Burman Chandra M
Memorial Sloan-Kettering Cancer Center, New York, N.Y., USA.
Front Radiat Ther Oncol. 2002;37:151-62. doi: 10.1159/000061314.
Response of human tissues to ionizing radiation is a complex process. It is influenced by many factors, such as use of chemotherapy drugs and underlying diseases such as diabetes and/or lung emphysema. A phenomenological model such as Lyman's is an attempt to predict the complication, for a variety of tissues, in the absence of these factors. The use of the model requires the knowledge of the parameters to predict the response for a specific endpoint. Clinical response data are needed to determine these parameters. Emami et al. [6] have provided some data, based on pre-CT and pre-3-D information, for some of the most serious complications. Based on this information the parameters were determined [4]. However, to validate and further improve the predictive power of the model, improved clinical response data are needed. With CT-based 3-D treatment planning systems the dose-volume information is routinely produced. Efforts by the radiation oncology community are needed to collect this information and correlate it with the clinical outcomes in a uniform and systematic way, not only for the most serious complications but also for less severe radiation-induced complications that are routinely considered in radiation therapy. Also, the information about the tissue response with underlying disease and drugs will be useful. The use of NTCP for plan comparison is useful. However, the incorporation of TCP and NTCP for designing the plan is remarkable. A plan can be optimized for the best outcome for the patient. It is hoped that as the models and parameters are refined and predictive power of the model increases, better plans will be produced, significantly improving the therapeutic ratio.
人体组织对电离辐射的反应是一个复杂的过程。它受到许多因素的影响，如化疗药物的使用以及诸如糖尿病和/或肺气肿等基础疾病。像莱曼模型这样的现象学模型试图在不存在这些因素的情况下预测各种组织的并发症。该模型的使用需要了解参数以预测特定终点的反应。需要临床反应数据来确定这些参数。埃马米等人[6]基于CT前和三维前信息提供了一些关于某些最严重并发症的数据。基于这些信息确定了参数[4]。然而，为了验证并进一步提高模型的预测能力，需要改进的临床反应数据。借助基于CT的三维治疗计划系统，剂量体积信息是常规生成的。放射肿瘤学界需要做出努力，以统一和系统的方式收集这些信息，并将其与临床结果相关联，不仅针对最严重的并发症，也针对放射治疗中常规考虑的不太严重的辐射诱导并发症。此外，关于伴有基础疾病和药物的组织反应的信息也将是有用的。使用正常组织并发症概率（NTCP）进行计划比较是有用的。然而，将肿瘤控制概率（TCP）和NTCP纳入计划设计则意义非凡。可以针对患者的最佳结果优化计划。希望随着模型和参数的完善以及模型预测能力的提高，能制定出更好的计划，显著提高治疗比。
