Previous studies on personalized radiotherapy (RT) have mostly focused on baseline patient stratification, adapting the treatment plan according to mid-treatment anatomical changes, or dose boosting to selected tumor subregions using mid-treatment radiological findings. However, the question of how to find the optimal adapted plan has not been properly tackled. Moreover, the effect of information uncertainty on the resulting adaptation has not been explored.

In this paper, we present a framework to optimally adapt radiation therapy treatments to early radiation treatment response estimates derived from pre- and mid-treatment imaging data while considering the information uncertainty. The framework is based on the optimal stopping in radiation therapy (OSRT) framework. Biological response is quantified using tumor control probability (TCP) and normal tissue complication probability (NTCP) models, and these are directly optimized for in the adaptation step. Two adaptation strategies are discussed: (1) uniform dose adaptation and (2) continuous dose adaptation. In the first strategy, the original fluence-map is simply scaled upwards or downwards, depending on whether dose escalation or de-escalation is deemed appropriate based on the mid-treatment response observed from the radiological images. In the second strategy, a full NTCP-TCP-based fluence map re-optimization is performed to achieve the optimal adapted plans.

We retrospectively tested the performance of these strategies on 14 canine head and neck cases treated with tomotherapy, using as response biomarker the change in the 3’-deoxy-3’[(18)F]-fluorothymidine (FLT)-PET signals between the pre- and mid-treatment images, and accounting for information uncertainty. Using a 10% uncertainty level, the two adaptation strategies both yield a noteworthy average improvement in guaranteed (worst-case) TCP.

先前关于个性化放射治疗 (RT) 的研究主要集中在基线患者分层、根据治疗中期解剖变化调整治疗计划，或使用治疗中期放射学发现对选定的肿瘤亚区域进行剂量增加。然而，如何找到最优适应计划的问题并没有得到妥善解决。此外，尚未探讨信息不确定性对由此产生的适应的影响。

在本文中，我们提出了一个框架，以在考虑信息不确定性的同时，使放射治疗以最佳方式适应从治疗前和治疗中成像数据得出的早期放射治疗反应估计。该框架基于最佳停止放射治疗 (OSRT) 框架。使用肿瘤控制概率 (TCP) 和正常组织并发症概率 (NTCP) 模型量化生物反应，并在适应步骤中直接优化这些模型。讨论了两种适应策略：（1）均匀剂量适应和（2）连续剂量适应。在第一种策略中，根据从放射图像观察到的治疗中期反应，根据剂量递增或递减是否合适，将原始通量图简单地向上或向下缩放。

我们回顾性地测试了这些策略在 14 个接受断层疗法治疗的犬头颈部病例中的表现，使用反应生物标志物将 3'-脱氧-3'[(18)F]-氟胸苷 (FLT)-PET 信号的变化作为反应生物标志物。治疗前和治疗中的图像，并考虑信息的不确定性。使用 10% 的不确定性水平，两种适应策略都在保证（最坏情况）TCP 中产生了显着的平均改进。