Journal article
Image analysis and machine learning applied to breast cancer diagnosis and prognosis
Analytical and quantitative cytology and histology, Vol.17(2), pp.77-87
04/01/1995
PMID: 7612134
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.
Details
- Title: Subtitle
- Image analysis and machine learning applied to breast cancer diagnosis and prognosis
- Creators
- W H Wolberg - University of Wisconsin–MadisonW N StreetO L Mangasarian
- Resource Type
- Journal article
- Publication Details
- Analytical and quantitative cytology and histology, Vol.17(2), pp.77-87
- PMID
- 7612134
- NLM abbreviation
- Anal Quant Cytol Histol
- ISSN
- 0884-6812
- eISSN
- 2690-7593
- Language
- English
- Date published
- 04/01/1995
- Academic Unit
- Bus Admin College; Nursing; Computer Science; Business Analytics
- Record Identifier
- 9984380440202771
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