Related Experiment Video
Updated: Feb 4, 2026

Author Spotlight: Radiotherapy and Clonogenic Assays for Advancing Cancer Research and Personalized Medicine
Published on: April 5, 2024
A Bayesian Approach for Prediction of Patient Radiosensitivity
Alan Herschtal1, Roger F Martin2, Trevor Leong3
1Centre for Biostatistics and Clinical Trials, Peter MacCallum Cancer Centre, Melbourne, Australia.
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.
Related Concept Videos
Predicting Molecular Geometry
Prediction Intervals
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
End Point Prediction: Gran Plot
For potentiometric titration, the Gran plot is created by plotting...
Sensitivity, Specificity, and Predicted Value
Sensitivity is the...
Predicting Reaction Outcomes
Predicting Products: Substitution vs. Elimination
The following factors can influence the mechanisms competing against each other:

