Likelihood Ratio Classifier for Computer-Aided Diagnosis in Mammography

摘要

In this research we developed a highly sensitive and specific computer-aided diagnosis classifier based on the likelihood ratio (LRb). The classifier is designed to aid physicians to identify mammographic lesions that should not be sent to biopsy. The classifier was developed using a large database of over five thousand breast biopsy cases from several medical centers. As a result of our research, we have developed a likelihood ratio classifier that can predict biopsy outcome for mass lesions. The performance of the classifier has been tested rigorously including testing on data that was not used for training, and also on data that originated from different medical centers. The results suggest that the LRb is a robust classifier for prediction of biopsy outcome. By decreasing the number of benign mass cases sent to biopsy, the classifier could be a valuable tool for physicians and ultimately beneficial to hospitals and patients.