Related Experiment Video
Updated: Aug 19, 2026
Ovarian Cancer Detection Using Photoacoustic Flow Cytometry
Published on: January 17, 2020
Predicting ovarian malignancy: application of artificial neural networks to transvaginal and color Doppler flow US
R Biagiotti1, C Desii, E Vanzi
1Division of Obstetrics and Gynecology, Santa Maria Annunziata Hospital, 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 (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.
