Mark Gesley, Spynsite LLC, Oakland, CA, Robert Goldsby, UCSF Benio ff Children’s Hospital, San Francisco, CA,
Stephen Lane, UC Davis, Sacramento, CA, and Romin Puri, Spynsite LLC, Oakland, CA
ABSTRACT
New forms of cancer cell identifi cation coupled with faster detection and better accuracy may enhance diagnostic capabilities. The purpose of this study is to improve recognition of minimal residual disease from peripheral blood samples. Spectral images are generated by optical microscopy using ltered broadband visible light elastically scattered from human blood and cancer cells. Exogenous tags, like CD markers may introduce a label bias and
are not required. A training cell may be validated without detailed knowledge of intra-cellular spectra used to classify random cells. Spectral object classi cation is scalable to any number of cell types. Small samples of erythrocytes, leukocytes, Jurkat cancer and non-small lung cell adenocarcinoma are accurately classi ed and associated with unique spatial-spectral characteristics.