Data-driven ion-independent relative biological effectiveness (RBE) modelling using the beam quality Q.

The beam quality Q=Z^2/E (Z = ion charge, E = energy), an alternative quantity to the conventionally used linear energy transfer (LET), enables ion-independent modelling of relative biological effectiveness (RBE) of ions. Therefore, the Q concept, i.e., different ions with similar Q have similar RBE values, could help to transfer clinical RBE knowledge from better studied ion types (e.g., carbon) to other ions. However, the validity of the Q concept, has so far only been demonstrated for low LET values. In this work, the Q concept was explored in a broad LET range including the so-called overkilling region. 
The particle irradiation data ensemble (PIDE) was used as experimental in-vitro dataset. Data driven models, i.e., neural network (NN) models with low complexity, were built to predict RBE values for H, He, C and Ne ions at different in-vitro endpoints taking different combinations of clinically available candidate inputs: LET, Q and linear-quadratic photon parameter αx/βx. Models were compared in terms of prediction power and ion dependence. The optimal model was compared to a published model data using the local effect model (LEM IV). 
The NN models performed best for the prediction of RBE at reference photon doses between 2-4 Gy, using only αx/βx and Q instead of LET as input. The Q model was not significantly ion-dependent (p > 0.5) and its prediction power was comparable to that of LEM IV.
In conclusion, the validity of the Q concept was demonstrated in a clinically relevant LET range including overkilling. A data-driven Q model was proposed and observed to have an RBE prediction power comparable to a mechanistic model regardless of particle type. The Q concept provides the possibility to reduce RBE uncertainty in treatment planning for protons and ions in the future by transferring clinical RBE knowledge between ions. &#xD.