Abstract: A system and method to predict the progression of disease of a test sample. A group of known biological samples is provided. Each known biological sample has an associated known outcome including a non-diseased sample or a diseased sample. A Raman data set is obtained for each known biological sample. Each Raman data set is analyzed to identify a diseased or non-diseased reference Raman data set depending on whether respective biological sample is the non-diseased sample or the diseased sample. A first database is generated where the first database contains reference Raman data sets for all diseased samples. A second database is generated where the second database contains reference Raman data sets for all non-diseased samples. A test Raman data set of a test biological sample is received, where the test biological sample has an unknown disease status. A diagnostic is provided as to whether the test sample is a non-diseased sample or a diseased sample.

Abstract: A system and method to predict the progression of disease of a test sample. A group of known biological samples is provided. Each known biological sample has an associated known outcome including a non-diseased sample or a diseased sample. A Raman data set is obtained for each known biological sample. Each Raman data set is analyzed to identify a diseased or non-diseased reference Raman data set depending on whether respective biological sample is the non-diseased sample or the diseased sample. A first database is generated where the first database contains reference Raman data sets for all diseased samples. A second database is generated where the second database contains reference Raman data sets for all non-diseased samples. A test Raman data set of a test biological sample is received, where the test biological sample has an unknown disease status. A diagnostic is provided as to whether the test sample is a non-diseased sample or a diseased sample.