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

¹ Division of Obstetrics and Gynecology, Santa Maria Annunziata Hospital (R.B., C.D., G.G.)
² Departments of Obstetrics and Gynecology (R.B., E.V.)
³ Radiology (E.V.), Università di Firenze, Florence, Italy.

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 (ie, 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.

Index terms: Computers, diagnostic aid • Computers, neural network • Ovary, neoplasms, 852.30 • Ovary, US, 852.1298, 852.12983 • Ultrasound (US), Doppler studies, 852.12983

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