Improved cancer detection in automated breast ultrasound by radiologists using Computer Aided Detection

Objective: To investigate the effect of dedicated Computer Aided Detection (CAD) software for automated breast ultrasound (ABUS) on the performance of radiologists screening for breast cancer. Methods: 90 ABUS views of 90 patients were randomly selected from a multi-institutional archive of cases collected between 2010 and 2013. This dataset included normal cases (n = 40) with >1 year of follow up, benign (n = 30) lesions that were either biopsied or remained stable, and malignant lesions (n = 20). Six readers evaluated all cases with and without CAD in two sessions. CAD-software included conventional CAD-marks and an intelligent minimum intensity projection of the breast tissue. Readers reported using a likelihood-of-malignancy scale from 0 to 100. Alternative free-response ROC analysis was used to measure the performance. Results: Without CAD, the average area-under-the-curve (AUC) of the readers was 0.77 and significantly improved with CAD to 0.84 (p = 0.001). Sensitivity of all readers improved (range 5.2-10.6%) by using CAD but specificity decreased in four out of six readers (range 1.4-5.7%). No significant difference was observed in the AUC between experienced radiologists and residents both with and without CAD. Conclusions: Dedicated CAD-software for ABUS has the potential to improve the cancer detection rates of radiologists screening for breast cancer. (C) 2017 Elsevier B.V. All rights reserved.