Computer-aided, case-based diagnosis of mammographic regions of interest containing microcalcifications

Abstract

The purpose of this study was to evaluate the effectiveness of a mapped-database diagnostic system in reducing the incidence of benign biopsies and misdiagnosed cancers among mammographic regions of interest (ROIs). A novel neural network was devised (a) to respond to a query ROI by recommending to biopsy or not to biopsy and (b) to map each ROI in the database as a dot on a computer screen. The network was designed so that clusters in the array of dots help the radiologist to find proved ROIs visually similar to the query ROI. This mapped-database diagnostic system was restricted to ROIs with visible microcalcifications. The neural network was trained with a stored database of 80 biopsy-proved ROIs. Four radiologists acting independently on 100 ROIs recommended biopsies for 18, 15, 28, and 18 benign ROIs and misdiagnosed cancers in 11, 12, 7, and eight ROIs, respectively. Interaction with the mapped-database system reduced the numbers of benign biopsies to 11, eight, 18, and 10 cases and of misdiagnosed cancers to eight, seven, four, and three cases, respectively. Statistical analysis indicated that three radiologists achieved significant improvements at P < or = .02 and the fourth achieved a substantial improvement at P < or = .07. By using a mapped database of proved mammographic ROIs containing microcalcifications, radiologists may statistically significantly reduce the numbers of benign biopsies and misdiagnosed cancers.