Combined photon-proton modeling of radiation-induced brain imaging changes supports variability in proton relative biological effectiveness and increased periventricular radiosensitivity
Martina Palkowitsch, Larissa S. Kilian, Fabian Hennings, Armin L\"uhr, Justus Thiem, Arne Grey, Rebecca B\"utof, Annekatrin Seidlitz, Esther G.C. Troost, Mechthild Krause, Steffen L\"ock

TL;DR
This study models radiation-induced brain changes in patients treated with photons or protons, revealing variable proton RBE and increased radiosensitivity in the periventricular region, which can improve risk assessment.
Contribution
It introduces a spatially resolved predictive modeling approach to assess proton RBE variability and PVR radiosensitivity without relying on predefined dose-response assumptions.
Findings
Proton RBE varies with LETd, modeled as RBE=1+m·LETd, with m=0.10 μm/keV.
Increased LETd and PVR are independent predictors of RICE occurrence.
Using EUD based on variable RBE improves prediction of radiation-induced brain changes.
Abstract
Purpose: Recent investigations of radiation-induced contrast enhancements (RICE) in brain tumor patients after proton therapy indicated variability in proton relative biological effectiveness (RBE) and increased radiosensitivity of the periventricular region (PVR). Prior studies, however, were restricted to proton cohorts requiring assumptions on reference radiation. This study assessed proton RBE variability and PVR radiosensitivity using spatially resolved predictive modeling of RICE in a combined photon-proton cohort. Methods and Materials: Predictive models for RICE detected on follow-up magnetic resonance imaging were developed in 152 brain tumor patients treated with photons or protons. Logistic regression was applied at the voxel level to model spatial occurrence and at the patient level to model incidence. A clinical RBE model was derived from voxel-wise comparisons of estimated…
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