Application of backpropagation neural networks to diagnosis of breast and ovarian cancer.

Neural network programs have been developed in an attempt to improve the diagnosis of breast and ovarian cancer using a group of laboratory tests and the age of the patient. The laboratory tests employed in this study include albumin, cholesterol, HDL-cholesterol, triglyceride, apolipoproteins A1 and B, NMR linewidth (the Fossel Index) and a tumor marker (i.e., CA 15-3 or CA 125). The breast cancer study involved 104 patients (45 malignant and 59 benign subjects). The ovarian cancer study involved 98 individuals (35 malignant, 36 benign and 27 control subjects). Methods are outlined for identification of the most influential input parameters and optimization of network structure and training. Network characteristics were contrasted with the test results of the appropriate serum tumor marker assay. For the breast cancer study, the best neural network program, using six input parameters, had a sensitivity of only 55.6% and a specificity of 72.9%. The tumor marker CA 15-3 alone gave results of 61.3% and 64.4%, respectively. For the ovarian cancer study, the best neural network program, using six input parameters, had a sensitivity of 80.6% and a specificity of 85.5%. The tumor marker CA 125 alone gave results of 77.8% and 82.3%, respectively. These methods provide an objective approach to neural network optimization and parameter selection applicable to other data bases of clinical and laboratory data.