Proposing a Clinical Model for RBE Based on Proton Track-End Counts.

Area of Science:

• Medical Physics
• Radiation Oncology
• Radiobiology

Background:

• Clinical application of linear energy transfer (LET) optimization in proton therapy is challenging due to difficulties in defining and calculating LET and its relationship with relative biological effectiveness (RBE).
• Variability in in vitro experimental data complicates LET-based RBE calculations.
• Proton track end counts are being explored as an alternative metric for biological optimization due to potentially favorable properties.

Purpose of the Study:

• To propose and evaluate a novel model for clinical calculations of RBE in proton therapy based on proton track end counts.
• To develop an effective dose concept for translating proton track-end counts to RBE values.
• To compare the performance of the track end model with existing dose-averaged LET (LETd)-based RBE models.

Main Methods:

• Developed an effective dose concept to link proton track end counts per unit mass in a voxel to proton RBE.
• Utilized Monte Carlo simulations to model dose, track end, and LETd distributions in water phantoms, in vitro studies, and patient treatment plans.
• Evaluated the correlation between track ends and regions of elevated biological effectiveness against LETd-based RBE models.

Main Results:

• Proton track ends demonstrated a correlation with biological effects in in vitro experiments, achieving accuracy comparable to LETd.
• Patient treatment plan simulations using the track end model identified biological hotspots consistent with those predicted by LETd-based RBE models.
• The proposed track end model showed comparable information content to LETd-based models in clinical scenarios.

Conclusions:

• An RBE model based on proton track end counts may serve as a viable alternative for clinical optimization and evaluation in proton therapy.
• The track end model offers potentially superior statistical properties compared to LETd-based models.
• This approach could enhance the biological optimization of proton therapy treatments.