Fine needle aspiration (FNA) accuracy is limited by, among
other factors, the subjective interpretation of the aspirate.  We have
increased breast FNA accuracy by coupling digital image analysis
methods with machine learning techniques.  Additionally, our
mathematical approach captures nuclear features ("grade") that are
prognostically more accurate than are estimates based on tumor size
and lymphnode status.
	An interactive computer system evaluates, diagnoses, and
determines prognosis based on nuclear features derived directly from a
digital scan of FNA slides.  A consecutive series of 569 patients
provided the data for the diagnostic study.  A 166 patient subset
provided the data for the prognostic study.  An additional 75
consecutive, new patients provided samples to test the diagnostic
system.  The projected prospective accuracy of the diagnostic system
was estimated to be 97% by tenfold cross validation and the actual
accuracy on 75 new samples was 100%.  The projected prospective
accuracy of the prognostic system was estimated to be 86% by
leaveoneout testing.