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Ga-PSMA-11的动力学建模及原发性前列腺癌患者定量简化方法的验证

Kinetic modeling of Ga-PSMA-11 and validation of simplified methods for quantification in primary prostate cancer patients.

作者信息

Ringheim Anna, Campos Neto Guilherme de Carvalho, Anazodo Udunna, Cui Lumeng, da Cunha Marcelo Livorsi, Vitor Taise, Martins Karine Minaif, Miranda Ana Cláudia Camargo, de Barboza Marycel Figols, Fuscaldi Leonardo Lima, Lemos Gustavo Caserta, Colombo Junior José Roberto, Baroni Ronaldo Hueb

机构信息

Hospital Israelita Albert Einstein, Avenida Albert Einstein 627/701, Morumbi, Sao Paulo, SP, CEP 05652-900, Brazil.

Lawson Health Research Institute, St Joseph's Health Care, 268 Grosvenor Street, London, Ontario, N6A 4V2, Canada.

出版信息

EJNMMI Res. 2020 Feb 24;10(1):12. doi: 10.1186/s13550-020-0594-6.

DOI:10.1186/s13550-020-0594-6
PMID:32140850
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7058750/
Abstract

BACKGROUND

The positron emission tomography (PET) ligand Ga-Glu-urea-Lys(Ahx)-HBED-CC (Ga-PSMA-11) targets the prostate-specific membrane antigen (PSMA), upregulated in prostate cancer cells. Although Ga-PSMA-11 PET is widely used in research and clinical practice, full kinetic modeling has not yet been reported nor have simplified methods for quantification been validated. The aims of our study were to quantify Ga-PSMA-11 uptake in primary prostate cancer patients using compartmental modeling with arterial blood sampling and to validate the use of standardized uptake values (SUV) and image-derived blood for quantification.

RESULTS

Fifteen patients with histologically proven primary prostate cancer underwent a 60-min dynamic Ga-PSMA-11 PET scan of the pelvis with axial T1 Dixon, T2, and diffusion-weighted magnetic resonance (MR) images acquired simultaneously. Time-activity curves were derived from volumes of interest in lesions, normal prostate, and muscle, and mean SUV calculated. In total, 18 positive lesions were identified on both PET and MR. Arterial blood activity was measured by automatic arterial blood sampling and manual blood samples were collected for plasma-to-blood ratio correction and for metabolite analysis. The analysis showed that Ga-PSMA-11 was stable in vivo. Based on the Akaike information criterion, Ga-PSMA-11 kinetics were best described by an irreversible two-tissue compartment model. The rate constants K and k and the net influx rate constants K were all significantly higher in lesions compared to normal tissue (p < 0.05). K derived using image-derived blood from an MR-guided method showed excellent agreement with K derived using arterial blood sampling (intraclass correlation coefficient = 0.99). SUV correlated significantly with K with the strongest correlation of scan time-window 30-45 min (rho 0.95, p < 0.001). Both K and SUV correlated significantly with serum prostate specific antigen (PSA) level and PSA density.

CONCLUSIONS

Ga-PSMA-11 kinetics can be described by an irreversible two-tissue compartment model. An MR-guided method for image-derived blood provides a non-invasive alternative to blood sampling for kinetic modeling studies. SUV showed strong correlation with K and can be used in routine clinical settings to quantify Ga-PSMA-11 uptake.

摘要

背景

正电子发射断层扫描(PET)配体镓-谷氨酸-尿素-赖氨酸(己二酸)-HBED-CC(镓-PSMA-11)靶向前列腺特异性膜抗原(PSMA),该抗原在前列腺癌细胞中上调。尽管镓-PSMA-11 PET已广泛应用于研究和临床实践,但尚未有完整的动力学模型报道,也未验证简化的定量方法。我们研究的目的是使用动脉血采样的房室模型对原发性前列腺癌患者的镓-PSMA-11摄取进行定量,并验证标准化摄取值(SUV)和图像衍生血用于定量的有效性。

结果

15例经组织学证实的原发性前列腺癌患者接受了60分钟的骨盆动态镓-PSMA-11 PET扫描,同时采集轴向T1 Dixon、T2和扩散加权磁共振(MR)图像。从病变、正常前列腺和肌肉的感兴趣区域得出时间-活性曲线,并计算平均SUV。PET和MR共识别出18个阳性病变。通过自动动脉血采样测量动脉血活性,并采集手动血样用于血浆与血液比率校正和代谢物分析。分析表明镓-PSMA-11在体内稳定。基于赤池信息准则,镓-PSMA-11动力学最好用不可逆双组织房室模型描述。与正常组织相比,病变中的速率常数K和k以及净流入速率常数K均显著更高(p < 0.05)。使用MR引导方法从图像衍生血得出的K与使用动脉血采样得出的K显示出极好的一致性(组内相关系数 = 0.99)。SUV与K显著相关,在扫描时间窗30 - 45分钟时相关性最强(rho 0.95,p < 0.001)。K和SUV均与血清前列腺特异性抗原(PSA)水平和PSA密度显著相关。

结论

镓-PSMA-11动力学可用不可逆双组织房室模型描述。用于图像衍生血的MR引导方法为动力学建模研究的血样采集提供了一种非侵入性替代方法。SUV与K显示出强相关性,可用于常规临床环境中定量镓-PSMA-11摄取。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5b07/7058750/5ac0744f7180/13550_2020_594_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5b07/7058750/b46cb0df10c0/13550_2020_594_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5b07/7058750/e06daf0720c5/13550_2020_594_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5b07/7058750/25e1db2a7e3b/13550_2020_594_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5b07/7058750/d3a3575ad804/13550_2020_594_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5b07/7058750/5ac0744f7180/13550_2020_594_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5b07/7058750/b46cb0df10c0/13550_2020_594_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5b07/7058750/e06daf0720c5/13550_2020_594_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5b07/7058750/25e1db2a7e3b/13550_2020_594_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5b07/7058750/d3a3575ad804/13550_2020_594_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5b07/7058750/5ac0744f7180/13550_2020_594_Fig5_HTML.jpg

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