title: Predicting ovarian malignancy: application of artificial neural networks to transvaginal and color Doppler flow US.
abstract: OBJECTIVE
  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).
  
  
  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).
  
  
  CONCLUSIONS
  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: Division of Obstetrics and Gynecology, Santa Maria Annunziata Hospital, Università di Firenze, Florence, Italy.
authors: R
authors: R
authors: Biagiotti
authors: C
authors: C
authors: Desii
authors: E
authors: E
authors: Vanzi
authors: G
authors: G
authors: Gacci
mesh_terms: D000328:Adult; D005260:Female; D006801:Humans; D016015:Logistic Models; D008875:Middle Aged; D016571:Neural Networks, Computer; D010051:Ovarian Neoplasms / Q000000981:diagnostic imaging* / Q000453:epidemiology; D011237:Predictive Value of Tests; D012680:Sensitivity and Specificity; D018615:Ultrasonography, Doppler, Color / Q000706:statistics & numerical data