Computer Aid for the Decision to Biopsy Breast Lesions
Annual rept. 1 Jul 2000-30 Jun 2001
DUKE UNIV MEDICAL CENTER DURHAM NC
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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 mammographer 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 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 mammographers 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. At a sensitivity of 0.98 relative to all biopsied lesions, the specificity of CBR was found to be 0.4. Thus, through the use of CBR 40 of the benign biopsies could have been avoided at the cost of delaying diagnosis for 2 of the malignancies. The results demonstrate the feasibility of developing CBR as a decision aid for breast biopsy using the BI-RADS lexicon to index the cases. 5 tables, 6 figures, 11 refs.
- Medicine and Medical Research
- Statistics and Probability