Y-90 SIRT: evaluation of TCP variation across dosimetric models.
Y-90 SIRT: evaluation of TCP variation across dosimetric models.
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DOI：
10.1186/s40658-021-00391-6
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发表时间：
2021-06-10
期刊：
影响因子：
4
通讯作者：
Mikell JK
中科院分区：
文献类型：
--
作者：
Van BJ;Dewaraja YK;Sangogo ML;Mikell JK
在实施选择性内放射治疗(SIRT)作为肝脏恶性肿瘤的一种可行的治疗选择方面已经取得了很大进展。然而，仍有许多需要改进的备选办法，以计算需要管理的活动量。为了朝着这一目标取得进展，本研究考察了在吸收剂量处方方法学不同的情况下，通过肿瘤控制概率(TCP)预测肝肿瘤的生物学结果与通过正常组织并发症概率(NTCP)预测肝肿瘤实质之间的关系。应用99mTC-MAA SPECT/CT和90YPET/CT对35例肝细胞癌或转移性肝病患者的39个玻璃微球治疗进行了分析。与单室(标准)模型和多室(分区)剂量学模型相对应的预测生物学结果，使用我们之前推导的TCP剂量-反应曲线，在80-150 的剂量范围内进行了比较，该剂量范围为玻璃微球包装插页中推荐的规定吸收剂量。在MAA SPECT/CT上进行回溯性计划剂量测定；使用因选择吸收剂量水平和剂量测定模型(标准或分区)而导致的计划输注活动的变化来衡量90YPET/CT报告的吸收剂量，包括肝实质和病变(N=120)>； 2 ml。使用先前提出的NTP模型，探讨了处方剂量从12 0 Gy1 2 0 Gy1 2到1 5%NTCP1 5%处方对正常肝脏的改变。当处方剂量在80-150 的剂量范围内变化时，分区模型与标准模型相比的平均Tcp预测从减少13%到增加32%不等。在比较标准模型和分割模型的吸收剂量处方范围的参数化图中，确定了肿瘤的等效吸收剂量线。当将分区模型与标准模型进行比较时，对于最常用的处方选项，在每个病变基础上的TCP预测从减少26%到增加81%不等。NTCP模型只适用于一小部分患者，因为在大多数情况下，肝脏的体积分数很小。我们对患者影像数据的回顾分析表明，处方剂量的选择和开出哪种模式可能会导致平均肿瘤疗效的广泛差异。在临床上寻求改善患者护理时，应将生物反应数据作为一个因素包括在内。当医生过渡到新的处方方法时，使用参数化图表，如这里所示，将有助于指导医生。网上版载有补充材料，可在10.1186/s40658-021-00391-6查阅。
Much progress has been made in implementing selective internal radiation therapy (SIRT) as a viable treatment option for hepatic malignancies. However, there is still much need for improved options for calculating the amount of activity to be administered. To make advances towards this goal, this study examines the relationship between predicted biological outcomes of liver tumors via tumor control probabilities (TCP) and parenchyma via normal tissue complication probabilities (NTCP) given variations in absorbed dose prescription methodologies. Thirty-nine glass microsphere treatments in 35 patients with hepatocellular carcinoma or metastatic liver disease were analyzed using 99mTc-MAA SPECT/CT and 90Y PET/CT scans. Predicted biological outcomes corresponding to the single compartment (standard) model and multi-compartment (partition) dosimetry model were compared using our previously derived TCP dose-response curves over a range of 80–150 Gy prescribed absorbed dose to the perfused volume, recommended in the package insert for glass microspheres. Retrospective planning dosimetry was performed on the MAA SPECT/CT; changes from the planned infused activity due to selection of absorbed dose level and dosimetry model (standard or partition) were used to scale absorbed doses reported from 90Y PET/CT including liver parenchyma and lesions (N = 120) > 2 ml. A parameterized charting system was developed across all potential prescription options to enable a clear relationship between standard prescription vs. the partition model-based prescription. Using a previously proposed NTCP model, the change in prescribed dose from a standard model prescription of 120 Gy to the perfused volume to a 15% NTCP prescription to the normal liver was explored. Average TCP predictions for the partition model compared with the standard model varied from a 13% decrease to a 32% increase when the prescribed dose was varied across the range of 80–150 Gy. In the parametrized chart comparing absorbed dose prescription ranges across the standard model and partition models, a line of equivalent absorbed dose to a tumor was identified. TCP predictions on a per lesion basis varied between a 26% decrease and a 81% increase for the most commonly chosen prescription options when comparing the partition model with the standard model. NTCP model was only applicable to a subset of patients because of the small volume fraction of the liver that was targeted in most cases. Our retrospective analysis of patient imaging data shows that the choice of prescribed dose and which model to prescribe potentially contribute to a wide variation in average tumor efficacy. Biological response data should be included as one factor when looking to improve patient care in the clinic. The use of parameterized charting, such as presented here, will help direct physicians when transitioning to newer prescription methods. The online version contains supplementary material available at 10.1186/s40658-021-00391-6.
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DOI：
10.1007/s00259-019-04340-5
发表时间：
2019-07-01
影响因子：
9.1
作者：
Salem, Riad;Padia, Siddharth A.;Garin, Etienne
通讯作者：
Garin, Etienne
影响因子：
9.3
作者：
Kafrouni, Marilyne;Allimant, Carole;Ben Bouallegue, Faycal
通讯作者：
Ben Bouallegue, Faycal
影响因子：
35.7
作者：
Garin, Etienne;Tselikas, Lambros;Rolland, Yan
通讯作者：
Rolland, Yan
影响因子：
9.3
作者：
Garin, Etienne;Lenoir, Laurence;Boucher, E.
通讯作者：
Boucher, E.
影响因子：
9.3
作者：
Lam, Marnix G. E. H.;Goris, Michael L.;Sze, Daniel Y.
通讯作者：
Sze, Daniel Y.