R Biagiotti1, C Desii, E Vanzi, G Gacci. 1. Division of Obstetrics and Gynecology, Santa Maria Annunziata Hospital, Università di Firenze, Florence, Italy.
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 (i.e., 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.
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 (i.e., 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.
Authors: Priscyla W Simões; Narjara B Izumi; Ramon S Casagrande; Ramon Venson; Carlos D Veronezi; Gustavo P Moretti; Edroaldo L da Rocha; Cristian Cechinel; Luciane B Ceretta; Eros Comunello; Paulo J Martins; Rogério A Casagrande; Maria L Snoeyer; Sandra A Manenti Journal: Cancer Inform Date: 2014-10-31
Authors: U Rajendra Acharya; S Vinitha Sree; Sanjeev Kulshreshtha; Filippo Molinari; Joel En Wei Koh; Luca Saba; Jasjit S Suri Journal: Technol Cancer Res Treat Date: 2013-12-06