The relationship between the physical characteristics of the radiation field and biological damage is central to both radiotherapy and radioprotection, yet the link between spatial scales of energy deposition and biological effects remains not entirely understood. To address this, we developed an interpretable deep learning model that predicts cell survival after proton and carbon ion irradiation, leveraging sequential attention to highlight relevant features and provide insight into the contribution of different energy deposition scales. Trained and tested on the PIDE dataset, our model incorporates, beside LET, nanodosimetric and microdosimetric quantities simulated with MC-Startrack and Open-TOPAS, enabling multi-scale characterization. While achieving high predictive accuracy, our approach also emphasizes transparency in decision-making. We demonstrate high accuracy in predicting RBE for in vitro experiments. Multiple scales are utilized concurrently, with no single spatial scale being predominant. Quantities defined at smaller spatial domains generally have a greater influence, whereas the LET plays a lesser role.

翻译：辐射场物理特性与生物损伤之间的关系是放射治疗和辐射防护的核心问题，然而能量沉积的空间尺度与生物效应之间的联系仍未完全阐明。为此，我们开发了一种可解释的深度学习模型，用于预测质子和碳离子辐照后的细胞存活情况。该模型利用序列注意力机制突出相关特征，并揭示不同能量沉积尺度的贡献。基于PIDE数据集进行训练和测试，我们的模型除了包含线性能量传递（LET）外，还整合了通过MC-Startrack和Open-TOPAS模拟的纳米剂量学和微剂量学量，实现了多尺度表征。在获得高预测精度的同时，我们的方法还强调了决策过程的透明度。实验证明，该模型在预测体外实验的相对生物效能（RBE）方面具有高精度。多个空间尺度被同时利用，且没有单一尺度占据主导地位。在较小空间域定义的量通常具有更大影响力，而LET所起的作用相对较小。