Beyond Dose in Boron Neutron Capture Therapy: Cellular ${}^{10}$B-Capture Statistics and Microdosimetric Context in Effect Prediction

Boron neutron capture therapy (BNCT) produces high-linear-energy-transfer, short-range particles through the ${}^{10}$B(n,$\alpha$)${}^7$Li reaction. Conventional descriptors, including absorbed dose, compound biological effectiveness (CBE)-weighted dose, and photon-isoeffective dose ($D_{\mathrm{i}\mathrm{s}\mathrm{o}\mathrm{E}}$), 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 ${}^{10}$B-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 $D_{\mathrm{i}\mathrm{s}\mathrm{o}\mathrm{E}}$, 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, $D_{\mathrm{i}\mathrm{s}\mathrm{o}\mathrm{E}}$, SMK, or PHITS.