Department of Radiation Oncology, University of Texas Health Sciences Center, San Antonio, Texas 78229, USA.
Med Phys. 2013 Mar;40(3):031715. doi: 10.1118/1.4792636.
Radiation therapy is often delivered by multiple sequential treatment plans. For an accurate radiobiological evaluation of the overall treatment, fractionation corrections to each dose distribution must be applied before summing the three-dimensional dose matrix of each plan since the simpler approach of performing the fractionation correction to the total dose-volume histograms, obtained by the arithmetical sum of the different plans, becomes inaccurate for more heterogeneous dose patterns. In this study, the differences between these two fractionation correction methods, named here as exact (corrected before) and approximate (after summation), respectively, are assessed for different cancer types.
Prostate, breast, and head and neck (HN) tumor patients were selected to quantify the differences between two fractionation correction methods (the exact vs the approximate). For each cancer type, two different treatment plans were developed using uniform (CRT) and intensity modulated beams (IMRT), respectively. The responses of the target and normal tissue were calculated using the Poisson linear-quadratic-time model and the relative seriality model, respectively. All treatments were radiobiologically evaluated and compared using the complication-free tumor control probability (P+), the biologically effective uniform dose (D) together with common dosimetric criteria.
For the prostate cancer patient, an underestimation of around 14%-15% in P+ was obtained when the fractionation correction was applied after summation compared to the exact approach due to significant biological and dosimetric variations obtained between the two fractionation correction methods in the involved lymph nodes. For the breast cancer patient, an underestimation of around 3%-4% in the maximum dose in the heart was obtained. Despite the dosimetric differences in this organ, no significant variations were obtained in treatment outcome. For the HN tumor patient, an underestimation of about 5% in treatment outcome was obtained for the CRT plan as a result of an underestimation of the planning target volume control probability by about 10%. An underestimation of about 6% in the complication probability of the right parotid was also obtained. For all the other organs at risk, dosimetric differences of up to 4% were obtained but with no significant impact in the expected clinical outcome. However, for the IMRT plan, an overestimation in P+ of 4.3% was obtained mainly due to an underestimation of the complication probability of the left and right parotids (2.9% and 5.8%, respectively).
The use of the exact fractionation correction method, which is applying fractionation correction on the separate dose distributions of a multiple phase treatment before their summation was found to have a significant expected clinical impact. For regions of interest that are irradiated with very heterogeneous dose distributions and significantly different doses per fraction in the different treatment phases, the exact fractionation correction method needs to be applied since a significant underestimation of the true patient outcome can be introduced otherwise.
放射治疗通常通过多个连续的治疗计划进行。为了对整个治疗进行准确的放射生物学评估，必须在对每个计划的三维剂量矩阵求和之前对每个剂量分布进行分割修正，因为对于更不均匀的剂量模式，通过对不同计划的总和进行分割修正来获得总剂量-体积直方图的简单方法变得不准确。在这项研究中，评估了这两种分割修正方法之间的差异，这里分别称为精确（在求和之前修正）和近似（在求和之后修正），并针对不同的癌症类型进行了评估。
选择前列腺、乳腺和头颈部（HN）肿瘤患者，以量化两种分割修正方法（精确与近似）之间的差异。对于每种癌症类型，分别使用均匀（CRT）和强度调制射线（IMRT）开发了两种不同的治疗计划。使用泊松线性二次时间模型和相对序列模型分别计算靶区和正常组织的反应。使用无并发症肿瘤控制概率（P+）、生物有效均匀剂量（D）和常见剂量学标准对所有治疗进行放射生物学评估和比较。
对于前列腺癌患者，由于在涉及的淋巴结中两种分割修正方法之间存在显著的生物学和剂量学差异，因此在求和后应用分割修正时，P+的低估约为 14%-15%，与精确方法相比。对于乳腺癌患者，心脏内最大剂量的低估约为 3%-4%。尽管该器官存在剂量学差异，但在治疗结果中未观察到显著变化。对于 HN 肿瘤患者，由于 CRT 计划的计划靶区控制概率低估约 10%，导致治疗结果低估约 5%。右腮腺并发症概率也低估约 6%。对于所有其他危及器官，尽管剂量学差异高达 4%，但对预期的临床结果没有显著影响。然而，对于 IMRT 计划，P+的高估约为 4.3%，主要是由于左、右腮腺并发症概率的低估（分别为 2.9%和 5.8%）。
使用精确的分割修正方法，即在多个阶段治疗的单独剂量分布求和之前对其进行分割修正，被发现具有显著的临床预期影响。对于用非常不均匀的剂量分布照射且在不同治疗阶段每剂量分割的剂量显著不同的感兴趣区域，需要应用精确的分割修正方法，因为否则可能会引入对真实患者结果的严重低估。
