Radiation therapy (RT) is prescribed for curative and palliative treatment for around 50% of patients with solid tumors. Radiation‐induced toxicities of healthy organs accompany many RTs and represent one of the main limiting factors during dose delivery. The existing RT planning solutions generally discard spatial dose distribution information and lose the ability to recognize radiosensitive regions of healthy organs potentially linked to toxicity manifestation. This study proposes a universal deep learning‐based algorithm for recognitions of consistent dose patterns and generation of toxicity risk maps for the abdominal area.

中文翻译：

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深度学习用于确定肝脏立体定向放射治疗后与毒性相关的关键区域。

约有50％的实体瘤患者被指定进行放射治疗（RT）的治疗和姑息治疗。辐射对健康器官的毒性伴随许多RT，是剂量给药期间的主要限制因素之一。现有的RT计划解决方案通常会丢弃空间剂量分布信息，并失去识别可能与毒性表现相关的健康器官放射敏感区域的能力。这项研究提出了一种基于通用深度学习的算法，用于识别一致的剂量模式并生成腹部区域的毒性风险图。