van Leeuwen C M, Crezee J, Oei A L, Franken N A P, Stalpers L J A, Bel A, Kok H P
a Department of Radiation Oncology , Academic Medical Center, University of Amsterdam , Amsterdam , The Netherlands.
b Laboratory for Experimental Oncology and Radiobiology (LEXOR)/Center for Experimental Molecular Medicine , Academic Medical Center, University of Amsterdam , Amsterdam , The Netherlands.
Int J Hyperthermia. 2017 Mar;33(2):160-169. doi: 10.1080/02656736.2016.1241431. Epub 2016 Nov 1.
Currently, clinical decisions regarding thermoradiotherapy treatments are based on clinical experience. Quantification of the radiosensitising effect of hyperthermia allows comparison of different treatment strategies, and can support clinical decision-making regarding the optimal treatment. The software presented here enables biological evaluation of thermoradiotherapy plans through calculation of equivalent 3D dose distributions.
Our in-house developed software (X-Term) uses an extended version of the linear-quadratic model to calculate equivalent radiation dose, i.e. the radiation dose yielding the same effect as the thermoradiotherapy treatment. Separate sets of model parameters can be assigned to each delineated structure, allowing tissue specific modelling of hyperthermic radiosensitisation. After calculation, the equivalent radiation dose can be evaluated according to conventional radiotherapy planning criteria. The procedure is illustrated using two realistic examples. First, for a previously irradiated patient, normal tissue dose for a radiotherapy and thermoradiotherapy plan (with equal predicted tumour control) is compared. Second, tumour control probability (TCP) is assessed for two (otherwise identical) thermoradiotherapy schedules with different time intervals between radiotherapy and hyperthermia.
The examples demonstrate that our software can be used for individualised treatment decisions (first example) and treatment optimisation (second example) in thermoradiotherapy. In the first example, clinically acceptable doses to the bowel were exceeded for the conventional plan, and a substantial reduction of this excess was predicted for the thermoradiotherapy plan. In the second example, the thermoradiotherapy schedule with long time interval was shown to result in a substantially lower TCP.
Using biological modelling, our software can facilitate the evaluation of thermoradiotherapy plans and support individualised treatment decisions.
目前，关于热放疗治疗的临床决策是基于临床经验。对热疗放射增敏效果进行量化能够比较不同的治疗策略，并可为关于最佳治疗的临床决策提供支持。本文介绍的软件可通过计算等效三维剂量分布对热放疗计划进行生物学评估。
我们自行研发的软件（X-Term）使用线性二次模型的扩展版本来计算等效辐射剂量，即产生与热放疗治疗相同效果的辐射剂量。可以为每个勾画的结构分配单独的模型参数集，从而实现热疗放射增敏的组织特异性建模。计算后，可根据传统放疗计划标准对等效辐射剂量进行评估。通过两个实际例子对该过程进行说明。首先，对于一名先前接受过放疗的患者，比较了放疗和热放疗计划（预测肿瘤控制情况相同）的正常组织剂量。其次，评估了两种（其他方面相同）热放疗方案在放疗与热疗之间不同时间间隔下的肿瘤控制概率（TCP）。
这些例子表明，我们的软件可用于热放疗中的个体化治疗决策（第一个例子）和治疗优化（第二个例子）。在第一个例子中，传统计划超出了肠道的临床可接受剂量，而热放疗计划预计可大幅减少这种过量情况。在第二个例子中，长时间间隔的热放疗方案显示出TCP显著降低。
通过生物学建模，我们的软件可促进热放疗计划的评估并支持个体化治疗决策。
