Medical physics · 2022
Kinetic parameter estimation of hepatocellular carcinoma on 18 F-FDG PET/CT based on Bayesian method.
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He Jianfeng, Li Yongjin, Wang Tao, Deng Yinglei, Wang Shaobo
Аннотация
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.
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.