A comparison between physicians' interpretation and a CAD system's cancer detection by using a mammogram database in a physicians' self-learning course

Abstract

We have been developing automated detection algorithms for masses and clustered microcalcifications in a mammography computer-aided diagnosis (CAD) system. In this study, we investigated the potential of our CAD system by comparing 579 physicians' interpretation results with that of the CAD system's cancer detection for 100 mammograms (21 malignant and 29 benign cases) employed in a physicians' self-learning course. As a result, our CAD system detected 7 out of 8 malignant lesions whose physicians' averaged sensitivity was less than 60%. Although the average of physicians' sensitivities were 76% (about 16 cases), the CAD system's detection rate was 90% (19 cases). Sensitivity was raised up to 97% if the physicians' interpretation and the CAD system's detection result were treated in a matter of logical OR. Thus, it was raised the possibility that even the less-experienced physicians would diagnose with a higher sensitivity by using the computer output as a guide effectively.