Radiation Transport and Dosimetric Methods for Preclinical CIRT Experiments

Abstract

Pediatric high-grade gliomas (pHGGs) pose significant treatment challenges due to their aggressive nature and poor prognosis, with a 5-year survival rate below 20% despite multimodal treatments including surgery, radiation, and chemotherapy [Das and Kumar, 2017]. Current strategies often require clinical trade-offs between insufficient tumor control and high risk of neurological toxicity [Liu et al., 2018]. Carbon-ion radiation therapy (CIRT) offers a promising alternative, delivering high linear energy transfer (LET), enhanced relative biological effect (RBE), precise dose conformity, and reduced normal tissue exposure [Eichkorn et al., 2021]. Despite success abroad, adoption of CIRT in the United States remains limited by gaps in parametric modeling, LET/RBE validation in vivo, and predictive normal tissue frameworks for normal tissue response [Helm and Fournier, 2023]. The parent project aims to evaluate CIRT’s anti-tumor efficacy, normal brain toxicity, cognitive effects, and secondary cancer risks using pediatric-relevant preclinical models. This thesis focuses on modeling and validating carbon-ion radiation transport in preclinical settings using TOPAS and TRiP98. These two simulation platforms provide complementary strengths in radiation transport modeling: TOPAS through stochastic Monte Carlo methods and TRiP98 through fast, deterministic transport calculations based on precomputed interaction tables. By simulating experimental exposures performed at Brookhaven National Laboratory (BNL), this work generates high-resolution dose and LET maps in water and juvenile mouse CT geometries and benchmarks simulation accuracy against ion chamber and film measurements. The resulting validation provides a foundation for in vivo CIRT experiments and a computational workflow to support future experimental applications of CIRT in pediatric glioblastoma treatment.