Predicting Ovarian Malignancy: Application of Artificial Neural Networks to Transvaginal and Color Doppler Flow US

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Abstract
PURPOSE:To compare the performance of artificial neural networks (ANNs) with that of multiple logistic regression (MLR) models for predicting ovarian malignancy in patients with adnexal masses by using transvaginal B-mode and color Doppler flow ultrasonography (US). MATERIALS AND METHODS: A total of 226 adnexal masses were examined before surgery: Fifty-one were malignant and 175 were benign. The data were divided into training and testing subsets by using a "leave n out method." The training subsets were used to compute the optimum MLR equations and to train the ANNs. The cross-validation subsets were used to estimate the performance of each of the two models in predicting ovarian malignancy. RESULTS: At testing, three-layer back-propagation networks, based on the same input variables selected by using MLR tie, women's ages, papillary projections, random echogenicity, peak systolic velocity, and resistance index), had a significantly higher sensitivity than did MLR (96% vs 84%; McNemar test, P =.04). The Brier scores for ANNs were significantly lower than those calculated for MLR (Student t test for paired samples, P =.004). CONCLUSION: ANNs might have potential for categorizing adnexal masses as either malignant or benign on the basis of multiple variables related to demographic and US features.