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Papers Published

1. Bilska-Wolak, A.O. and Floyd, C.E., Jr. and Lo, J.Y., Prediction of breast biopsy outcome using a likelihood ratio classifier and biopsy cases from two medical centers, Proc. SPIE - Int. Soc. Opt. Eng. (USA), vol. 5032 (2003), pp. 1386 - 91 [12.481349] .
   (last updated on 2007/04/15)

   Abstract:
   Potential malignancy of a mammographic 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 mammographic 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

   Keywords:
   biological organs;cancer;image classification;mammography;medical image processing;medical signal detection;sensitivity analysis;