A Bayesian Approach for Prediction of Patient Radiosensitivity

Alan Herschtal, Roger F. Martin, Trevor Leong, Pavel Lobachevsky, Olga A. Martin

Research output: Contribution to journalArticleResearchpeer-review

Abstract

Purpose: A priori identification of the small proportion of radiation therapy patients who prove to be severely radiosensitive is a long-held goal in radiation oncology. A number of published studies indicate that analysis of the DNA damage response after ex vivo irradiation of peripheral blood lymphocytes, using the γ-H2AX assay to detect DNA damage, provides a basis for a functional assay for identification of the small proportion of severely radiosensitive cancer patients undergoing radiotherapy. Methods and Materials: We introduce a new, more rigorous, integrated approach to analysis of radiation-induced γ-H2AX response, using Bayesian statistics. Results: This approach shows excellent discrimination between radiosensitive and non-radiosensitive patient groups described in a previously reported data set. Conclusions: Bayesian statistical analysis provides a more appropriate and reliable methodology for future prospective studies.

Original languageEnglish
Pages (from-to)627-634
Number of pages8
JournalInternational Journal of Radiation Oncology Biology Physics
Volume102
Issue number3
DOIs
Publication statusPublished - 1 Nov 2018
Externally publishedYes

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

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