Dynamic fluorine-18 fluorodeoxyglucose PET for evaluating different-sized metastatic lymph nodes in patients with non-small cell lung cancers

Read full text
Authors: Bin Huang, Fen Du, Xinyu Yang, Xieraili Wumener, L W Guo, Tao Sun, Ying Liang
Cited by: 0

Abstract

Background: For patients with non-small cell lung cancer (NSCLC), accurate and detailed lymph node (LN) staging is crucial for treatment planning and prognosis. However, reliably distinguishing metastatic from nonmetastatic LNs remains a clinical challenge, especially in small-sized LNs. A retrospective study was conducted to evaluate the ability of dynamic fluorine-18 fluorodeoxyglucose (F-18 FDG) positron emission tomography (PET), specifically the metabolic parameter of net influx rate (Ki), in distinguishing metastatic from and nonmetastatic LNs of different sizes in patients with NSCLC. Methods: In the study, 366 patients with lung nodules or masses detected on chest computed tomography (CT) scan underwent dynamic chest F-18 FDG PET/CT and static whole-body F-18 FDG PET/CT imaging after providing informed consent. A retrospective collection of 369 LNs in 98 patients with NSCLC was pathologically confirmed. CT features [density, maximum short diameter (Dmax), ratio of LN long diameter to short diameter (L/S), and ratio of LN density to muscle density (DR)] and static PET features [maximum standardized uptake value (SUVmax) and ratio of LN SUVmax to mediastinal blood pool SUVmax (SUVR)] of the LNs were measured. The Ki was also obtained through the application of an irreversible two-tissue compartment model. CT features, PET features, and Ki were compared between the metastatic and nonmetastatic LNs via the Wilcoxon rank-sum test. Receiver operating characteristic (ROC) analysis was performed for each parameter. Differences in area under the curve (AUC) were assessed via the DeLong test. Differences in sensitivity and specificity at a given threshold were compared using χ2 test. P

Fields of Study & Subjects

Lung Cancer Diagnosis and Treatment Medical Imaging Techniques and Applications Radiomics and Machine Learning in Medical Imaging

Publication details

Journal / source
Quantitative Imaging in Medicine and Surgery
Volume
16
Issue
3
Pages
249–249
Publication date
13 February 2026
Language
EN