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Optimizing principal component models for representing interfraction variation in lung cancer radiotherapy

机译:优化主成分模型以表示肺癌放疗中的分数变化

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摘要

>Purpose: To optimize modeling of interfractional anatomical variation during active breath-hold radiotherapy in lung cancer using principal component analysis (PCA).>Methods: In 12 patients analyzed, weekly CT sessions consisting of three repeat intrafraction scans were acquired with active breathing control at the end of normal inspiration. The gross tumor volume (GTV) and lungs were delineated and reviewed on the first week image by physicians and propagated to all other images using deformable image registration. PCA was used to model the target and lung variability during treatment. Four PCA models were generated for each specific patient: (1) Individual models for the GTV and each lung from one image per week (week to week, W2W); (2) a W2W composite model of all structures; (3) individual models using all images (weekly plus repeat intrafraction images, allscans); and (4) composite model with all images. Models were reconstructed retrospectively (using all available images acquired) and prospectively (using only data acquired up to a time point during treatment). Dominant modes representing at least 95% of the total variability were used to reconstruct the observed anatomy. Residual reconstruction error between the model-reconstructed and observed anatomy was calculated to compare the accuracy of the models.>Results: An average of 3.4 and 4.9 modes was required for the allscans models, for the GTV and composite models, respectively. The W2W model required one less mode in 40% of the patients. For the retrospective composite W2W model, the average reconstruction error was 0.7±0.2 mm, which increased to 1.1±0.5 mm when the allscans model was used. Individual and composite models did not have significantly different errors (p=0.15, paired t-test). The average reconstruction error for the prospective models of the GTV stabilized after four measurements at 1.2±0.5 mm and for the composite model after five measurements at 0.8±0.4 mm.>Conclusions: Retrospective PCA models were capable of reconstructing original GTV and lung shapes and positions within several millimeters with three to four dominant modes, on average. Prospective models achieved similar accuracy after four to five measurements.
机译:>目的:使用主成分分析(PCA)优化肺癌主动屏气放疗期间的组织间解剖学变异。>方法:在12例分析的患者中,每周进行一次CT在正常吸气结束时,通过主动呼吸控制获得了由三个重复的分数内扫描组成的扫描。医师在第一周图像上描绘并检查了总肿瘤体积(GTV)和肺部,并使用可变形图像配准将其传播到所有其他图像。 PCA用于在治疗过程中模拟目标和肺部变异性。为每个特定患者生成了四个PCA模型:(1)每周从一张图像(每周到一周,W2W)为GTV和每个肺部建立单独的模型; (2)所有结构的W2W复合模型; (3)使用所有图像的单独模型(每周加上重复的内部分数图像,全扫描); (4)具有所有图像的合成模型。回顾性(使用获得的所有可用图像)和前瞻性(仅使用治疗期间至某个时间点的数据)重建模型。代表至少95%的总变异性的显性模式用于重建观察到的解剖结构。计算模型重建和观察到的解剖结构之间的残留重建误差,以比较模型的准确性。>结果:对于所有扫描模型,GTV和复合模型,平均需要3.4和4.9个模式, 分别。 W2W模型要求40%的患者减少一种模式。对于回顾性复合W2W模型,平均重建误差为0.7±0.2 mm,使用全扫描模型时,平均重建误差增加到1.1±0.5 mm。个体模型和复合模型没有明显不同的误差(p = 0.15,配对t检验)。 GTV前瞻性模型的平均重建误差在1.2±0.5 mm处进行四次测量后稳定,而在0.8±0.4 mm处进行五次测量后对复合模型进行平均。>结论:回顾性PCA模型能够重建原始的GTV和肺部形状和位置平均在3到4个主导模式的几毫米内。经过四到五次测量,预期模型获得了相似的精度。

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