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

Nanodosimetry relates the cumulative or statistical moments of Ionization Detail (ID) with biological endpoints of relevance to cancer radiotherapy using charged particles. This association suggests to develop an additional physics-detailed layer of modeling that may complement biological modeling and treatment planning. The recently introduced cluster dose $g^{\left(I_p\right)}$ may serve as a purely physical quantity bridging the Ionization Parameter ( $I_p$ ) to the macroscopic treatment planning scale.

In this work, we developed a framework to enable flexible and direct cluster dose optimization using a pencil-beam algorithm, which we validated with condensed history Monte Carlo (MC) simulations.

Cluster dose combines the contributions to $I_p$ from all particles within a macroscopic volume. Our framework, implemented in the open source planning toolkit matRad, utilizes the particle and energy-dependent $I_p$ values from an ID database precomputed from MC track strucure (MCTS) simulations. First, we create pencil-beam (PB) kernels, including fluence spectra, from condensed history MC simulations. For a water box phantom and a representative prostate patient, we create treatment plans optimized on dose and cluster dose $F_5$ coverage and homogeneity for protons, helium and carbon ions. Plans were validated with Geant4/TOPAS MC.

Our framework provided accurate, practical cluster dose calculation and planning. PB algorithms achieve typical accuracy for cluster dose calculation comparable to dose calculation. Recalculation with TOPAS on the box phantom yielded 3D gamma passing rates (GPRs) greater than $97\%$ . For the prostate patient, GPRs exceeded $98\%$ . Both used the $3\%/3mm$ criterion with a threshold of $10\%$ of the maximum dose. Using cluster dose $F_5$ optimization, homogeneous cluster dose target coverage was achieved in all plans. A constant cluster dose prescription across all ion species shows the expected decrease in required absorbed dose for heavier ions.

We demonstrate that fast, direct cluster dose calculation and optimization is feasible using MC validated planning with PB algorithms. Cluster dose prescription and optimization results in the expected cluster dose coverage and physical dose levels depending on the respective primary ion.