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用正电子发射断层扫描定义放射治疗靶区。

Defining a radiotherapy target with positron emission tomography.

作者信息

Black Quinten C, Grills Inga S, Kestin Larry L, Wong Ching-Yee O, Wong John W, Martinez Alvaro A, Yan Di

机构信息

21st Century Oncology, Inc., Asheville, NC, USA.

出版信息

Int J Radiat Oncol Biol Phys. 2004 Nov 15;60(4):1272-82. doi: 10.1016/j.ijrobp.2004.06.254.

Abstract

PURPOSE

F-18 fluorodeoxyglucose positron emission tomography (FDG-PET) imaging is now considered the most accurate clinical staging study for non-small-cell lung cancer (NSCLC) and is also important in the staging of multiple other malignancies. Gross tumor volume (GTV) definition for radiotherapy, however, is typically based entirely on computed tomographic data. We performed a series of phantom studies to determine an accurate and uniformly applicable method for defining a GTV with FDG-PET.

METHODS AND MATERIALS

A model-based method was tested by a phantom study to determine a threshold, or unique cutoff of standardized uptake value based on body weight (standardized uptake value [SUV]) for FDG-PET based GTV definition. The degree to which mean target SUV, background FDG concentration, and target volume influenced that GTV definition were evaluated. A phantom was constructed consisting of a 9.0-L cylindrical tank. Glass spheres with volumes ranging from 12.2 to 291.0 cc were suspended within the tank, with a minimum separation of 4 cm between the edges of the spheres. The sphere volumes were selected based on the range of NSCLC patient tumor volumes seen in our clinic. The tank and spheres were filled with a variety of known concentrations of FDG in several experiments and then scanned using a General Electric Advance PET scanner. In the initial experiment, six spheres with identical volumes were filled with varying concentrations of FDG (mean SUV = 1.85 approximately 9.68) and suspended within a background bath of FDG at a similar concentration to that used in clinical practice (0.144 muCi/mL). The second experiment was identical to the first, but was performed at 0.144 and 0.036 muCi/mL background concentrations to determine the effect of background FDG concentration on sphere definition. In the third experiment, six spheres with volumes of 12.2 to 291.0 cc were filled with equal concentrations of FDG and suspended in a standard background FDG concentration of 0.144 muCi/mL. Sphere images in each experiment were auto-contoured (simulating a GTV) using the threshold SUV that yielded a volume matching that of the known sphere volume. A regressive function was constructed to represent the relationship between the threshold SUV and the mean target SUV. This function was then applied to define the GTV of 15 NSCLC patients. The GTV volumes were compared to those determined by a fixed image intensity threshold proposed by other investigators.

RESULTS

There was a strong linear relationship between the threshold SUV and the mean target SUV. The linear regressive function derived was: threshold SUV = 0.307 x (mean target SUV) + 0.588. The background concentration and target volume indirectly affect the threshold SUV by way of their influence on the mean target SUV. We applied the linear regressive function, as well as a fixed image intensity threshold (42% of maximum intensity) to the sphere phantoms and 15 patients with NSCLC. The results indicated that a much smaller deviation occurred when the threshold SUV regressive function was utilized to estimate the phantom volume as compared to the fixed image intensity threshold. The average absolute difference between the two methods was 21% with respect to the true phantom volume. The deviation became even more pronounced when applied to true patient GTV volumes, with a mean difference between the two methods of 67%. This was largely due to a greater degree of heterogeneity in the SUV of tumors over phantoms.

CONCLUSIONS

An FDG-PET-based GTV can be systematically defined using a threshold SUV according to the regressive function described above. The threshold SUV for defining the target is strongly dependent on the mean target SUV of the target, and can be uniquely determined through the proposed iteration process.

摘要

目的

F-18氟脱氧葡萄糖正电子发射断层扫描(FDG-PET)成像目前被认为是对非小细胞肺癌(NSCLC)最准确的临床分期检查,在多种其他恶性肿瘤的分期中也很重要。然而,放射治疗的大体肿瘤体积(GTV)定义通常完全基于计算机断层扫描数据。我们进行了一系列体模研究,以确定一种准确且统一适用的用FDG-PET定义GTV的方法。

方法和材料

通过体模研究测试了一种基于模型的方法,以确定基于体重的标准化摄取值(SUV)的阈值或唯一截断值,用于基于FDG-PET的GTV定义。评估了平均目标SUV、背景FDG浓度和目标体积对该GTV定义的影响程度。构建了一个由9.0升圆柱形水箱组成的体模。将体积从12.2到291.0立方厘米不等的玻璃球悬浮在水箱内,球边缘之间的最小间距为4厘米。根据我们诊所中NSCLC患者肿瘤体积的范围选择球的体积。在几个实验中,水箱和球中填充了各种已知浓度的FDG,然后使用通用电气Advance PET扫描仪进行扫描。在初始实验中,六个体积相同的球填充了不同浓度的FDG(平均SUV = 1.85至9.68),并悬浮在与临床实践中使用的浓度相似的FDG背景浴中(0.144μCi/mL)。第二个实验与第一个相同,但在0.144和0.036μCi/mL的背景浓度下进行,以确定背景FDG浓度对球定义的影响。在第三个实验中,六个体积为12.2至291.0立方厘米的球填充了相等浓度的FDG,并悬浮在0.144μCi/mL的标准背景FDG浓度中。使用产生与已知球体积匹配的体积的SUV阈值对每个实验中的球图像进行自动轮廓勾画(模拟GTV)。构建了一个回归函数来表示阈值SUV与平均目标SUV之间的关系。然后将该函数应用于定义15例NSCLC患者的GTV。将GTV体积与其他研究者提出的固定图像强度阈值确定的体积进行比较。

结果

阈值SUV与平均目标SUV之间存在很强的线性关系。得出的线性回归函数为:阈值SUV = 0.307×(平均目标SUV)+ 0.588。背景浓度和目标体积通过对平均目标SUV的影响间接影响阈值SUV。我们将线性回归函数以及固定图像强度阈值(最大强度的42%)应用于球形体模和15例NSCLC患者。结果表明,与固定图像强度阈值相比,当使用阈值SUV回归函数估计体模体积时,偏差要小得多。两种方法之间相对于真实体模体积的平均绝对差异为21%。当应用于真实患者的GTV体积时,偏差变得更加明显,两种方法之间的平均差异为67%。这主要是由于肿瘤的SUV比体模中的异质性程度更高。

结论

可以根据上述回归函数使用阈值SUV系统地定义基于FDG-PET的GTV。定义目标的阈值SUV强烈依赖于目标的平均目标SUV,并且可以通过所提出的迭代过程唯一确定。

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