Combination of multiple serum markers using an artificial neural network to improve specificity in discriminating malignant from benign pelvic masses

A panel of four selected tumor markers, CA 125 II, CA 72-4, CA 15-3, and li pid-associated sialic acid, was analyzed collectively using an artificial n eural network (ANN) approach to differentiate malignant from benign pelvic masses. A dataset of 429 patients, 192 of whom had malignant histology, was retrospectively used in the study. A prototype ANN classifier was develope d using a subset of the data which included 73 patients with malignant cond itions and 101 patients with benign conditions. The ANN classifier demonstr ated a much improved specificity over that of the assay CA 125 II alone (87 .5% vs 68.4%) while maintaining a statistically comparable sensitivity (79. 0% vs 82.4%) in discriminating malignant from benign pelvic masses in an in dependent validation test using data from the remaining 255 patients which had been set aside and kept blind to the developers of the ANN system. A si milar improvement in specificity was observed among patients under 50 years of age (82.3% vs 62.0%). The ANN system was further tested using additiona l serum specimens collected from 196 apparently heal-thy women, The ANN sys tem had a specificity of 100.0% compared to that of 94.8% with the assay CA 125 II alone, (C) 1999 Academic Press.