School of Physics, Mathematics and Computing, University of Western Australia, Crawley, Western Australia, Australia.
Department of Radiation Oncology, Sir Charles Gairdner Hospital, Nedlands, Western Australia, Australia.
Phys Med Biol. 2021 Jun 17;66(12). doi: 10.1088/1361-6560/ac0681.
For decades, dose-volume information for segmented anatomy has provided the essential data for correlating radiotherapy dosimetry with treatment-induced complications. Dose-volume information has formed the basis for modelling those associations via normal tissue complication probability (NTCP) models and for driving treatment planning. Limitations to this approach have been identified. Many studies have emerged demonstrating that the incorporation of information describing the spatial nature of the dose distribution, and potentially its correlation with anatomy, can provide more robust associations with toxicity and seed more general NTCP models. Such approaches are culminating in the application of computationally intensive processes such as machine learning and the application of neural networks. The opportunities these approaches have for individualising treatment, predicting toxicity and expanding the solution space for radiation therapy are substantial and have clearly widespread and disruptive potential. Impediments to reaching that potential include issues associated with data collection, model generalisation and validation. This review examines the role of spatial models of complication and summarises relevant published studies. Sources of data for these studies, appropriate statistical methodology frameworks for processing spatial dose information and extracting relevant features are described. Spatial complication modelling is consolidated as a pathway to guiding future developments towards effective, complication-free radiotherapy treatment.
几十年来，分段解剖的剂量-体积信息为将放射治疗剂量与治疗引起的并发症相关联提供了必要的数据。剂量-体积信息已成为通过正常组织并发症概率 (NTCP) 模型对这些关联进行建模以及驱动治疗计划的基础。已经确定了这种方法的局限性。许多研究表明，纳入描述剂量分布空间性质的信息，以及其与解剖结构的潜在相关性，可以提供与毒性更紧密的关联，并为更通用的 NTCP 模型提供依据。这些方法最终应用了计算密集型的过程，如机器学习和神经网络的应用。这些方法在实现治疗个体化、预测毒性和扩大放射治疗的解决方案空间方面具有巨大的潜力，并且具有明显的广泛和颠覆性的潜力。实现这一潜力的障碍包括与数据收集、模型泛化和验证相关的问题。本文回顾了并发症的空间模型的作用，并总结了相关的已发表研究。本文描述了这些研究的数据来源、处理空间剂量信息和提取相关特征的适当统计方法框架。空间并发症建模被整合为指导未来发展以实现有效、无并发症的放射治疗的途径。
