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

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-2/neu amplification, tumor size, treatment center, and age were univariately associated with outcome, but only HER-2/neu and treatment center significant in multivariate analyses. The recursive partitioning method selected HER-2/neu as the strongest predictor, and divided the non-amplified group by treatment center, and the amplified group by nuclear grade.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Dec 01, 1997
Accession Number
ADA346651

Entities

People

  • Jonathan D. Buckley

Organizations

  • University of Southern California

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Algorithms
  • Amplification
  • Biomedical Research
  • Breast Cancer
  • Data Analysis
  • Data Science
  • Data Sets
  • Databases
  • Diseases And Disorders
  • Genetic Algorithms
  • Health Services
  • Information Science
  • Medical Personnel
  • Multivariate Analysis
  • Neoplasms
  • Neural Networks
  • Statistics

Readers

  • Molecular and genetic basis of cancer.
  • Neural Network Machine Learning.
  • Women's Health and Cancer Risk Research: African American Women and Pregnancy Outcomes.

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks