Decision quality support in diagnostic breast ultrasound through artificial Intelligence

摘要

Medical Ultrasonography is a valuable imaging technology for medical diagnostics and, more recently, as a screening alternative to mammography for women with dense breasts. However, ultrasound imaging within the contexts of both diagnostic and screening mammography suffers from inter-operator and intra-operator variability. Consequently, there is a broad distribution of performance profiles, even for radiologists of similar training. Typically, these profiles tend to err on the side of caution, preferring false positive errors to false negative errors. While this approach may lead to a higher Cancer Detection Rate (CDR), it also lowers the Positive Predictive Value (PPV3) of performed biopsies. A lower PPV3 translates to an increase in benign biopsies, the annual cost of which are estimated to be on the order of 1−3 billion USD (not including pathological workups). And, of course, there is the immeasurable cost of pain, worry, and suffering borne by women undergoing these potentially unnecessary procedures. In this paper, we evaluate the ability of the ClearView cCAD algorithms to increase overall performance and reduce the inter-operator variance on a set of imaged lesions. The cCAD system provides an automated assessment of some ACR BI-RADs criteria and calculates a preliminary BI-RADs assessment, given as BI-RADS categorical bucket (1-3) or (4-5). Through the evaluation of 1300 breast lesion images, 3 MQSA certified radiologists were asked to determine both a Likelihood of Malignancy (LoM) and a BI-RADs assessment, from which their ROC curve AUC as well as PPV3 could be calculated. The cCAD system was also evaluated, on the same set of lesions, by a similar set of metrics. From this analysis we have been able to show that the cCAD system outperforms radiologists at all operating points within the scope of this study design. Furthermore, we've shown that through simple fusion schemes we are able to increase performance beyond that of either the cCAD system or the radiologist alone by all typically tracked quality metrics, and significantly reduce inter-operator variance.