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针对(103)Pd 乳腺近距离放疗的患者特异性蒙特卡罗剂量计算。

Patient-specific Monte Carlo dose calculations for (103)Pd breast brachytherapy.

作者信息

Miksys N, Cygler J E, Caudrelier J M, Thomson R M

机构信息

Department of Physics, Carleton Laboratory for Radiotherapy Physics, Carleton University, Ottawa, ON, Canada.

出版信息

Phys Med Biol. 2016 Apr 7;61(7):2705-29. doi: 10.1088/0031-9155/61/7/2705. Epub 2016 Mar 15.

Abstract

This work retrospectively investigates patient-specific Monte Carlo (MC) dose calculations for (103)Pd permanent implant breast brachytherapy, exploring various necessary assumptions for deriving virtual patient models: post-implant CT image metallic artifact reduction (MAR), tissue assignment schemes (TAS), and elemental tissue compositions. Three MAR methods (thresholding, 3D median filter, virtual sinogram) are applied to CT images; resulting images are compared to each other and to uncorrected images. Virtual patient models are then derived by application of different TAS ranging from TG-186 basic recommendations (mixed adipose and gland tissue at uniform literature-derived density) to detailed schemes (segmented adipose and gland with CT-derived densities). For detailed schemes, alternate mass density segmentation thresholds between adipose and gland are considered. Several literature-derived elemental compositions for adipose, gland and skin are compared. MC models derived from uncorrected CT images can yield large errors in dose calculations especially when used with detailed TAS. Differences in MAR method result in large differences in local doses when variations in CT number cause differences in tissue assignment. Between different MAR models (same TAS), PTV [Formula: see text] and skin [Formula: see text] each vary by up to 6%. Basic TAS (mixed adipose/gland tissue) generally yield higher dose metrics than detailed segmented schemes: PTV [Formula: see text] and skin [Formula: see text] are higher by up to 13% and 9% respectively. Employing alternate adipose, gland and skin elemental compositions can cause variations in PTV [Formula: see text] of up to 11% and skin [Formula: see text] of up to 30%. Overall, AAPM TG-43 overestimates dose to the PTV ([Formula: see text] on average 10% and up to 27%) and underestimates dose to the skin ([Formula: see text] on average 29% and up to 48%) compared to the various MC models derived using the post-MAR CT images studied herein. The considerable differences between TG-43 and MC models underline the importance of patient-specific MC dose calculations for permanent implant breast brachytherapy. Further, the sensitivity of these MC dose calculations due to necessary assumptions illustrates the importance of developing a consensus modelling approach.

摘要

本研究回顾性地调查了针对¹⁰³Pd永久性植入式乳腺近距离放射治疗的患者特异性蒙特卡罗(MC)剂量计算,探索了推导虚拟患者模型所需的各种假设:植入后CT图像金属伪影减少(MAR)、组织分配方案(TAS)和元素组织组成。三种MAR方法(阈值法、三维中值滤波、虚拟正弦图)应用于CT图像;将所得图像相互比较,并与未校正图像进行比较。然后通过应用不同的TAS推导虚拟患者模型,范围从TG - 186基本建议(混合脂肪和腺体组织,密度为文献中统一推导得出)到详细方案(通过CT推导密度对脂肪和腺体进行分割)。对于详细方案,考虑了脂肪和腺体之间不同的质量密度分割阈值。比较了几种文献中推导得出的脂肪、腺体和皮肤的元素组成。从未校正CT图像推导的MC模型在剂量计算中可能会产生较大误差,特别是与详细的TAS一起使用时。当CT值的变化导致组织分配差异时,MAR方法的差异会导致局部剂量的巨大差异。在不同的MAR模型(相同的TAS)之间,计划靶体积(PTV)[公式:见原文]和皮肤[公式:见原文]各自变化高达6%。基本的TAS(混合脂肪/腺体组织)通常比详细的分割方案产生更高的剂量指标:PTV[公式:见原文]和皮肤[公式:见原文]分别高出多达13%和9%。采用不同的脂肪、腺体和皮肤元素组成会导致PTV[公式:见原文]变化高达11%,皮肤[公式:见原文]变化高达30%。总体而言,与本文研究的使用MAR后CT图像推导的各种MC模型相比,AAPM TG - 43高估了PTV的剂量(平均高估10%,最高达27%),低估了皮肤的剂量(平均低估29%,最高达48%)。TG - 43与MC模型之间的显著差异突显了针对永久性植入式乳腺近距离放射治疗进行患者特异性MC剂量计算的重要性。此外,由于必要假设导致的这些MC剂量计算的敏感性说明了开发一种共识建模方法的重要性。

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