Accession Number:

ADA346651

Title:

Predicting Time-to-Relapse in Breast Cancer Using Neural Networks

Descriptive Note:

Final rept. 15 Sep 94-14 Nov 97

Corporate Author:

UNIVERSITY OF SOUTHERN CALIFORNIA LOS ANGELES

Personal Author(s):

Report Date:

1997-12-01

Pagination or Media Count:

25.0

Abstract:

We implemented neural network MN algorithms for analysis of censored-data in predicting time to relapse for breast cancer patients, including a generalization of the Buckley-James approach to censored linear regression, and the methods proposed by Faraggi and Simon and Liestol et al. The data set available included 236 women with node negative breast cancer treated with surgery only. In Cox models HER-2neu amplification, tumor size, treatment center, and age were univariately associated with outcome, but only HER-2neu and treatment center significant in multivariate analyses. The recursive partitioning method selected HER-2neu as the strongest predictor, and divided the non-amplified group by treatment center, and the amplified group by nuclear grade.

Subject Categories:

  • Medicine and Medical Research
  • Cybernetics

Distribution Statement:

APPROVED FOR PUBLIC RELEASE