D'Souza Warren D, Thames Howard D, Kuban Deborah A
Department of Radiation Physics, University of Texas M. D. Anderson Cancer Center, Houston, TX, USA.
Int J Radiat Oncol Biol Phys. 2004 Apr 1;58(5):1540-8. doi: 10.1016/j.ijrobp.2003.09.016.
Although it is known that brachytherapy dose distributions are highly heterogeneous, the effect of particular dose distribution patterns on tumor control probability (TCP) is unknown. It is unlikely that clinical results will throw light on the question in the near future, given the long follow-up and detailed dosimetry required for each patient. We used detailed dose distribution data from 50 patients combined with radiobiologic parameters consistent with what is known about TCP curves for prostate cancer to study the changes in TCP that accompany gross dosimetric measures and particular dosing irregularities (e.g., moderate underdosing of large volumes vs. extreme underdosing of small volumes).
For each of the 50 patients with organ-confined prostate cancer who had undergone 125I prostate implants alone at our clinic, postimplant CT scans were obtained approximately 1 month after implantation. Dose distribution information was obtained from postimplant dosimetry. The percentage of the prostate volume receiving a specified dose was recorded from the respective differential dose-volume histograms in 10-Gy bins. In addition, the percentage of prostate volume underdosed at varying fractions of the prescription dose were determined, as was the minimal prostate dose. The log-normal distributions of the radiobiologic parameters [ln(initial clonogen number), alpha, and alpha/beta] were adjusted so that the predicted population parameters (steepness and location) of the dose-response curves for external beam radiotherapy agreed with the published estimates. The variability in the dose-volume details was increased by scaling the dose distributions by factors ranging from 0.7 to 1.5, thereby simulating, for each of the patients, nine new patients with different total doses but identical relative distributions of the dose over the voxels. Radiobiologic variability between the selected dose distributions was then removed by averaging >50 randomly chosen sets of radiobiologic parameters from the log-normal distributions to estimate the TCP for each of the dose distributions, giving some insight into the TCP variations with conventional dosimetric indexes and different patterns of underdosing.
Using the 450 dose distributions created by expanding the 50-patient data set, the volume of the prostate that was extremely underdosed (between 50% and 70% of the prescription dose) was related to the volume that was moderately underdosed (between 80% and 100% of the prescription dose). We found that the individual TCP is greatly dependent on the inhomogeneous dose distribution and the dosimetric indexes, such as the volume of prostate receiving 100% of the prescribed dose (V100) and the maximal dose received by 90% of the prostate volume (D90), which, by themselves, are not always accurate predictors of control probabilities. In a multivariate analysis of the dependence of TCP on these parameters (V100, D90, minimal dose, and moderately and severely underdosed volumes), only D90 and the minimal dose were statistically significant. Generally speaking, however, a lower minimal dose means a lower TCP.
The work described here was an hypothesis-generating study. Our results showed that even if the V100 and D90 are nearly identical for 2 patients, there can be (and frequently are) significant differences in the dose distributions in the subvolumes of the prostate. Under simulated dose-response conditions (i.e., with variations in the dose distribution), the D90 and minimal dose significantly affected the TCP but the V100 and the volumes moderately or severely underdosed did not. In general, one must consider the totality of the dose distribution to evaluate the dosimetric quality of a low-dose-rate prostate implant. TCP is not a monotonic function of extreme or moderate underdosing. In some instances, extreme underdosing of relatively small volumes may result in a greater TCP than moderate underdosing of relatively large volumes and vice versa.
虽然已知近距离放射治疗的剂量分布高度不均匀，但特定剂量分布模式对肿瘤控制概率（TCP）的影响尚不清楚。鉴于每个患者都需要长期随访和详细的剂量测定，临床结果近期不太可能阐明这个问题。我们使用了50例患者的详细剂量分布数据，并结合与前列腺癌TCP曲线已知情况一致的放射生物学参数，来研究伴随总体剂量测定指标和特定剂量不规则性（例如，大体积的中度剂量不足与小体积的极端剂量不足）的TCP变化。
对于在我们诊所仅接受过¹²⁵I前列腺植入的50例器官局限性前列腺癌患者中的每一位，在植入后约1个月获得植入后CT扫描。剂量分布信息从植入后剂量测定中获取。从相应的差分剂量 - 体积直方图中，以10 Gy区间记录接受特定剂量的前列腺体积百分比。此外，还确定了在不同处方剂量分数下剂量不足的前列腺体积百分比以及最小前列腺剂量。调整放射生物学参数[ln（初始克隆原数量）、α和α/β]的对数正态分布，以使外照射放疗剂量反应曲线的预测总体参数（斜率和位置）与已发表的估计值一致。通过将剂量分布按0.7至1.5的因子进行缩放，增加剂量 - 体积细节的变异性，从而为每位患者模拟9例具有不同总剂量但体素上剂量相对分布相同的新患者。然后通过从对数正态分布中平均>50个随机选择的放射生物学参数集来消除所选剂量分布之间的放射生物学变异性，以估计每个剂量分布的TCP，从而深入了解TCP随传统剂量测定指标和不同剂量不足模式的变化。
使用通过扩展50例患者数据集创建得到的450个剂量分布，前列腺中极端剂量不足（在处方剂量的50%至70%之间）的体积与中度剂量不足（在处方剂量的80%至100%之间）的体积相关。我们发现个体TCP极大地依赖于不均匀的剂量分布和剂量测定指标，例如接受100%处方剂量的前列腺体积（V₁₀₀）以及90%前列腺体积所接受的最大剂量（D₉₀），而这些指标本身并不总是控制概率的准确预测指标。在对TCP对这些参数（V₁₀₀、D₉₀、最小剂量以及中度和重度剂量不足体积）依赖性的多变量分析中，只有D₉₀和最小剂量具有统计学意义。然而，一般来说，较低的最小剂量意味着较低的TCP。
这里描述的工作是一项产生假设的研究。我们的结果表明，即使两名患者的V₁₀₀和D₉₀几乎相同，前列腺子体积中的剂量分布也可能存在（并且经常存在）显著差异。在模拟的剂量反应条件下（即剂量分布存在变化），D₉₀和最小剂量显著影响TCP，但V₁₀₀以及中度或重度剂量不足的体积则不然。一般来说，必须考虑剂量分布的整体情况来评估低剂量率前列腺植入的剂量测定质量。TCP不是极端或中度剂量不足的单调函数。在某些情况下，相对小体积的极端剂量不足可能导致比相对大体积的中度剂量不足更高的TCP，反之亦然。
