An experimentally calibrated nanodosimetry-based framework for predicting LET-dependent relative biological...

Proton boron capture therapy (PBCT) studies focus on physical dose enhancement or biological observations, while quantitative prediction of biological effectiveness remains limited. This study aimed to establish a framework combining Monte Carlo (MC) simulation, nanodosimetry, and radiobiological experiments to evaluate the relative biological effectiveness (RBE) of PBCT.

Geant4-based MC simulations and a pre-calculated nanodosimetric database were used to determine mixed-field nanodosimetric parameters, including M1, M2, and F2. Clonogenic survival experiments were performed using a clinical 70 MeV proton beam and a 6 MV photon beam. U-87 MG cells were irradiated with photons, protons, and PBCT at 0, 2, 4, 6, and 8 Gy using boronophenylalanine (BPA, 21.0 ppm 11B). Survival data were fitted with the linear-quadratic model, and the resulting α and β values were used to optimize the Logistic Nanodosimetry Model (LNDM) for depth-dependent RBE prediction.

The LNDM, combined with MC-derived nanodosimetric parameters and experimental survival data, reproduced the measured radiobiological response with differences in α and β below 0.5% at the experimental cell position, corresponding to a water-equivalent depth (WED) of 3.9 cm and located on the rising edge of the Bragg peak. PBCT consistently exhibited higher α and RBE values than proton irradiation across the investigated LET range. At 3.9 cm, PBCT increased RBE by 8.7% compared with protons. For the 70 MeV beam, PBCT yielded α = 0.116 Gy⁻¹, β = 0.023 Gy⁻², and RBE = 1.25, compared with α = 0.076 Gy⁻¹, β = 0.023 Gy⁻², and RBE = 1.15 for proton therapy.

An experimentally calibrated framework integrating MC simulation, nanodosimetry, radiobiological experiments, and the LNDM was established for LET-dependent PBCT RBE prediction based on biological calibration data. PBCT showed consistently higher RBE than proton therapy, with a greater advantage at higher LET, supporting future biological optimization of PBCT.