Kinetic Modeling and Parametric Imaging of 13N-NH3 in Treatment-Naïve Lung Cancer.

IF 4.5 2区 医学 Q2 MEDICINE, RESEARCH & EXPERIMENTAL
Hui Yuan, Fanghu Wang, Yang Chen, Xiaoqiang Pan, Qing Zhang, Tao Sun, Lei Jiang
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引用次数: 0

Abstract

13N-NH3 PET imaging provides insights into tumor perfusion and metabolism, hence potentially valuable in oncological diagnosis, staging, and prognosis. However, its in vivo kinetic characteristics and the optimal protocol for kinetic modeling and parametric imaging remain unclear. This study aims to elucidate the kinetic features of 13N-NH3 in lung cancer and its clinical value. Nine lung cancer surgical candidates were prospectively incorporated. Using total-body PET/CT scanner, 35 min 13N-NH3 acquisitions were conducted immediately postinjection. Subsequently, routine 5 min 18F-FDG acquisitions were made. 13N-NH3 PET data were reconstructed into dynamic image series. Tumor lesions and normal organs were segmented using nonthreshold dependent automatic or semiautomatic tools. Reversible and irreversible 2-tissue compartment models (2TC vs 2TiC) using image-derived input functions (IDIFs) with population-based metabolite correction were adopted for parametric modeling. Akaike Information Criterion (AIC) was calculated for model selection. Parametric images were produced with the optimal model for lesions. A total of 9 patients presented with 9 primary lung tumors and 17 histologically confirmed lymphadenopathy. All primary lung tumors and regional lymph node metastases were detectable using both 13N-NH3 and 18F-FDG imaging. Primary lung tumors, regional lymphadenopathy, and lung backgrounds demonstrated smaller AIC using 2TC models with pulmonary artery as IDIF, while other organs favored either 2TC or 2TiC models with the descending aorta as IDIF. Lesion-to-background lung ratios reached around 2.218-3.407 for primary lung tumors and 1.932-2.537 for regional lymphadenopathy 10-20 min postinjection of 13N-NH3. Vt images derived from 2TC modeling showed better lesion-to-background lung ratios (4.511 ± 2.955 for primary tumor, and 2.991 ± 2.152 for regional lymphadenopathy). For 13N-NH3 imaging in lung cancer, a static image can be acquired at 10-20 min postinjection for clinical diagnosis. The reversible 2TC model is preferred over 2TiC, and the Vt image is preferred over other parametric images in terms of lesion contrast.

13N-NH3在Treatment-Naïve肺癌中的动力学建模和参数成像。
13N-NH3 PET成像提供了对肿瘤灌注和代谢的深入了解,因此在肿瘤诊断、分期和预后方面具有潜在的价值。然而,其体内动力学特性和动力学建模和参数化成像的最佳方案仍不清楚。本研究旨在阐明13N-NH3在肺癌中的动力学特征及其临床价值。前瞻性纳入9例肺癌手术候选者。使用全身PET/CT扫描仪,在注射后立即进行35分钟的13N-NH3采集。随后,进行常规5分钟18F-FDG采集。将13N-NH3 PET数据重构为动态图像序列。使用无阈值依赖的自动或半自动工具对肿瘤病变和正常器官进行分割。采用图像衍生输入函数(idif)和基于群体的代谢物校正的可逆和不可逆2组织室模型(2TC vs 2TiC)进行参数化建模。计算赤池信息准则(Akaike Information Criterion, AIC)进行模型选择。参数化图像是用最优的病灶模型生成的。9例患者共出现9例原发性肺肿瘤和17例组织学证实的淋巴结病。13N-NH3和18F-FDG成像均可检测到所有原发性肺肿瘤和区域淋巴结转移。原发性肺肿瘤、区域性淋巴结病变和肺背景显示,以肺动脉为IDIF的2TC模型AIC较小,而其他器官则倾向于以降主动脉为IDIF的2TC或2TiC模型。13N-NH3注射后10-20分钟,原发性肺肿瘤的肺本底比值约为2.218-3.407,局部淋巴结病变的肺本底比值约为1.932-2.537。由2TC模型获得的Vt图像显示出更好的病灶与背景肺比值(原发肿瘤为4.511±2.955,局部淋巴结病为2.991±2.152)。肺癌13N-NH3成像,可在注射后10- 20min获得静态图像,用于临床诊断。可逆的2TC模型优于2TiC模型,在病变对比方面,Vt图像优于其他参数图像。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Molecular Pharmaceutics
Molecular Pharmaceutics 医学-药学
CiteScore
8.00
自引率
6.10%
发文量
391
审稿时长
2 months
期刊介绍: Molecular Pharmaceutics publishes the results of original research that contributes significantly to the molecular mechanistic understanding of drug delivery and drug delivery systems. The journal encourages contributions describing research at the interface of drug discovery and drug development. Scientific areas within the scope of the journal include physical and pharmaceutical chemistry, biochemistry and biophysics, molecular and cellular biology, and polymer and materials science as they relate to drug and drug delivery system efficacy. Mechanistic Drug Delivery and Drug Targeting research on modulating activity and efficacy of a drug or drug product is within the scope of Molecular Pharmaceutics. Theoretical and experimental peer-reviewed research articles, communications, reviews, and perspectives are welcomed.
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