Yuan Hui, Wang Fanghu, Chen Yang, Pan Xiaoqiang, Zhang Qing, Sun Tao, Jiang Lei
PET Center, Department of Nuclear Medicine, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, 106 Zhongshan Er Road, Guangzhou 510080, China.
Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, 1068 Xueyuan Avenue, Shenzhen 518055, China.
Mol Pharm. 2025 Jul 7;22(7):4314-4320. doi: 10.1021/acs.molpharmaceut.5c00602. Epub 2025 Jun 13.
N-NH PET imaging provides insights into tumor perfusion and metabolism, hence potentially valuable in oncological diagnosis, staging, and prognosis. However, its kinetic characteristics and the optimal protocol for kinetic modeling and parametric imaging remain unclear. This study aims to elucidate the kinetic features of N-NH in lung cancer and its clinical value. Nine lung cancer surgical candidates were prospectively incorporated. Using total-body PET/CT scanner, 35 min N-NH acquisitions were conducted immediately postinjection. Subsequently, routine 5 min F-FDG acquisitions were made. N-NH 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 N-NH and F-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 N-NH. 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 N-NH 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.
N-NH PET成像可提供有关肿瘤灌注和代谢的信息，因此在肿瘤学诊断、分期和预后评估方面可能具有重要价值。然而，其动力学特征以及动力学建模和参数成像的最佳方案仍不明确。本研究旨在阐明N-NH在肺癌中的动力学特征及其临床价值。前瞻性纳入了9名肺癌手术候选患者。使用全身PET/CT扫描仪，在注射后立即进行35分钟的N-NH采集。随后，进行常规的5分钟F-FDG采集。将N-NH PET数据重建为动态图像序列。使用非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。采用基于人群代谢物校正的图像衍生输入函数（IDIF）的可逆和不可逆双组织室模型（2TC与2TiC）进行参数建模。计算赤池信息准则（AIC）以进行模型选择。使用针对病变的最佳模型生成参数图像。共有9例患者出现9个原发性肺肿瘤和17个经组织学证实的淋巴结病变。使用N-NH和F-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、区域淋巴结病变和肺背景在使用以肺动脉为IDIF的2TC模型时显示出较小的AIC，而其他器官则倾向于使用以降主动脉为IDIF的2TC或2TiC模型。在注射N-NH后10-20分钟，原发性肺肿瘤的病变与肺背景比值达到约2.218-3.407，区域淋巴结病变的比值为1.932-2.537。源自2TC建模的Vt图像显示出更好的病变与肺背景比值（原发性肿瘤为4.511±2.955，区域淋巴结病变为2.991±2.152）。对于肺癌的N-NH成像，可在注射后10-20分钟采集静态图像用于临床诊断。在病变对比度方面，可逆2TC模型优于2TiC模型，Vt图像优于其他参数图像。
