用可解释深度学习预测质子碳离子照射后细胞存活率
One scale to rule them all: interpretable multi-scale Deep Learning for predicting cell survival after proton and carbon ion irradiation
- 融合多尺度能量沉积特征，通过注意力机制识别关键信息
- 在PIDE数据集上预测准确率高，小尺度量占主导作用
- 适合放射治疗和辐射防护领域的研究人员参考
辐射场的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，但能量沉积的空间尺度与生物效应之间的联系仍不完全清楚。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照射后的细胞存活率，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。模型基于PIDE数据集训练与测试，融合了LET、纳米剂量学和微剂量学参数（由MC-Startrack和Open-TOPAS模拟），实现多尺度表征。模型在预测体外实验的相对生物效应（RBE）方面表现出高精度，多个空间尺度被同时使用，无单一尺度占主导地位；通常，较小空间域定义的量影响更大，而LET的作用相对较小。
原文摘要 · Abstract (English)
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.
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