TH‐E‐230A‐01: Can Dose‐Volume Parameters Be Replaced with GEUD in the Treatment Planning Process?
TH‐E‐230A‐01: Can Dose‐Volume Parameters Be Replaced with GEUD in the Treatment Planning Process?
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TH-E-230A-01：治疗计划过程中剂量-体积参数可以用 GEUD 代替吗？
DOI：
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发表时间：
2006
期刊：
影响因子：
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通讯作者：
J. Deasy
中科院分区：
文献类型：
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作者：
V. Clark;I. E. Naqa;A. Hope;G. Suneja;J. Bradley;J. Deasy
目的：剂量-体积指标通常与结果相关，并常用于评估治疗方案。不幸的是，当用于imrt治疗计划时，剂量-体积指标在计算上是复杂的（非凸的），并且可能使dvh在限制剂量附近弯曲。我们研究了通过调整指数参数是否可以使广义等效均匀剂量（gEUD）与DVH曲线的不同部分高度相关。如果是这样，在治疗计划和评估中，gEUD可能是一个平滑的、计算上有吸引力的剂量-体积计量法的替代品。方法与材料：我们将gEUD与各种参数a值和临床适用的剂量-体积限制相关联。使用了三个数据集：肺、食管和前列腺，分别有219、263和291个患者计划。我们以0.2的间隔测试了- 10到10之间的a值，在某些情况下测试的值低至- 40。测试的剂量-体积限制包括：肺的V10、V20和V30，食道的V55，前列腺PTV和肺PTV的D95。结果：对于所有测试的病例，我们发现Spearman相关性在0.917和0.989之间（平均相关性0.956），p值可以忽略不计（<1×10−6）。体积指标的取值范围为0.4 ~ 3.2，肺PTV和前列腺PTV剂量指标的取值范围分别为- 7.8 ~ - 27.2。结论：在所测试的数据集中，剂量-体积指标与gEUD之间存在显著且强的相关性。该方法的实际应用是，对于特定的剂量-体积度量，我们可以找到a （gEUD参数）具有最高相关性的值，并在IMRT优化中使用凸gEUD函数代替非凸剂量-体积约束，从而使优化更快，更能有效地实现全局最优。利益冲突：部分由NIH资助R01 CA85181和TomoTherapy， Inc.资助。
Purpose: Dose‐volume metrics have often been correlated with outcomes and are often used to evaluate treatment plans. Unfortunately, when used for IMRTtreatment planning, dose‐volume metrics are computationally complex (non‐convex) and can warp DVHs near the constraint dose. We investigate whether the generalized equivalent uniform dose (gEUD) can be made to highly correlate with different parts of the DVH curve by tuning the exponential parameter. If so, gEUD may be a smooth and computationally attractive replacement for dose‐volume metrics in treatment planning and evaluation. Method and Materials: We correlated gEUD with various values of its parameter a and clinically applicable dose‐volume constraints. Three datasets were used: lung, esophagus, and prostate, with 219, 263, and 291 patient plans, respectively. We tested values of a between −10 and 10 by intervals of 0.2 and in some cases tested values as low as −40. The dose‐volume constraints tested include: V10, V20, and V30 for lung, V55 for esophagus, and D95 for prostate PTV and lung PTV. Results: For all cases tested, we found a Spearman correlation between 0.917 and 0.989 (mean correlation 0.956) with negligible (<1×10−6) p‐values. Values of a ranged from 0.4 to 3.2 for volume metrics and −7.8 to −27.2 for lung PTV and prostate PTV dose metrics (respectively). Conclusion: There is a significant and strong correlation between dose‐volume metrics and gEUD for the datasets tested. The practical application of this is that for a particular dose‐volume metric, we can find the value of a (the gEUD parameter) with the highest correlation and use the convex gEUD function in place of the non‐convex dose‐volume constraint in the IMRT optimization, thereby allowing optimization to be faster and more able to efficiently achieve a global optimum. Conflict of Interest: Partially supported by NIH grant R01 CA85181 and a grant from TomoTherapy, Inc.