Combination of multiple serum markers using an artificial neural network to improve specificity in discriminating malignant from benign pelvic masses

Zhen Zhang, Stephen D. Barnhill, Hong Zhang, Fengji Xu, Yinhua Yu, Ian Jacobs, Robert P. Woolas, Andrew Berchuck, K. R. Madyastha, Robert C. Bast

Research output: Contribution to journalArticlepeer-review

Abstract

A panel of four selected tumor markers, CA 125 II, CA 72-4, CA 15-3, and lipid-associated sialic acid, was analyzed collectively using an artificial neural network (ANN) approach to differentiate malignant from benign pelvic masses. A dataset of 429 patients, 192 of whom had malignant histology, was retrospectively used in the study. A prototype ANN classifier was developed using a subset of the data which included 73 patients with malignant conditions and 101 patients with benign conditions. The ANN classifier demonstrated a much improved specificity over that of the assay CA 125 II alone (87.5% vs 68.4%) while maintaining a statistically comparable sensitivity (79.0% vs 82.4%) in discriminating malignant from benign pelvic masses in an independent validation test using data from the remaining 255 patients which had been set aside and kept blind to the developers of the ANN system. A similar improvement in specificity was observed among patients under 50 years of age (82.3% vs 62.0%). The ANN system was further tested using additional serum specimens collected from 196 apparently healthy women. The ANN system had a specificity of 100.0% compared to that of 94.8% with the assay CA 125 II alone.

Original languageEnglish (US)
Pages (from-to)56-61
Number of pages6
JournalGynecologic oncology
Volume73
Issue number1
DOIs
StatePublished - Apr 1999
Externally publishedYes

ASJC Scopus subject areas

  • Oncology
  • Obstetrics and Gynecology

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