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

Abstract

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 k 3 (p = 0.001) and fa (p < 0.001) were higher in HCCs than in background liver tissue, while K 1 (p = 0.603), k 2 (p = 0.405), k 4 (p = 0.492), V b (p = 0.112), and K i (p = 0.091) were not significantly different. The Bayesian method showed that k 3 (p < 0.001), fa (p < 0.001), and K i (p = 0.002) were higher in HCCs than in background liver tissue, while K 1 (p = 0.195), k 2 (p = 0.028), k 4 (p = 0.723), and V b (p = 0.018) were not significantly different. For k 3 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.

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

“…Samimi et al [32] proposed a short-term PET protocol using an early 5-min dynamic, supplemented by 3-min static imaging at 60 min postinjection. Based on the findings of previous studies and our clinical practice [11,25,33], a short-term protocol was used in this study, with 5-min dynamic PET supplemented by 1-min static imaging at 60 min postinjection; because of the metabolism of 18 F-FDG, static images were considered.…”

“…The SUVmax was measured by delineating regions of interest (ROIs) on PET images following the methodology described in previous studies [11,25]. Briefly, for lesions with imperceptible FDG uptake, ROIs were drawn relative to the conventional imaging findings.…”

“…Continuous exploration has driven PET kinetics research, the advent of total-body PET has improved photon capture capabilities, and fast dynamic imaging with high temporal resolution has become feasible [5,8,9]. Moreover, more efficient algorithms are used to improve the accuracy of kinetic parameter estimation [10,11].…”