Suppr超能文献

使用模拟动态 PET 和药代动力学模型预测 PRRT 中的时间积分活度系数。

Prediction of time-integrated activity coefficients in PRRT using simulated dynamic PET and a pharmacokinetic model.

机构信息

Medical Radiation Physics/Radiation Protection, Universitätsmedizin Mannheim, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany; Department of Electrical Engineering, Universitas Padjadjaran, Bandung, Indonesia.

Medical Radiation Physics/Radiation Protection, Universitätsmedizin Mannheim, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany; Department of Radiation Oncology, Universitätsmedizin Mannheim, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany.

出版信息

Phys Med. 2017 Oct;42:298-304. doi: 10.1016/j.ejmp.2017.06.024. Epub 2017 Jul 22.

Abstract

PURPOSE

To investigate the accuracy of predicted time-integrated activity coefficients (TIACs) in peptide-receptor radionuclide therapy (PRRT) using simulated dynamic PET data and a physiologically based pharmacokinetic (PBPK) model.

METHODS

PBPK parameters were estimated using biokinetic data of 15 patients after injection of (152±15)MBq of In-DTPAOC (total peptide amount (5.78±0.25)nmol). True mathematical phantoms of patients (MPPs) were the PBPK model with the estimated parameters. Dynamic PET measurements were simulated as being done after bolus injection of 150MBq Ga-DOTATATE using the true MPPs. Dynamic PET scans around 35min p.i. (P), 4h p.i. (P) and the combination of P and P (P) were simulated. Each measurement was simulated with four frames of 5min each and 2 bed positions. PBPK parameters were fitted to the PET data to derive the PET-predicted MPPs. Therapy was simulated assuming an infusion of 5.1GBq of Y-DOTATATE over 30min in both true and PET-predicted MPPs. TIACs of simulated therapy were calculated, true MPPs (true TIACs) and predicted MPPs (predicted TIACs) followed by the calculation of variabilities v.

RESULTS

For P and P the population variabilities of kidneys, liver and spleen were acceptable (v<10%). For the tumours and the remainders, the values were large (up to 25%). For P, population variabilities for all organs including the remainder further improved, except that of the tumour (v>10%).

CONCLUSION

Treatment planning of PRRT based on dynamic PET data seems possible for the kidneys, liver and spleen using a PBPK model and patient specific information.

摘要

目的

使用模拟动态 PET 数据和基于生理的药代动力学 (PBPK) 模型,研究肽受体放射性核素治疗 (PRRT) 中预测的时间积分活度系数 (TIAC) 的准确性。

方法

使用 15 名患者注射(152±15)MBq In-DTPAOC(总肽量(5.78±0.25)nmol)后的生物动力学数据估计 PBPK 参数。真实的患者数学模型(MPPs)是使用估计参数的 PBPK 模型。使用真实的 MPPs 模拟了在 bolus 注射 150MBq Ga-DOTATATE 后进行的动态 PET 测量。模拟了注射后 35min p.i.(P)、4h p.i.(P)和 P 和 P 的组合(P)的动态 PET 扫描。每个测量都模拟了 4 个 5 分钟的帧和 2 个床位位置。将 PBPK 参数拟合到 PET 数据中,以得出 PET 预测的 MPPs。假设在真实和 PET 预测的 MPPs 中输注 5.1GBq 的 Y-DOTATATE 30 分钟,模拟治疗。计算模拟治疗的 TIAC,真实 MPPs(真实 TIAC)和预测 MPPs(预测 TIAC),然后计算变异性 v。

结果

对于 P 和 P,肾脏、肝脏和脾脏的群体变异性可接受(v<10%)。对于肿瘤和剩余物,值较大(高达 25%)。对于 P,所有器官(包括剩余物)的群体变异性进一步改善,除了肿瘤(v>10%)。

结论

基于动态 PET 数据的 PRRT 治疗计划似乎可以使用 PBPK 模型和患者特定信息对肾脏、肝脏和脾脏进行。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验