title: Image analysis and machine learning applied to breast cancer diagnosis and prognosis.
abstract: 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 lymph node 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 10-fold 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 leave-one-out testing.
authors.affiliation: Department of Surgery, University of Wisconsin, Madison, USA.
authors.forename: W H
authors.initials: WH
authors.lastname: Wolberg
authors.forename: W N
authors.initials: WN
authors.lastname: Street
authors.forename: O L
authors.initials: OL
authors.lastname: Mangasarian
mesh_terms: D001707:Biopsy, Needle; D001943:Breast Neoplasms / Q000175:diagnosis*; D005260:Female; D005348:Fibrocystic Breast Disease / Q000175:diagnosis; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D009362:Neoplasm Metastasis; D016571:Neural Networks, Computer; D011379:Prognosis; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity