标题： 用于辐射诱导DNA损伤和细胞存活机制预测的纳米和微米尺度能量沉积模型整合 显示英文标题

标题： Integrating nano- and micrometer-scale energy deposition models for mechanistic prediction of radiation-induced DNA damage and cell survival

摘要： 我们提出了一种综合建模框架，将广义随机微剂量模型（GSM2）与MINAS-TIRITH相结合，其中GSM2用于预测细胞存活分数，而MINAS-TIRITH是一种快速高效的基于Geant4 DNA的工具，用于模拟细胞群体中辐射引起的DNA损伤。 这种方法能够生成具有空间和结构分辨率的双链断裂（DSB）分布，捕捉到诸如损伤复杂性和染色体特异性等关键特征。 引入了一种DBSCAN聚类算法的新应用，以在微米尺度上对DSB进行分组。 这使得能够识别DNA损伤的物理聚集物及其与亚核域的关联，从而直接联系到由\gsm 预测的细胞存活概率。 该模型使用了经220 kV X射线照射的HUVEC细胞以及在广泛线性能量传递（LET）范围内（从约4 keV/{\mu }m到超过20 keV/{\mu }m）暴露于质子的H460细胞的实验数据进行了验证。 结果表明，模拟结果与实验存活概率之间有很好的一致性，使该模型成为首批能够连接纳米剂量学和微剂量学辐射表示与生物结果（如细胞存活）的多尺度模型之一。 通过结合辐射-物质相互作用的固有随机性，该框架有效地将辐射场的物理特性与细胞水平的生物反应联系起来。 其在各种辐射类型和能量下的准确性支持其在生物优化放疗中的潜在应用。 显示英文摘要

摘要： We present an integrated modeling framework that combines the Generalized Stochastic Microdosimetric Model (GSM2), used to predict cell survival fractions, with MINAS-TIRITH, a fast and efficient Geant4 DNA-based tool for simulating radiation-induced DNA damage in cell populations. This approach enables the generation of spatially and structurally resolved double-strand break (DSB) distributions, capturing key features such as damage complexity and chromosome specificity. A novel application of the DBSCAN clustering algorithm is introduced to group DSBs at the micrometer scale. This allows the identification of physical aggregates of DNA damage and their association with subnuclear domains, providing a direct link to the cell survival probability as predicted by \gsm. The model was validated using experimental data from HUVEC cells irradiated with 220 kV X-rays and H460 cells exposed to protons over a wide linear energy transfer (LET) range, from approximately 4 keV/{\mu}m to over 20 keV/{\mu}m. Results show excellent agreement between simulations and experimental survival probabilities, making this one of the first consistent multi-scale models to bridge nanodosimetric and microdosimetric representations of radiation with biological outcomes such as cell survival. By incorporating the inherent stochastic nature of radiation-matter interactions, this framework effectively connects the physical properties of the radiation field to the biological response at the cellular level. Its accuracy across various radiation types and energies supports its potential for use in biologically optimized radiotherapy.