Predicting ovarian malignancy: application of artificial neural networks to transvaginal and color Doppler flow US

Radiology. 1999 Feb;210(2):399-403. doi: 10.1148/radiology.210.2.r99fe18399.

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.

Publication types

  • Comparative Study

MeSH terms

  • Adult
  • Female
  • Humans
  • Logistic Models
  • Middle Aged
  • Neural Networks, Computer*
  • Ovarian Neoplasms / diagnostic imaging*
  • Ovarian Neoplasms / epidemiology
  • Predictive Value of Tests
  • Sensitivity and Specificity
  • Ultrasonography, Doppler, Color* / statistics & numerical data