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 distinguish normal cells from cells having undergone a biochemical change. A pre-determined vector space is selected where the vector space mathematically describes a first plurality of reference spectral data sets for normal cells and a second plurality of reference spectral data sets for cells having undergone a biochemical change. A sample is irradiated to generate a target spectral data set based on photons absorbed, reflected, emitted, or scattered by the sample. The target spectral data set is transformed into a pre-determined vector space. A distribution of transformed data is analyzed in the pre-determined vector space. Based on the analysis, the sample is classified as containing normal cells, cells having undergone a biochemical change, and combinations thereof. The method includes treating the sample with a pharmaceutical agent prior to irradiating the sample and using the classification to assess the efficiency of the pharmaceutical agent.

Abstract: A system and method to distinguish normal cells from apoptotic cells. A pre-determined vector space is selected where the vector space mathematically describes a first plurality of reference Raman data sets for normal cells and a second plurality of reference Raman data sets for apoptotic cells. A sample is irradiated with substantially monochromatic light generating a target Raman data set based on scattered photons. The target Raman data set is transformed into a vector space defined by the pre-determined vector space. A distribution of transformed data is analyzed in the pre-determined vector space. Based on the analysis, the sample is classified as containing normal cells, apoptotic cells, and a combination of normal and apoptotic cells. The sample includes the step of treating the sample with a pharmaceutical agent prior to irradiating the sample. Based on the classification, the therapeutic efficiency of the pharmaceutical agent is assessed.

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.