Prediction of breast biopsy outcome using a likelihood ratio classifier and biopsy cases from two medical centers
Potential malignancy of a mammographie lesion can be assessed using the mathematically optimal likelihood ratio (LR) from signal detection theory. We developed a LR classifier for prediction of breast biopsy outcome of mammographie masses from BI-RADS findings. We used cases from Duke University Medical Center (645 total, 232 malignant) and University of Pennsylvania (496, 200). The LR was trained and tested alternatively on both subsets. Leave-one-out sampling was used when training and testing was performed on the same data set. When tested on the Duke set, the LR achieved a Received Operating Characteristic (ROC) area of 0.91 ± 0.01, regardless of whether Duke or Pennsylvania set was used for training. The LR achieved a ROC area of 0.85 ± 0.02 for the Pennsylvania set, again regardless of which set was used for training. When using actual case data for training, the LR's procedure is equivalent to case-based reasoning, and can explain the classifier's decisions in terms of similarity to other cases. These preliminary results suggest that the LR is a robust classifier for prediction of biopsy outcome using biopsy cases from different medical centers.
Bilska-Wolak, AO; Floyd, CE; Lo, JY
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