Beyond Dose in Boron Neutron Capture Therapy: Cellular 10B-Capture Statistics and Microdosimetric Context in Effect Prediction
Abstract
Boron neutron capture therapy (BNCT) produces high-linear-energy-transfer, short-range particles through the 10B(n,α)7Li reaction. Conventional descriptors, including absorbed dose, compound biological effectiveness (CBE)-weighted dose, and photon-isoeffective dose (DisoE), remain clinically useful but compress cellular heterogeneity, compartmental boron localization, and stochastic reaction occurrence into macroscopic summaries. We propose a hierarchical framework that places cell-associated 10B-capture reaction statistics upstream of microdosimetric effect modeling and links clinical observations to cellular-scale determinants of BNCT response. We synthesize published work on BNCT microdosimetry, CBE and DisoE, stochastic microdosimetric kinetic modeling, PHITS-based cellular simulations, boron imaging, tissue microstructure, and boron-agent characterization, reorganizing these elements into observational, latent-cellular, reaction-count, and biological-effect layers. Imaging, pathology, agent-specific data, and neutron-field information constrain latent variables such as cellular boron burden, compartmental localization, and geometry; these variables determine expected reaction burdens and distributions of realized reaction counts, which are then translated into cell survival, tumor control, or normal-tissue injury by microdosimetric models. Cell-associated reaction-count statistics thus provide an interpretable inferential bridge between macroscopic measurements and established BNCT effect models, supporting spatially resolved and uncertainty-aware effect prediction without replacing existing approaches based on CBE, DisoE, SMK, or PHITS.
Related
- A No-Go Framework for Coarse-Grained Effect Prediction in Boron Neutron Capture Therapy: Structural Identifiability Limits and Microscopic Coverage
- Fast Monte Carlo calculation of multiple scattering in resonant neutron capture
- Selective Operando Visualization of Ultrathin Lithium in Li-Metal Batteries by Neutron Capture Imaging
- Neutron Capture Information in Improving IBD Angular Resolution
- Stochastic dose fluctuations in boron neutron capture therapy with heterogeneous boron distributions
BibTeX
@misc{furuya2026beyond,
title = {{Beyond Dose in Boron Neutron Capture Therapy: Cellular $^10$B-Capture Statistics and Microdosimetric Context in Effect Prediction}},
author = {Shuichi Furuya and Atsushi Fujimura},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.03130},
eprint = {2609.03130},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.03130},
}
RIS
TY - UNPB
TI - Beyond Dose in Boron Neutron Capture Therapy: Cellular ^10B-Capture Statistics and Microdosimetric Context in Effect Prediction
AU - Shuichi Furuya
AU - Atsushi Fujimura
PY - 2026
DA - 2026/09/02
PB - arXiv
DO - 10.48550/arxiv.2609.03130
UR - https://arxiv.org/abs/2609.03130
AB - Boron neutron capture therapy (BNCT) produces high-linear-energy-transfer, short-range particles through the ^10B(n,α)^7Li reaction. Conventional descriptors, including absorbed dose, compound biological effectiveness (CBE)-weighted dose, and photon-isoeffective dose (D_isoE), remain clinically useful but compress cellular heterogeneity, compartmental boron localization, and stochastic reaction occurrence into macroscopic summaries. We propose a hierarchical framework that places cell-associated ^10B-capture reaction statistics upstream of microdosimetric effect modeling and links clinical observations to cellular-scale determinants of BNCT response. We synthesize published work on BNCT microdosimetry, CBE and D_isoE, stochastic microdosimetric kinetic modeling, PHITS-based cellular simulations, boron imaging, tissue microstructure, and boron-agent characterization, reorganizing these elements into observational, latent-cellular, reaction-count, and biological-effect layers. Imaging, pathology, agent-specific data, and neutron-field information constrain latent variables such as cellular boron burden, compartmental localization, and geometry; these variables determine expected reaction burdens and distributions of realized reaction counts, which are then translated into cell survival, tumor control, or normal-tissue injury by microdosimetric models. Cell-associated reaction-count statistics thus provide an interpretable inferential bridge between macroscopic measurements and established BNCT effect models, supporting spatially resolved and uncertainty-aware effect prediction without replacing existing approaches based on CBE, D_isoE, SMK, or PHITS.
ER -