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Recommendations for dose calculations of lung cancer treatment plans treated with stereotactic ablative body radiotherapy (SABR)

机译:肺癌治疗计划剂量计算的建议(SABR)治疗肺癌治疗计划(SABR)

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The purpose of this study was to systematically evaluate dose distributions computed with 5 different dose algorithms for patients with lung cancers treated using stereotactic ablative body radiotherapy (SABR). Treatment plans for 133 lung cancer patients, initially computed with a 1D-pencil beam (equivalent-path-length, EPL-1D) algorithm, were recalculated with 4 other algorithms commissioned for treatment planning, including 3-D pencil-beam (EPL-3D), anisotropic analytical algorithm (AAA), collapsed cone convolution superposition (CCC), and Monte Carlo (MC). The plan prescription dose was 48 Gy in 4 fractions normalized to the 95% isodose line. Tumors were classified according to location: peripheral tumors surrounded by lung (lung-island, N=39), peripheral tumors attached to the rib-cage or chest wall (lung-wall, N=44), and centrally-located tumors (lung-central, N=50). Relative to the EPL-1D algorithm, PTV D95 and mean dose values computed with the other 4 algorithms were lowest for "lung-island" tumors with smallest field sizes (3-5 cm). On the other hand, the smallest differences were noted for lung-central tumors treated with largest field widths (7-10 cm). Amongst all locations, dose distribution differences were most strongly correlated with tumor size for lung-island tumors. For most cases, convolution/superposition and MC algorithms were in good agreement. Mean lung dose (MLD) values computed with the EPL-1D algorithm were highly correlated with that of the other algorithms (correlation coefficient =0.99). The MLD values were found to be ~10% lower for small lung-island tumors with the model-based (conv/superposition and MC) vs. the correction-based (pencil-beam) algorithms with the model-based algorithms predicting greater low dose spread within the lungs. This study suggests that pencil beam algorithms should be avoided for lung SABR planning. For the most challenging cases, small tumors surrounded entirely by lung tissue (lung-island type), a Monte-Carlo-based algorithm may be warranted.
机译:本研究的目的是系统地评估用5种不同剂量算法计算的剂量分布,用于使用立体定向烧蚀体放射治疗(SABR)处理的肺癌患者。 133例肺癌患者的治疗计划最初用1D铅笔束(等效路径长度,EPL-1D)算法进行了计算,用4个其他算法进行了一次用于治疗计划,包括3-D铅笔束(EPL- 3D),各向异性分析算法(AAA),折叠锥卷积叠加(CCC)和蒙特卡罗(MC)。计划处方剂量为48倍,在4分馏分中归一化至95%同学线。肿瘤根据所在的位置进行分类:肺(肺岛,N = 39)包围的外周肿瘤,附着在肋骨或胸壁(肺壁,N = 44)和中心位的肿瘤上附着外周肿瘤(肺部 - 中央,n = 50)。相对于EPL-1D算法,用其他4算法计算的PTV D95和平均剂量值对于具有最小场尺寸(3-5厘米)的“肺岛”肿瘤最低。另一方面,用最大场宽度(7-10cm)处理的肺中心肿瘤注意到最小差异。在所有地点中,剂量分布差异与肺岛肿瘤的肿瘤大小最强烈相关。对于大多数情况而言,卷积/叠加和MC算法非常一致。使用EPL-1D算法计算的平均肺剂量(MLD)值与其他算法(相关系数= 0.99)高度相关。对于基于模型的(Conv / Superthosition和MC)与模型的算法预测更大的低算法,对小肺/叠加和MC)与基于模型的算法(Conv / SuperThiposition and MC)VS为基于模型的算法而降低MLD值为〜10%剂量在肺部蔓延。本研究表明,对于肺SABR规划,应避免铅笔梁算法。对于最具挑战性的病例,可以保证肺组织(肺岛式)完全包围的小肿瘤,可以保证基于蒙特卡罗的算法。

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