A knowledge-based approach to CADx of mammographic masses

Today, mammography is recognized as the most effective technique for breast cancer screening. Unfortunately, the low positive predictive value of breast biopsy examinations resulting from mammogram interpretation leads to many unnecessary biopsies performed on benign lesions. In the last years, several computer assisted diagnosis (CADx) systems have been proposed with the goal to assist the radiologist in the discrimination of benign and malignant breast lesions and thus to reduce the high number of unnecessary biopsies. In this paper we present a novel, knowledge-based approach to the computer aided discrimination of mammographic mass lesions that uses computer-extracted attributes of mammographic masses and clinical data as input attributes to a case-based reasoning system. Our approach emphasizes a transparent reasoning process which is important for the acceptance of a CADx system in clinical practice. We evaluate the performance of the proposed system on a large publicly available mammography database using receiver operating characteristic curve analysis. Our results indicate that the proposed CADx system has the potential to significantly reduce the number of unnecessary breast biopsies in clinical practice.