Open AccessComputerized Diagnosis of Breast Fine‐Needle Aspirates
Author(s) -
Wolberg William H.,
Street W. Nick,
Mangasarian Olvi L.
Publication year - 1997
Publication title -
the breast journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.533
H-Index - 72
eISSN - 1524-4741
pISSN - 1075-122X
DOI - 10.1111/j.1524-4741.1997.tb00145.x
Subject(s) - medicine , malignancy , radiology , breast imaging , rendering (computer graphics) , medical physics , artificial intelligence , mammography , breast cancer , pathology , computer science , cancer
The goal of this work is to determine the accuracy of computer‐based image analysis in diagnosing breast fine‐needle aspirates (FNA). On 192 FNAs, the computer‐based diagnostic accuracy was 97.9%. This is consistent with the 97.5% accuracy projected by machine learning methods during the initial training in 1994 with 569 FNAs. One of the attributes of this system is the rendering of a value that estimates the probability of malignancy. We consider estimated probability of malignancy values between 0.30 and 0.70 to be equivocal. Eleven of our samples (5.7%) fell into this equivocal category. All computer misclassified FNAs were correctly diagnosed visually based on contextual features. We propose this computer‐based system as a diagnostic adjunct rather than as a stand‐alone system.