A prior information-based multi-population multi-objective optimization for estimating 18F-FDG PET/CT pharmacokinetics of hepatocellular carcinoma

Abstract

Background

¹⁸F fluoro-D-glucose (¹⁸F-FDG) positron emission tomography/computed tomography (PET/CT) pharmacokinetics is an approach for efficiently quantifying perfusion and metabolic processes in the liver, but the conventional single-individual optimization algorithms and single-population optimization algorithms have difficulty obtaining reasonable physiological characteristics from estimated parameters. A prior-based multi-population multi-objective optimization (p-MPMOO) approach using two sub-populations based on two categories of prior information was preliminarily proposed for estimating the ¹⁸F-FDG PET/CT pharmacokinetics of patients with hepatocellular carcinoma.

Methods

PET data from 24 hepatocellular carcinoma (HCC) tumors of 5-min dynamic PET/CT supplemented with 1-min static PET at 60 min were prospectively collected. A reversible double-input three-compartment model and kinetic parameters (K₁, k₂, k₃, k₄, f_a, and \(\:{v}_{b}\)) were used to quantify the metabolic information. The single-individual Levenberg–Marquardt (LM) algorithm, single-population algorithms (Particle Swarm Optimization (PSO), Differential Evolution (DE), and Genetic Algorithm (GA)) and p-MPMO optimization algorithms (p-MPMOPSO, p-MPMODE, and p-MPMOGA) were used to estimate the parameters.

Results

The areas under the curve (AUCs) of the three p-MPMO methods were significantly higher than other methods in K₁ and k₄ (P < 0.05 in the DeLong test) and the single population optimization in k₂ and k₃ (P < 0.05), and did not differ from other methods in f_a and v_b (P > 0.05). Compared with single-population optimization, the three p-MPMO methods improved the significant differences between K₁, k₂, k₃, and k₄. The p-MPMOPSO showed significant differences (P < 0.05) in the parameter estimation of k₂, k₃, k₄, and f_a. The p-MPMODE is implemented on K₁, k₂, k₃, k₄, and f_a; The p-MPMOGA does it on all six parameters.

Conclusions

The p-MPMOO approach proposed in this paper performs well for distinguishing HCC tumors from normal liver tissue.