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局部晚期前列腺癌的自适应放疗:应用统计变形运动模型。

Adaptive radiotherapy in locally advanced prostate cancer using a statistical deformable motion model.

机构信息

Department of Medical Physics, Aarhus University Hospital , Aarhus , Denmark.

出版信息

Acta Oncol. 2013 Oct;52(7):1423-9. doi: 10.3109/0284186X.2013.818249. Epub 2013 Aug 22.

Abstract

UNLABELLED

Daily treatment plan selection from a plan library is a major adaptive radiotherapy strategy to account for individual internal anatomy variations. This strategy depends on the initial input images being representative for the variations observed later in the treatment course. Focusing on locally advanced prostate cancer, our aim was to evaluate if residual motion of the prostate (CTV-p) and the elective targets (CTV-sv, CTV-ln) can be prospectively accounted for with a statistical deformable model based on images acquired in the initial part of treatment.

METHODS

Thirteen patients with locally advanced prostate cancer, each with 9-10 repeat CT scans, were included. Displacement vectors fields (DVF) obtained from contour-based deformable registration of delineations in the repeat- and planning CT scans were used to create patient-specific statistical motion models using principal component analysis (PCA). For each patient and CTV, four PCA-models were created: one with all 9-10 DVF as input in addition to models with only four, five or six DVFs as input. Simulations of target shapes from each PCA-model were used to calculate iso-coverage levels, which were converted to contours. The levels were analyzed for sensitivity and precision.

RESULTS

A union of the simulated shapes was able to cover at least 97%, 97% and 95% of the volumes of the evaluated CTV shapes for PCA-models using six, five and four DVFs as input, respectively. There was a decrease in sensitivity with higher iso-coverage levels, with a sharper decline for greater target movements. Apart from having the steepest decline in sensitivity, CTV-sv also displayed the greatest influence on the number of geometries used in the PCA-model.

CONCLUSIONS

PCA-based simulations of residual motion derived from four to six DVFs as input could account for the majority of the target shapes present during the latter part of the treatment. CTV-sv displayed the greatest range in both sensitivity and precision.

摘要

未加标签

从计划库中选择每日治疗计划是应对个体内部解剖结构变化的主要自适应放射治疗策略。该策略取决于初始输入图像能够代表治疗过程后期观察到的变化。本文聚焦局部晚期前列腺癌,旨在评估基于治疗初始阶段采集的图像,是否可以前瞻性地预测前列腺(CTV-p)和选择性靶区(CTV-sv、CTV-ln)的残余运动。

方法

纳入 13 例局部晚期前列腺癌患者,每位患者均有 9-10 次重复 CT 扫描。通过轮廓变形配准获取的勾画位移向量场(DVF),用于创建基于主成分分析(PCA)的患者特定的统计运动模型。针对每位患者和 CTV,创建了 4 个 PCA 模型:一个模型使用所有 9-10 个 DVF 作为输入,另外三个模型分别仅使用 4、5 或 6 个 DVF 作为输入。使用每个 PCA 模型的模拟目标形状,计算等覆盖水平,并将其转换为轮廓。分析了这些水平的灵敏度和精度。

结果

使用 6、5 和 4 个 DVF 作为输入的 PCA 模型,模拟形状的联合能够覆盖评估的 CTV 形状的体积至少 97%、97%和 95%。随着等覆盖水平的提高,灵敏度逐渐降低,而对于更大的靶区运动,灵敏度的下降更为陡峭。除了灵敏度下降最陡峭外,CTV-sv 对 PCA 模型中使用的几何形状数量也有最大的影响。

结论

基于 PCA 的模拟,可以从 4 到 6 个 DVF 作为输入,来预测治疗后期的大部分靶区形状。CTV-sv 在灵敏度和精度方面的变化范围最大。

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