A Bayesian Approach for Prediction of Patient Radiosensitivity.

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.

• Authors: Alan Herschtal, Roger F Martin, Trevor Leong, Pavel Lobachevsky, Olga A Martin
• Journal: International journal of radiation oncology, biology, physics (Int J Radiat Oncol Biol Phys) Vol. 102 Issue 3 Pg. 627-634 (11 01 2018) ISSN: 1879-355X [Electronic] United States
• PMID: 30244880 (Publication Type: Journal Article, Research Support, Non-U.S. Gov't)
• Copyright: Copyright © 2018 Elsevier Inc. All rights reserved.

Chemical References

• Biomarkers
• H2AX protein, human
• Histones

Topics

• Bayes Theorem
• Biomarkers
• DNA Damage
• Databases, Factual
• Dose-Response Relationship, Radiation
• Female
• Histones (chemistry)
• Humans
• Lymphocytes (radiation effects)
• Male
• Models, Theoretical
• Monte Carlo Method
• Neoplasms (blood, radiotherapy)
• Prospective Studies
• Radiation Tolerance (genetics)
• Radiotherapy
• Reproducibility of Results
• Retrospective Studies
• Time Factors