Once the most fit key m/z values are selected, the diagnostic model, identified in the pattern discovery phase, is tested using masked spectra (i.e. the “testing set”). In this so-called pattern-matching phase only the key m/z values and intensities in the feature set identified in the pattern discovery phase are used to classify the unknown samples as being from healthy or cancer-affected individuals. The diagnostic feature set defined in training was able to correctly diagnose the samples as being acquired from either control patients or those suffering from ovarian cancer with asensitivity of 100% and a specificity of 95%, yielding an overall PPV of 94%. The success in correctly deciphering stage I ovarian cancer suggested that proteomic patterns generated from biofluids may provide a useful indicator of the early onset of a particular disease state.