical density, picograms of DNA, contrast, correlation, sum average, sum variance, difference variance, difference entropy, information measure B, product moment, and standard deviation;
• and
• (c) predicting the occurrence of fatal metastatic prostate disease by statistical analysis of the nuclear morphometric descriptors.

A method of predicting the progression of prostate cells from a normal state to a malignant state comprising the steps of:

• (a) obtaining prostate cells from a subject;
• (b) generating nuclear morphometric descriptors for the cells;
• (c) analyzing selected cell biomarkers;
• and
• (d) predicting the progression of the cells by using multivariate statistical modeling of the nuclear morphometric descriptors and the selected biomarkers.

A method of determining prostate cancer progression comprising:

• (a) providing a neural network;
• (b) training the neural network using predictive parameters, obtained from prostate cells known to progress and a set of predictive parameters obtained from prostate cells known not to progress, the predictive parameters comprising nuclear morphometric descriptors;
• (c) analyzing dredictive parameters in tumor cells of an individual having an unknown state of cancer progression;
• and
• (d) predicting cancer progression in cells of the individual having an unknown state of cancer progression using the predictive parameters and the trained neural network.

A method of predicting the progression of prostate cancer comprising the steps of:

• (a) obtaining prostate cells from a subject;
• (b) analyzing predictive parameters from the prostate cells, the predictive parameters including nuclear morphometric descriptors;
• (c) utilizing statistical analysis to determine multivariately significant nuclear morphometric descriptors to calculate a quantitative nuclear grade;
• and
• (d) predicting the probability of progression in the patient by statistical analysis of the quantitative nuclear grade.