Breast cancer computer aided diagnosis (CAD) using a recently developed SVM/GRNN Oracle hybrid

• Published in: SMC'03 Conference Proceedings. 2003 IEEE International Conference on Systems, Man and Cybernetics. Conference Theme - System Security and Assurance (Cat. No.03CH37483) Vol. 5; pp. 4705 - 4711 vol.5
• Main Authors: Land, W.H., Wong, L., Mckee, D.W., Masters, T., Anderson, F.R.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 2003
• Subjects: Biopsy; Breast cancer; Cancer detection; Chromium; Costs; Lesions; Lungs; Mammography; Sensitivity; Support vector machines
• ISBN: 9780780379527; 0780379527
• ISSN: 1062-922X; 2577-1655
• DOI: 10.1109/ICSMC.2003.1245727

Carcinoma of the breast is second only to lung cancer as a tumor-related cause of death in women. For 2003, it has been reported that 211,300 new cases and 39,800 deaths occurred just in the US. It has been proposed, however, that the mortality from breast cancer could be decreased by up to 25% if all women in appropriate age groups were screened regularly. Currently, the method of choice for the early detection of breast cancer is mammography, due to its general 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 histological examination. This paper explores the use of a newly designed support vector machine (SVM)/generalized regression neural network (GRNN) Oracle hybrid and evaluates its performance as an interpretive aid to radiologists. The authors demonstrate that this hybrid has the potential to improve both the specificity and PPV of screen film mammography at 95-100% sensitivity, and consistently produce partial A/sub Z/ values (defined as average specificity over the top 10% of the ROC curve) of grater than 50% using a data set of /spl sim/2000 lesions from four different hospitals. As expected, initial experiments demonstrated that combining age and mass margin (AgeMM) that provided the most accurate diagnostic performance. Secondly, the value of the crossover constant, CR = 0.6, provided the best A/sub Z/, while CR = 0.8 resulted in the most accurate partial A/sub Z/ specificity, and PPV at the lower sensitivities. Finally, the results of the GRNN oracle output were essentially the same as those of the SVM suggesting that the SVMs, as anticipated, had optimized the diagnostic performance. Practically, this means that at 100% sensitivity (which means no cancerous lesions are misdiagnosed) and using a crossover constant of 0.8, approximately 454 biopsies is avoided using this SVM/GRNN oracle diagnostic aid when compared to the circumstance where all 1979 samples were biopsied.