Technical note: Impact of dose voxel kernel (DVK) values on dosimetry estimates in 177 Lu and 90 Y radiopharmaceutical therapy (RPT) applications.
Technical note: Impact of dose voxel kernel (DVK) values on dosimetry estimates in 177 Lu and 90 Y radiopharmaceutical therapy (RPT) applications.
复制标题
技术说明：剂量体素核 (DVK) 值对 177 Lu 和 90 Y 放射性药物治疗 (RPT) 应用中剂量测定估计的影响。
DOI：
10.1002/mp.16729
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发表时间：
2024
期刊：
影响因子：
3.8
通讯作者：
Cremonesi,Marta
中科院分区：
文献类型：
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作者：
Danieli,Rachele;Pistone,Daniele;Tranel,Jonathan;Botta,Francesca;Uribe-Munoz,Carlos;Raspanti,Davide;Salvat,Francesc;Wilderman,ScottJ;Bardiès,Manuel;Amato,Ernesto;Dewaraja,YuniK;Cremonesi,Marta
背景放射性药物治疗(RPT)是一种日益被采用的治疗癌症的方法。有证据表明，根据剂量学优化治疗可以改善结果。然而，临床剂量学工作流程的标准化仍然是一个主要的努力。在众多的可变性来源中，使用不同剂量的体素核(DVK)通过与时间积分活动(TIA)分布卷积来生成吸收剂量(AD)图的影响尚未被系统地研究。目的本研究旨在比较DVK，并评估将同一TIA图与不同DVK卷积时ADS的差异。方法从商业/自由软件中最常用的或已有文献中介绍的DVK中选择3×3×3×3 mm 3采样的DVK。对于11×11×11×11矩阵中的每个体素，计算变异系数(CoV)和最大值与最小值的百分比差(%最大差)。每次衰变的总吸收剂量(SUM)，由每个核中所有体素值的总和计算，也进行了比较。两名接受177Lu-DOTATATE治疗的患者使用了公开可用的定量SPECT图像，两名接受90Y微球治疗的患者使用了PET图像，包括危险器官(177Lu：肾脏；90Y：肝脏和健康肝脏)和肿瘤的节段。每个患者使用不同的DVK、相同的TIA图和相同的剂量卷积软件工具计算感兴趣体积(VOI)的平均AD，从而集中于DVK的影响。对于每个VOI，计算最大值和最小值之间的平均AD的最大百分比差值。结果177Lu DVK的归一化坐标[0，0，0]、[0，1，0]和[0，1，1]的体素的CoV(%最大差值)分别为5%(21%)、9%(35%)和10%(46%)。对于90Y的情况，这些值分别为2%(9%)、4%(14%)和4%(16%)。总和的最大差值为9%(33%)，90Y为4%(15%)。在177Lu-DOTATE和90Y-微球患者中，肿瘤和器官AD的平均变异性分别高达19%和15%。结论本研究显示出相当大的AD变异性，完全是由于使用不同的DVK。科学界的共同努力将有助于减少这些差异，加强RPT中AD计算的一致性。
BackgroundRadiopharmaceutical therapy (RPT) is an increasingly adopted modality for treating cancer. There is evidence that the optimization of the treatment based on dosimetry can improve outcomes. However, standardization of the clinical dosimetry workflow still represents a major effort. Among the many sources of variability, the impact of using different Dose Voxel Kernels (DVKs) to generate absorbed dose (AD) maps by convolution with the time‐integrated activity (TIA) distribution has not been systematically investigated.PurposeThis study aims to compare DVKs and assess the differences in the ADs when convolving the same TIA map with different DVKs.MethodsDVKs of 3 × 3 × 3 mm3sampling—nine for177Lu, nine for90Y—were selected from those most used in commercial/free software or presented in prior publications. For each voxel within a 11 × 11 × 11 matrix, the coefficient of variation (CoV) and the percentage difference between maximum and minimum values (% maximum difference) were calculated. The total absorbed dose per decay (SUM), calculated as the sum of all the voxel values in each kernel, was also compared. Publicly available quantitative SPECT images for two patients treated with177Lu‐DOTATATE and PET images for two patients treated with90Y‐microspheres were used, including organs at risk (177Lu: kidneys;90Y: liver and healthy liver) and tumors’ segmentations. For each patient, the mean AD to the volumes of interest (VOIs) was calculated using the different DVKs, the same TIA map and the same software tool for dose convolution, thereby focusing on the DVK impact. For each VOI, the % maximum difference of the mean AD between maximum and minimum values was computed.ResultsThe CoV (% maximum difference) in voxels of normalized coordinates [0,0,0], [0,1,0], and [0,1,1] were 5%(21%), 9%(35%), and 10%(46%) for the177Lu DVKs. For the case of90Y, these values were 2%(9%), 4%(14%), and 4%(16%). The CoV (% maximum difference) for SUM was 9%(33%) for177Lu, and 4%(15%) for90Y. The variability of the mean tumor and organ AD was up to 19% and 15% in177Lu‐DOTATATE and90Y‐microspheres patients, respectively.ConclusionsThis study showed a considerable AD variability due exclusively to the use of different DVKs. A concerted effort by the scientific community would contribute to decrease these discrepancies, strengthening the consistency of AD calculation in RPT.