Abstract
BackgroundKinetic parameters estimated with dynamic 18F-FDG PET/CT can help to characterize hepatocellular carcinoma (HCC). We aim to evaluate the feasibility of the gravitational search algorithm (GSA) for kinetic parameter estimation and to propose a dynamic chaotic gravitational search algorithm (DCGSA) to enhance parameter estimation.MethodsFive-minute dynamic PET/CT data of 20 HCCs were prospectively enrolled, and the kinetic parameters k1 ~ k4 and the hepatic arterial perfusion index (HPI) were estimated with a dual-input three-compartment model based on nonlinear least squares (NLLS), GSA and DCGSA.ResultsThe results showed that there were significant differences between the HCCs and background liver tissues for k1, k4 and the HPI of NLLS; k1, k3, k4 and the HPI of GSA; and k1, k2, k3, k4 and the HPI of DCGSA. DCGSA had a higher diagnostic performance for k3 than NLLS and GSA.ConclusionsGSA enables accurate estimation of the kinetic parameters of dynamic PET/CT in the diagnosis of HCC, and DCGSA can enhance the diagnostic performance.
Highlights
Kinetic parameters estimated with dynamic 18F-FDG Positron emission tomography (PET)/computed tomography (CT) can help to characterize hepatocellular carcinoma (HCC)
This study evaluated the role of the compartmental parameters of 5-min 18F-FDG PET/CT estimated by nonlinear least squares (NLLS), gravitational search algorithm (GSA) and dynamic chaotic gravitational search algorithm (DCGSA) for distinguishing HCC from background liver tissue
NLLS yielded a significant difference in HCCs due to its higher k1, k4 and the hepatic arterial perfusion index (HPI) than those in background liver tissue (P = 0.019, P < 0.001, and P < 0.001, respectively), and k2 and k3 did not show a significant difference between HCCs and background liver tissue (P = 0.067 and P = 0.411, respectively)
Summary
Kinetic parameters estimated with dynamic 18F-FDG PET/CT can help to characterize hepatocellular carcinoma (HCC). Conventional medical imaging techniques such as computed tomography (CT) and magnetic resonance imaging (MRI) are often used for the initial examination in clinical practice. They can only generate structural images and lack tumor metabolic information [3]. Dynamic PET/CT imaging can track the distribution of 18F-FDG in tissues and derive some kinetic parameters that accurately describe the cellular metabolic processes of 18F-FDG to enhance diagnosis and therapy in various diseases, and compartmental modeling is routinely applied to estimate kinetic parameters [6, 7]. Wang et al [9] found that dynamic 18F-FDG PET with optimization-derived blood input
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