Does a Mammography CAD Algorithm with Varying Filtering Levels of Detection Marks, Used to Reduce the False Mark Rate, Adversely Affect the Detection of Small Masses?

Abstract

This study evaluates the performance of an advanced CAD algorithm that is capable of filtering detection marks by level-of-suspicion. The detection of small invasive cancers, which has the greatest impact on breast cancer survival, was investigated. The advanced algorithm (Siemens) permits the radiologist to toggle back and forth between different levels of filtering, and focus on the detection marks that appear or disappear as findings with lower or higher levels of suspicion are displayed. The performance of the algorithm at three levels of filtering was evaluated, by lesion size and breast composition. 149 malignant cases with 151 masses (60 small masses) and 528 normal cases were analyzed. When the highest level of filtering was applied, and only findings most indicative of malignancy were displayed, the number of false masses was markedly reduced by 37.8%, while the detection sensitivity for masses was slightly reduced from 84.8% to 80.1%, and similarly, the detection sensitivity for small masses in dense breast decreased from 69.6% to 65.2%. In conclusion, when using an algorithm with filtering capabilities, designed to draw radiologists’ attention to prompts most indicative of malignancy, the substantial reduction in false marks offsets the slight reduction in the detection sensitivity. Reducing the false mark rate by altering the level of filtering of the CAD prompts, does not have a selectively adverse impact on the detection sensitivity of small masses.