Forecasting the performance status of head and neck cancer patient treatment by an interval arithmetic pruned perceptron

Gian Paolo Drago, Ernesto Setti, Lisa Licitra, Diego Liberati

Research output: Contribution to journalArticle

Abstract

The integration of chemotherapy and radiotherapy for the treatment of advanced head and neck cancer is still a matter of clinical investigation. An important limitation is that the concomitant administration of chemotherapy and radiotherapy still induces severe toxicity. In this paper, a simple artificial neural network is used to predict, on the basis of biological and clinical data, if the cumulative toxicity of the combined chemo-radiation treatment itself would be tolerated. The resulting method, tested on clinical data from a phase II trial, proved to be able to forecast which patients will tolerate a combined chemo-radiotherapeutic approach. This result should open a new perspective in the clinical approach, by supplying a potential predictive indicator for toxicity.

Original languageEnglish
Pages (from-to)782-787
Number of pages6
JournalIEEE Transactions on Biomedical Engineering
Volume49
Issue number8
DOIs
Publication statusPublished - 2002

Keywords

  • Chemo-radiation
  • Head and neck cancer
  • Interval arithmetic
  • Learning
  • Neural networks
  • Perceptrons
  • Performance status
  • Predictive factors
  • Toxicity

ASJC Scopus subject areas

  • Biomedical Engineering

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