A novel method for the evaluation of uncertainty in dose volume histogram computation

TL;DR

This paper introduces a probabilistic method to evaluate uncertainty in dose volume histograms used in radiotherapy planning, accounting for various sources of computational and measurement errors.

Contribution

A new dose-expected volume histogram approach is proposed, providing a quantitative measure of uncertainty effects in dose distribution assessments.

Findings

Uncertainty impacts PTV coverage significantly.

Dose to organs at risk is less affected by uncertainty.

Method applied successfully to multiple patient cases.

Abstract

Dose volume histograms are a useful tool in state-of-the-art radiotherapy planning, and it is essential to be aware of their limitations. Dose distributions computed by treatment planning systems are affected by several sources of uncertainty such as algorithm limitations, measurement uncertainty in the data used to model the beam and residual differences between measured and computed dose, once the model is optimized. In order to take into account the effect of uncertainty, a probabilistic approach is proposed and a new kind of histogram, a dose-expected volume histogram, is introduced. The expected value of the volume in the region of interest receiving an absorbed dose equal or greater than a certain value is found using the probability distribution of the dose at each point. A rectangular probability distribution is assumed for this point dose, and a relationship is given for practical computations. This method is applied to a set of dose volume histograms for different regions of interest for 6 brain patients, 8 lung patients, 8 pelvis patients and 6 prostate patients planned for IMRT. These results show how dose computation uncertainty has effects on PTV coverage and, to a lesser extent, in dose to organs at risk. This method allows to quantify these effects.