Characteristics of whole-body dynamic 18F-FDG PET/CT Patlak multi-parametric imaging in lung cancer and the influence of different delineation methods on quantitative parameters

Liya Zhang; Jinzhou Zhang; Jingxuan Miao; Gan Zhu; Xiaoyu Su; Hui Wang

doi:10.21037/qims-23-862

Characteristics of whole-body dynamic ¹⁸F-FDG PET/CT Patlak multi-parametric imaging in lung cancer and the influence of different delineation methods on quantitative parameters

Quant Imaging Med Surg. 2024 Jan 3;14(1):291-304. doi: 10.21037/qims-23-862. Epub 2024 Jan 2.

Authors

Liya Zhang^#¹, Jinzhou Zhang^#¹, Jingxuan Miao¹, Gan Zhu¹, Xiaoyu Su¹, Hui Wang¹

Affiliation

¹ Department of Nuclear Medicine, The First Affiliated Hospital of Anhui Medical University, Hefei, China.

^# Contributed equally.

Abstract

Background: Dynamic course of flourine-18 fluorodeoxyglucose positron emission tomography/computed tomography (¹⁸F-FDG PET/CT) Patlak muti-parametric imaging spatial distribution in the targeted tissues may reveal highly useful clinical information about the tissue's metabolic properties. The characteristics of the Patlak multi-parametric imaging in lung cancer and the influence of different delineation methods on quantitative parameters may provide reference for the clinical application of this new technology.

Methods: A total of 27 patients with pathologically diagnosed lung cancer underwent whole-body dynamic ¹⁸F-FDG PET/CT examination before treatment. Parametric images of metabolic rate of FDG (MR_FDG) and Patlak intercept (or distribution volume; DV) were generated using Patlak reconstruction. The values of primary lung cancer lesions, target-to-background ratio (TBR), and contrast-to-noise ratio (CNR) were investigated using contour delineation and boundary delineation. Statistical analysis was performed to analyze the relationship between multi-parametric images and clinicopathological features, and to compare the effects of contour delineation and boundary delineation on quantitative parameters.

Results: MR_FDG images showed higher TBR and CNR than did standardized uptake value (SUV) images. There were significant differences in MR_FDG-max, MR_FDG-mean, and MR_FDG-peak among groups with different tumor diameters and pathology types (P<0.05). Moreover, the metabolic parameters of MR_FDG were higher in patients with tumor diameters ≥3 cm and squamous carcinoma. The differences of the maximum and peak values of MR_FDG and DV were not statistically significant in the different outlining method subgroups (all P>0.05). However, the difference of the mean values of MR_FDG and DV were statistically significant in the different outline method groupings (all P<0.05).

Conclusions: Dynamic ¹⁸F-FDG PET/CT Patlak multi-parametric imaging can obtain quantitative values for lung cancer with high TBR and CNR. Moreover, the multi-parameters are various from different pathology types to tumor size. Different delineation methods have a greater influence on the mean value of quantitative parameters.

Keywords: Dynamic positron emission tomography (dynamic PET); Patlak; flourine-18 fluorodeoxyglucose (18F-FDG); lung cancer; whole-body parametric imaging.