Kinetic 18F-FDG PET/CT imaging of hepatocellular carcinoma: a dual input four-compartment model.

BACKGROUND: The endoplasmic reticulum plays an important role in glucose metabolism and has not been explored in the kinetic estimation of hepatocellular carcinoma (HCC) via 18F-fluoro-2-deoxy-D-glucose PET/CT. METHODS: A dual-input four-compartment (4C) model, regarding endoplasmic reticulum was preliminarily used for kinetic estimation to differentiate 28 tumours from background liver tissue from 24 patients with HCC. Moreover, parameter images of the 4C model were generated from one patient with negative findings on conventional metabolic PET/CT. RESULTS: Compared to the dual-input three-compartment (3C) model, the 4C model has better fitting quality, a close transport rate constant (K1) and a dephosphorylation rate constant (k6/k4), and a different removal rate constant (k2) and phosphorylation rate constant (k3) in HCC and background liver tissue. The K1, k2, k3, and hepatic arterial perfusion index (HPI) from the 4C model and k3, HPI, and volume fraction of blood (Vb) from the 3C model were significantly different between HCC and background liver tissues (all P < 0.05). Meanwhile, the 4C model yielded additional kinetic parameters for differentiating HCC. The diagnostic performance of the top ten genes from the most to least common was HPI(4C), Vb(3C), HPI(3C), SUVmax, k5(4C), k3(3C), k2(4C), v(4C), K1(4C) and Vb(4C). Moreover, a patient who showed negative findings on conventional metabolic PET/CT had positive parameter images in the 4C model. CONCLUSIONS: The 4C model with the endoplasmic reticulum performed better than the 3C model and produced additional useful parameters in kinetic estimation for differentiating HCC from background liver tissue.

Short-term PET-derived kinetic estimation for the diagnosis of hepatocellular carcinoma: a combination of the maximum-slope method and dual-input three-compartment model.

BACKGROUND: Kinetic estimation provides fitted parameters related to blood flow perfusion and fluorine-18-fluorodeoxyglucose (18F-FDG) transport and intracellular metabolism to characterize hepatocellular carcinoma (HCC) but usually requires 60 min or more for dynamic PET, which is time-consuming and impractical in a busy clinical setting and has poor patient tolerance. METHODS: This study preliminarily evaluated the equivalence of liver kinetic estimation between short-term (5-min dynamic data supplemented with 1-min static data at 60 min postinjection) and fully 60-min dynamic protocols and whether short-term 18F-FDG PET-derived kinetic parameters using a three-compartment model can be used to discriminate HCC from the background liver tissue. Then, we proposed a combined model, a combination of the maximum-slope method and a three-compartment model, to improve kinetic estimation. RESULTS: There is a strong correlation between the kinetic parameters K1 ~ k3, HPI and [Formula: see text] in the short-term and fully dynamic protocols. With the three-compartment model, HCCs were found to have higher k2, HPI and k3 values than background liver tissues, while K1, k4 and [Formula: see text] values were not significantly different between HCCs and background liver tissues. With the combined model, HCCs were found to have higher HPI, K1 and k2, k3 and [Formula: see text] values than background liver tissues; however, the k4 value was not significantly different between HCCs and the background liver tissues. CONCLUSIONS: Short-term PET is closely equivalent to fully dynamic PET for liver kinetic estimation. Short-term PET-derived kinetic parameters can be used to distinguish HCC from background liver tissue, and the combined model improves the kinetic estimation. CLINICAL RELEVANCE STATEMENT: Short-term PET could be used for hepatic kinetic parameter estimation. The combined model could improve the estimation of liver kinetic parameters.

Kinetic parameter estimation of hepatocellular carcinoma on 18 F-FDG PET/CT based on Bayesian method.

BACKGROUND: Dynamic PET/CT combined with a dual-input three-compartment model can be applied to assess the kinetic parameters of hepatocellular carcinoma (HCC). The nonlinear least squares (NLLS) method is the most common method for fitting model parameters; however, some limitations remain. PURPOSE: A novel Bayesian-based method was compared with the NLLS method to estimate the kinetic parameters for differentiating HCCs from background liver tissue. METHODS: The proposed Bayesian method combined a priori knowledge of the physiological range and likelihood functions of HCC lesions to obtain HCC PET/CT measurements. Metropolis-Hastings sampling was used to numerically estimate the posterior distribution. This study used 5-minute dynamic PET imaging and 1-minute static PET imaging acquired 60 min post-injection from 19 HCC lesions and 17 background liver regions. RESULTS: The NLLS method indicated that k3 (p = 0.001) and fa (p < 0.001) were higher in HCCs than in background liver tissue, while K1 (p = 0.603), k2 (p = 0.405), k4 (p = 0.492), Vb (p = 0.112), and Ki (p = 0.091) were not significantly different. The Bayesian method showed that k3 (p < 0.001), fa (p < 0.001), and Ki (p = 0.002) were higher in HCCs than in background liver tissue, while K1 (p = 0.195), k2 (p = 0.028), k4 (p = 0.723), and Vb (p = 0.018) were not significantly different. For k3 and fa, the Bayesian method showed a higher AUC value for diagnostic performance in differentiating HCCs from background liver tissue than the NLLS method (0.853 vs. 0.745 and 0.928 vs. 0.886). Additionally, the Bayesian method had smaller Akaike information criteria and residual sum of squares values, as well as fewer parameter estimation outliers, than the NLLS method. CONCLUSIONS: The proposed Bayesian method can accurately and robustly estimate liver kinetic parameters, effectively distinguish between lesions and background liver tissue, and provide accurate information about the uncertainty in parameter estimation.

Dynamic chaotic gravitational search algorithm-based kinetic parameter estimation of hepatocellular carcinoma on 18F-FDG PET/CT.

BACKGROUND: Kinetic 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. METHODS: Five-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. RESULTS: The 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. CONCLUSIONS: GSA enables accurate estimation of the kinetic parameters of dynamic PET/CT in the diagnosis of HCC, and DCGSA can enhance the diagnostic performance.