Journal article
Computer-derived nuclear features distinguish malignant from benign breast cytology
Human pathology, Vol.26(7), pp.792-796
07/01/1995
DOI: 10.1016/0046-8177(95)90229-5
PMID: 7628853
Abstract
This article describes the use of computer-based analytical techniques to define nuclear size, shape, and texture features. These features are then used to distinguish between benign and malignant breast cytology. The benign and malignant cell samples used in this study were obtained by fine needle aspiration (FNA) from a consecutive series of 569 patients: 212 with cancer and 357 with fibrocystic breast masses. Regions of FNA preparations to be analyzed were converted by a video camera to computer files that were displayed on a computer monitor. Nuclei to be analyzed were roughly outlined by an operator using a mouse. Next, the computer generated a “snake” that precisely enclosed each designated nucleus. The computer calculated 10 features for each nucleus. The ability to correctly classify samples as benign or malignant on the basis of these features was determined by inductive machine learning and logistic regression. Cross-validation was used to test the validity of the predicted diagnosis. The logistic regression cross validated classification accuracy was 96.2% and the inductive machine learning cross-validated classification accuracy was 97.5%. Our computerized system provides a probability that a sample is malignant. Should this probability fall between 30% and 70%, the sample is considered “suspicious,” in the same way a visually graded FNA may be termed suspicious. All of the 128 consecutive cases obtained since the introduction of this system were correctly diagnosed, but nine benign aspirates fell into the suspicious category. Fifty-seven FNAs were obtained that had been visually diagnosed elsewhere by others as “suspicious.” Eleven (19.3%) were similarly classified as suspicious by the computer, but 84.8% of the remaining samples were correctly diagnosed. The methods described in this article will provide the basis for computerized systems to diagnose breast cytology.
Details
- Title: Subtitle
- Computer-derived nuclear features distinguish malignant from benign breast cytology
- Creators
- William H Wolberg - University of Wisconsin–MadisonW.Nick Street - University of Wisconsin–MadisonDennis M Heisey - University of Wisconsin–MadisonOlvi L Mangasarian - University of Wisconsin–Madison
- Resource Type
- Journal article
- Publication Details
- Human pathology, Vol.26(7), pp.792-796
- Publisher
- Elsevier Inc
- DOI
- 10.1016/0046-8177(95)90229-5
- PMID
- 7628853
- ISSN
- 0046-8177
- eISSN
- 1532-8392
- Language
- English
- Date published
- 07/01/1995
- Academic Unit
- Bus Admin College; Nursing; Computer Science; Business Analytics
- Record Identifier
- 9984380479302771
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