METHODS FOR GENERATING PREDICTIVE MODELS FOR EPITHELIAL OVARIAN CANCER AND METHODS FOR IDENTIFYING EOC

摘要

A method for generating a model for epithelial ovarian cancer is presented, comprising the steps of obtaining a mass spectrum for each of a plurality of samples, segmenting each of the mass spectra into “bins,” and determining a plurality of relationships between two or more bins. One are more statistically significant factors are identified according to the determined plurality of relationships, and a predictive model is generated as a function of the one or more identified factors. A method of the present invention may further comprise the step of obtaining one or more nuclear magnetic resonance spectra of each of the samples, which are segmented into a plurality of bins. Combinations of mass spectra and NMR spectra may be used to determine the plurality of relationships. In other embodiments, methods for identifying the presence of EOC indicated by a biological sample of an individual are presented.

主权项

1. A method of generating a predictive model for diagnosing early-stage epithelial ovarian cancer using a plurality of biological samples, each sample being taken from a different individual having a known disease state of either diseased (“EOC”), benign ovarian cyst (“benign”), or healthy (“healthy”), the method comprising the steps of:
obtaining a mass spectrum of each of the plurality of biological samples; segmenting each spectrum along the mass-to-charge axis to provide a plurality of bins; determining a plurality of relationships between two or more groups of bins, each group of bins comprising one or more bins; identifying one or more statistically significant factors based on the plurality of relationships; and generating a predictive model, wherein the predictive model is a function of the one or more factors.