Method for predicting the future occurrence of clinically occult or non-existent medical conditions

A method of predicting the relapse of cancer that is presently clinically occult or non-existing, comprising:

• providing a neural network;
• data compressing a plurality of DNA flow cytometric histograms generated from tumor cells of patients having known cancer relapse rates to produce a first set of histograms;
• training said neural network using said first set of DNA flow cytometric histograms;
• obtaining at least one DNA flow cytometric histogram from tumor cells of a patient having an unknown cancer relapse rate;
• and
• predicting relapse of cancer in said patient having an unknown relapse rate using said at least one DNA flow cytometric histogram and said trained neural network.

A method of predicting the future occurrence of breast cancer that is presently occult or non-existing, comprising:

• providing a neural network;
• data compressing DNA cytometric data generated from cells of patients having known occurrence of breast cancer to produce a first set of DNA cytometric data;
• training said neural network using said first set of DNA cytometric data and first sets of known data, each of said first sets of known data including a predetermined number of prognostic input variables, and corresponding known breast cancer occurrence, said prognostic input variables chosen according to capability to predict occurrence of breast cancer;
• and
• predicting future occurrence of breast cancer for a second set of DNA cytometric data and second sets of data using said trained neural network, each of said second sets of data including only said predetermined number of prognostic input variables.