Computer Aid for the Decision to Biopsy Breast Lesions.

摘要

The goal of this project is to improve the accuracy of the diagnosis of breast cancer from mammograms by using a computer-based system to provide the physician with a second opinion on whether or not to perform a biopsy. An estimated 2% to 10% of true cancers are not biopsied but are instead followed, while between 60% and 90% of breast biopsies are performed on benign lesions. This report documents progress that has been made in improving the accuracy of diagnoses from mammograms using a Case-Based Reasoning (CBR) approach. The CBR approach predicts the outcome of a biopsy from the known biopsy outcomes for similar cases. The current version of the CBR performs with an accuracy of 61% on a retrospective set of consecutive cases for which the clinical diagnostic accuracy was 35%. The CBR algorithm has four fundamental tasks: (1) specify a reference set of cases, (2) define a metric for the distance between cases, (3) define a rule (based on the distance metric) for selecting 'similar' cases from the reference set, and (4) specify a classification technique for predicting the outcome of biopsy from the known outcomes of the selected similar reference cases. The reference database for this study contained about 1500 cases that were referred for biopsy at Duke University Medical Center between 1992 and 2000. Each case included the mammographer's description of the lesion using the BI-RADS (TM) lexicon, known risk factors, and outcomes in the form of benign or malignant status as determined by biopsy. The author concludes that CBR is a promising technique for identifying benign cases. (7 tables, 9 figures, 11 refs. ).