Dosimetric study of proton boron capture therapy based on Monte Carlo simulation and machine learning algorithms

Proton boron capture therapy offers a promising enhancement to conventional

proton therapy by leveraging the 11B(p,a)3a nuclear reaction for localized

dose amplification. This study systematically investigates the Bragg curve

characteristics of proton beams using the Geant4 Monte Carlo toolkit, with a

particular focus on the dosimetric impact of ??B. The introduction of a

pure ??B target induced a consistent forward shift in the Bragg peak

position, attributed to increased stopping power and nuclear reactions.

Furthermore, Proton boron capture therapy demonstrated enhanced local energy

deposition along the central axis due to the generation of high linear

energy transfer alpha particles, with minimal lateral broadening. To

facilitate precise treatment planning, Bragg curve data for 800 distinct

proton energies (0-80 MeV) in water were generated. Various machine learning

algorithms were subsequently employed to develop predictive models for the

Bragg peak position. Comparative analysis identified gaussian process

regression as the optimal model, achieving an R? of 0.999997 and a root mean

squared error of approximately 0.0273 mm for predicting Bragg peak positions

in water. Crucially, this research pioneers a novel pathway for proton boron

capture therapy treatment planning by combining high-accuracy machine

learning-based prediction of the initial Bragg peak (in water) with a

characterized correction for the ??B-induced forward shift, enabling more

precise determination of the actual treatment depth. This work provides

critical dosimetric characterization, quantifies key ??B-induced phenomena,

and offers a validated predictive framework, thereby establishing a

theoretical foundation and technical support for dose optimization in this

emerging therapeutic modality.