Data fusion of several support-vector-machine breast-cancer diagnostic paradigms using a GRNN oracle
Walker H. Land, Lut Wong, D.W. McKee, Timothy D. Masters, Frances R. Anderson, Sapan Sarvaiya · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2004
Breast cancer is second to lung cancer as a tumor-related cause of death in women. For 2003, it was reported that 211,300 new cases and 39,800 deaths would occur in the US. It has been proposed that breast cancer mortality could be decreased by 25% if women in appropriate age groups were screened regularly. Currently, the preferred method for breast cancer screening is mammography, due to its widespread availability, low cost, speed, and non-invasiveness. At the same time, while mammography is sensitive to the detection of breast cancer, its positive predictive value (PPV) is low, resulting in costly, invasive biopsies that are only 15-34% likely to reveal malignancy at histologic examination. This paper explores the use of a newly designed Support Vector Machine (SVM)/Generalized Regression Neural Network (GRNN) Oracle hybrid and evaluates the hybrid’s performance as an interpretive aid to radiologists. The authors demonstrate that this hybrid has the potential to (1) improve both specificity and PPV of screen film mammography at 95-100% sensitivity, and (2) consistently produce partial AZ values (defined as average specificity over the top 10% of the ROC curve) of greater than 30%, using a data set of ~2500 lesions from five different hospitals and/or institutions.