Department of Medical Radiation Physics, Karolinska Institutet and Stockholm University, S-17176 Stockholm, Sweden.
Med Phys. 2011 May;38(5):2382-97. doi: 10.1118/1.3570613.
This study aims at demonstrating a new method for treatment plan evaluation and comparison based on the radiobiological response of individual voxels. This is performed by applying them on three different cancer types and treatment plans of different conformalities. Furthermore, their usefulness is examined in conjunction with traditionally applied radiobiological and dosimetric treatment plan evaluation criteria.
Three different cancer types (head and neck, breast and prostate) were selected to quantify the benefits of the proposed treatment plan evaluation method. In each case, conventional conformal radiotherapy (CRT) and intensity modulated radiotherapy (IMRT) treatment configurations were planned. Iso-probability of response charts was produced by calculating the response probability in every voxel using the linear-quadratic-Poisson model and the dose-response parameters of the corresponding structure to which this voxel belongs. The overall probabilities of target and normal tissue responses were calculated using the Poisson and the relative seriality models, respectively. The 3D dose distribution converted to a 2 Gy fractionation, D2(GY) and iso-BED distributions are also shown and compared with the proposed methodology. Response-probability volume histograms (RVH) were derived and compared with common dose volume histograms (DVH). The different dose distributions were also compared using the complication-free tumor control probability, P+, the biologically effective uniform dose, D, and common dosimetric criteria.
3D Iso-probability of response distributions is very useful for plan evaluation since their visual information focuses on the doses that are likely to have a larger clinical effect in that particular organ. The graphical display becomes independent of the prescription dose highlighting the local radiation therapy effect in each voxel without the loss of important spatial information. For example, due to the exponential nature of the Poisson distribution, cold spots in the target volumes or hot spots in the normal tissues are much easier to be identified. Response-volume histograms, as DVH, can also be derived and used for plan comparison. RVH are advantageous since by incorporating the radiobiological properties of each voxel they summarize the 3D distribution into 2D without the loss of relevant information. Thus, more clinically relevant radiobiological objectives and constraints could be defined and used in treatment planning optimization. These measures become increasingly important when dose distributions need to be designed according to the microscopic biological properties of tumor and normal tissues.
The proposed methods do not aim to replace quantifiers like the probabilities of total tissue response, which ultimately are the quantities of interest to evaluate treatment success. However, iso-probability of response charts and response-probability volume histograms illustrates more clearly the difference in effectiveness between different treatment plans than the information provided by alternative dosimetric data. The use of 3D iso-probability of response distributions could serve as a good descriptor of the effectiveness of a dose distribution indicating primarily the regions in a tissue that dominate its response.
本研究旨在展示一种基于个体体素放射生物学反应的新的治疗计划评估和比较方法。通过将其应用于三种不同的癌症类型和不同适形性的治疗计划来实现这一点。此外，还结合传统应用的放射生物学和剂量学治疗计划评估标准来检验它们的有用性。
选择三种不同的癌症类型（头颈部、乳腺和前列腺）来量化所提出的治疗计划评估方法的益处。在每种情况下，都规划了常规适形放疗（CRT）和调强放疗（IMRT）治疗方案。通过使用线性二次泊松模型和相应结构的剂量反应参数计算每个体素的反应概率，生成等反应概率图。使用泊松和相对序列模型分别计算靶区和正常组织反应的总体概率。还显示并比较了转换为 2 Gy 分割的 3D 剂量分布、D2（GY）和等 BED 分布与所提出的方法。导出反应概率体素直方图（RVH）并与常见的剂量体素直方图（DVH）进行比较。还使用无并发症肿瘤控制概率 P+、生物有效均匀剂量 D 和常见剂量学标准比较不同的剂量分布。
3D 等反应概率分布对于计划评估非常有用，因为它们的视觉信息集中在特定器官中可能具有更大临床效果的剂量上。图形显示变得独立于处方剂量，突出了每个体素中的局部放射治疗效果，而不会丢失重要的空间信息。例如，由于泊松分布的指数性质，靶区中的冷点或正常组织中的热点更容易被识别。也可以导出反应体积直方图，如 DVH，并用于计划比较。RVH 具有优势，因为它们通过将每个体素的放射生物学特性结合起来，将 3D 分布概括为 2D，而不会丢失相关信息。因此，可以定义和使用更多与临床相关的放射生物学目标和约束条件来进行治疗计划优化。当需要根据肿瘤和正常组织的微观生物学特性设计剂量分布时，这些措施变得越来越重要。
所提出的方法并不是要替代总组织反应概率等定量指标，这些指标最终是评估治疗成功的关注量。然而，等反应概率图和反应概率体素直方图比替代剂量数据提供的信息更清楚地说明了不同治疗计划之间的有效性差异。3D 等反应概率分布的使用可以作为剂量分布有效性的良好描述符，主要指示组织中主导其反应的区域。
