An ion treatment planning framework for inclusion of nanodosimetric ionization detail through cluster dose

TL;DR

This paper presents a novel ion treatment planning framework that incorporates nanodosimetric ionization detail through cluster dose, enabling physics-based optimization of biological effects in charged-particle radiotherapy.

Contribution

The authors developed and validated a cluster dose optimization framework integrated into treatment planning, bridging nanodosimetry and clinical ion therapy planning.

Findings

Achieved >97% gamma passing rates in phantom validation

Demonstrated homogeneous target coverage with cluster dose optimization

Validated framework's accuracy against Monte Carlo simulations

Abstract

Nanodosimetry relates Ionization Detail (ID) and ionization parameters (Ip) to biological endpoints relevant for charged-particle radiotherapy. This supports a more physics-based modeling of biological effectiveness than traditional dose-response relationships and RBE models. Faddegon et al. (2023) introduced cluster dose g(Ip) as a physical quantity, bridging ID to the treatment planning level, which can be directly optimized. We developed a framework enabling cluster dose optimization via a pencil-beam (PB) algorithm, and validated against Monte Carlo (MC) simulations. The framework, integrated into the treatment planning toolkit matRad, uses precomputed Ip values obtained from MC track-structure simulations. We applied our tool for plan optimization with protons, helium, and carbon ions in a water phantom and a prostate case. Recalculation with TOPAS showed 3D gamma passing rates >97% (phantom) and >98% (patient) using a 3%/3mm criterion and a threshold of 10% of the maximum dose. Cluster dose F5 optimization produced homogeneous target coverage, with heavier ions requiring lower absorbed doses for the same prescribed cluster dose level. This demonstrates the feasibility of fast, accurate cluster dose optimization using PB algorithms.