Neural networks in the diagnosis of malignant ovarian tumours

Br J Obstet Gynaecol. 1999 Oct;106(10):1078-82. doi: 10.1111/j.1471-0528.1999.tb08117.x.


Objective: To assess the role of neural networks in predicting the likelihood of malignancy in women presenting with ovarian tumours.

Design: Retrospective case study.

Setting: University Department of Obstetrics and Gynaecology, St James's Hospital, Leeds.

Methods: Information from 217 cases with histologically proven benign, borderline or malignant tumours was extracted for study. Four variables (age, ultrasound findings with and without colour Doppler imaging and CA125) were entered in the neural network classifier. The neural network results were compared with logistic regression analysis.

Results: When used in the neural network the variables of age, CA125 and ultrasound score produced the best result with a sensitivity of 95% and a corresponding specificity of 78% in predicting malignancy. Logistic regression gave a sensitivity or 82% for a specificity of 51%.

Conclusion: The neural network is a good method of combining diagnostic variables and may be a useful predictor of malignancy in women presenting with ovarian tumours. A comparison of the performance of the neural network with conventional diagnostic methods would be warranted prior to use in clinical practice.

MeSH terms

  • Adolescent
  • Adult
  • Aged
  • Aged, 80 and over
  • CA-125 Antigen / blood
  • Decision Making
  • Diagnosis, Computer-Assisted / methods*
  • Female
  • Humans
  • Middle Aged
  • Neural Networks, Computer*
  • Ovarian Neoplasms / blood
  • Ovarian Neoplasms / diagnostic imaging*
  • Retrospective Studies
  • Sensitivity and Specificity
  • Ultrasonography


  • CA-125 Antigen