发表机构
Particle Beam Therapy Research Institute, Inc.; Kagawa University Faculty of Medicine（粒子束治疗研究所有限公司; 香川大学医学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出分层框架，整合多类BNCT相关研究，以细胞¹⁰B俘获统计为桥梁，实现考虑不确定性的空间分辨BNCT效应预测，不取代现有方法。
AI 中文摘要
硼中子俘获疗法（BNCT）通过¹⁰B(n,α)⁷Li反应产生高线性能量转移、短射程粒子。传统描述符包括吸收剂量、复合生物效应（CBE）加权剂量和光子等效剂量（D_isoE），仍具临床实用性，但将细胞异质性、区室化硼定位及随机反应发生压缩为宏观总结。我们提出分层框架，将细胞相关的¹⁰B俘获反应统计置于微剂量学效应建模上游，连接临床观察与BNCT响应的细胞尺度决定因素。我们整合BNCT微剂量学、CBE与D_isoE、随机微剂量动力学建模（SMK）、基于PHITS的细胞模拟、硼成像、组织微结构及硼剂表征的已发表研究，将这些元素重组为观测、潜在细胞、反应计数和生物效应层。成像、病理、剂特异性数据及中子场信息约束潜在变量，如细胞硼负荷、区室定位与几何；这些变量决定预期反应负荷与实际反应计数分布，随后通过微剂量学模型转化为细胞存活、肿瘤控制或正常组织损伤。因此，细胞相关的反应计数统计为宏观测量与已建立的BNCT效应模型提供可解释的推断桥梁，支持空间分辨且考虑不确定性的效应预测，无需取代基于CBE、D_isoE、SMK或PHITS的现有方法。
英文摘要
Boron neutron capture therapy (BNCT) produces high-linear-energy-transfer, short-range particles through the $^{10}$B(n,$α$)$^{7}$Li reaction. Conventional descriptors, including absorbed dose, compound biological effectiveness (CBE)-weighted dose, and photon-isoeffective dose ($D_{\mathrm{isoE}}$), 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 $^{10}$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 $D_{\mathrm{isoE}}$, 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, $D_{\mathrm{isoE}}$, SMK, or PHITS.
Comments43 pages, 2 figures, 1 table, 1 box. Review article. Submitted to Physica Medica
