Abteilung für Medizinische Physik in der Strahlentherapie, Deutsches Krebsforschungszentrum, Heidelberg, Germany.
Z Med Phys. 2009;19(4):236-50. doi: 10.1016/j.zemedi.2009.08.002. Epub 2009 Sep 11.
It is still an unanswered question whether a relatively low dose of radiation to a large volume or a higher dose to a small volume produces the higher cancer incidence. This is of interest in view of modalities like IMRT or rotation therapy where high conformity to the target volume is achieved at the cost of a large volume of normal tissue exposed to radiation. Knowledge of the shape of the dose response for radiation-induced cancer is essential to answer the question of what risk of second cancer incidence is implied by which treatment modality. This study therefore models the dose response for radiation-induced second cancer after radiation therapy of which the exact mechanisms are still unknown. A second cancer risk estimation tool for treatment planning is presented which has the potential to be used for comparison of different treatment modalities, and risk is estimated on a voxel basis for different organs in two case studies. The presented phenomenological model summarises the impact of microscopic biological processes into effective parameters of mutation and cell sterilisation. In contrast to other models, the effective radiosensitivities of mutated and non-mutated cells are allowed to differ. Based on the number of mutated cells present after irradiation, the model is then linked to macroscopic incidence by summarising model parameters and modifying factors into natural cancer incidence and the dose response in the lower-dose region. It was found that all principal dose-response functions discussed in the literature can be derived from the model. However, from the investigation and due to scarcity of adequate data, rather vague statements about likelihood of dose-response functions can be made than a definite decision for one response. Based on the predicted model parameters, the linear response can probably be rejected using the dynamics described, but both a flattening response and a decrease appear likely, depending strongly on the effective cell sterilisation of the mutated cells. Thus insights could be gained into the impact of parameters describing the effective mutation or cell sterilisation of non-mutated as well as of mutated cells, which constitute precursors of cancer. The biggest drawbacks in the estimation of second cancer incidence remain the low statistical power of clinical studies on radiation induction of cancer and the inability to isolate the effect due to radiation alone - if the latter is possible at all. We conclude that at the present stage of knowledge, further investigations have to be carried out in order to really compare treatment modalities with respect to the second cancer risk they imply.
目前仍不清楚是大体积小剂量辐射还是小体积高剂量辐射会导致更高的癌症发病率。鉴于调强放疗或旋转治疗等方式,为了实现高靶区适形度,大量正常组织会受到辐射,因此这一问题很有意义。了解辐射诱导癌症的剂量反应形状对于回答哪种治疗方式会带来多大的二次癌症发病风险至关重要。本研究因此对放疗后辐射诱导的二次癌症的剂量反应进行建模,而后者的具体机制尚不清楚。本文提出了一种用于治疗计划的二次癌症风险评估工具,该工具具有用于比较不同治疗方式的潜力,并在两个案例研究中针对不同器官在体素水平上估计风险。所提出的现象学模型将微观生物学过程的影响概括为突变和细胞灭菌的有效参数。与其他模型不同的是,允许突变细胞和未突变细胞的有效放射敏感性不同。基于照射后存在的突变细胞数量,该模型通过将模型参数和修正因子概括为自然癌症发病率和低剂量区域的剂量反应,与宏观发病率联系起来。研究发现,文献中讨论的所有主要剂量反应函数都可以从该模型中推导出来。然而,由于调查和缺乏足够的数据,只能对剂量反应函数的可能性做出相当模糊的陈述,而不能对一个反应做出明确的决定。基于预测的模型参数,使用所描述的动力学,线性反应可能被拒绝,但无论是平坦的反应还是下降的反应都很有可能,这主要取决于突变细胞的有效细胞灭菌。因此,可以深入了解描述非突变细胞和突变细胞的有效突变或细胞灭菌的参数的影响,这些细胞构成了癌症的前体。在评估二次癌症发病率方面,最大的困难仍然是辐射诱导癌症的临床研究的统计功效低,以及无法单独隔离辐射的影响——如果有可能的话。我们的结论是,在目前的知识水平上,为了真正比较不同治疗方式所带来的二次癌症风险,还需要进一步研究。