Copenhagen, Denmark
Onsite/Online

ESTRO 2022

Session Item

Tuesday
May 10
11:00 - 12:15
Room D5
Modelling at the voxel level: Dose and image-data mining
Abigail Bryce-Atkinson, United Kingdom;
Wouter van Elmpt, The Netherlands
4190
Symposium
Physics
11:18 - 11:36
Voxel-based RBE studies in proton therapy
Armin Lühr, Germany
SP-1016

Abstract

Voxel-based RBE studies in proton therapy
Authors:

Armin Lühr1

1TU Dortmund University, Department of Physics, Dortmund, Germany

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Abstract Text

Protons are considered to be more biologically effective than photons. The proton relative biological effectiveness (RBE) is accounted for by using a fixed value of 1.1 in treatment planning and delivery. However, the RBE of protons is not constant; it varies depending on a combination of dose, endpoint, tissue α/β, and the linear energy transfer (LET) as has been demonstrated in numerous preclinical experiments. Accordingly, the remaining central question is how much the proton RBE variability impacts proton therapy treatment and, in particular, outcome. Therefore, clinical data are urgently needed that help to quantify the variable biological effect and thereby to reduce the uncertainty currently associated with the RBE.
In recent years, a couple of retrospective studies considered post treatment image changes as a surrogate for biological effect. These image changes, e.g. contrast enhancements (CE) on post-treatment magnetic resonance (MR) images, were correlated on the voxel level with proton dose as well as LET and used to perform radiation effect modelling. Several studies demonstrated a significant effect of dose and LET, while in some studies the effect was found to be non-significant.
In this contribution, first, the motivation why to use voxel-based analysis in proton therapy is discussed. Afterwards, several aspects of voxel-based RBE studies in proton therapy are considered, including the relevance of studied endpoints, requirements for the used imaging and irradiation data, possible analysis strategies and inherent technical and conceptual challenges.