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

Abstract

Neural networks (NN) have become established as powerful tools for complex pattern recognition problems. One application which appears well suited to NN methods is the identification of prognostic groups, to be used for treatment planning. For many cancer studies of cancer cell biology have added many factors of potential prognostic value, but the way in which these interact with known factors is generally not well studied. The potential of NNs to model these data in a non-linear fashion has only begun to be explored. NNs are not part of standard statistical packages, making them relatively inaccessible to many statisticians. More importantly, current NN methods cannot accommodate censored outcome variables. This proposal is for application of several proposed methods for applying NNs to censored-data to the problem of predicting time-to-relapse for breast cancer patients. The methods will be evaluation in comparison to each other, and also to more conventional approaches such as Cox regression and recursive partitioning. The data on which the analyses will be conducted comes from the database of the NSABP.

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Document Details

Document Type
Technical Report
Publication Date
Oct 01, 1995
Accession Number
ADA300396

Entities

People

  • Jonathan D. Buckley

Organizations

  • University of Southern California

Tags

DTIC Thesaurus Topics

  • Algorithms
  • Biology
  • Breast Cancer
  • Computer Programming
  • Data Analysis
  • Data Management
  • Databases
  • Genetic Algorithms
  • Identification
  • Materials
  • Neoplasms
  • Neural Networks
  • Pattern Recognition
  • Recombinant Dna
  • Software Development
  • Standards
  • Test And Evaluation

Readers

  • Neural Network Machine Learning.
  • Oncology
  • Statistical inference.

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks