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Background <p><sup>18</sup>F fluoro-D-glucose (<sup>18</sup>F-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 <sup>18</sup>F-FDG PET/CT pharmacokinetics of patients with hepatocellular carcinoma.</p> Methods <p>PET data from 24 hepatocellular carcinoma (HCC) tumors of 5-min dynamic PET/CT supplemented with 1-min static PET at 60&#xa0;min were prospectively collected. A reversible double-input three-compartment model and kinetic parameters (<i>K</i><sub>1</sub>, <i>k</i><sub>2</sub>, <i>k</i><sub>3</sub>, <i>k</i><sub>4</sub>, <i>f</i><sub><i>a</i></sub>, and <InlineEquation ID="IEq1"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="12880_2024_1534_Article_IEq1.gif" Format="GIF" Height="12" Rendition="HTML" Resolution="72" Type="Linedraw" Width="17" /> </InlineMediaObject> <EquationSource Format="TEX">\(\:{v}_{b}\)</EquationSource> </InlineEquation>) 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.</p> Results <p>The areas under the curve (AUCs) of the three p-MPMO methods were significantly higher than other methods in <i>K</i><sub>1</sub> and <i>k</i><sub>4</sub> (<i>P</i> &lt; 0.05 in the DeLong test) and the single population optimization in <i>k</i><sub>2</sub> and <i>k</i><sub>3</sub> (<i>P</i> &lt; 0.05), and did not differ from other methods in <i>f</i><sub><i>a</i></sub> and <i>v</i><sub><i>b</i></sub> (<i>P</i> &gt; 0.05). Compared with single-population optimization, the three p-MPMO methods improved the significant differences between <i>K</i><sub>1</sub>, <i>k</i><sub>2</sub>, <i>k</i><sub>3</sub>, and <i>k</i><sub>4</sub>. The p-MPMOPSO showed significant differences (<i>P</i> &lt; 0.05) in the parameter estimation of <i>k</i><sub>2</sub>, <i>k</i><sub>3</sub>, <i>k</i><sub>4</sub>, and <i>f</i><sub><i>a</i></sub>. The p-MPMODE is implemented on <i>K</i><sub>1</sub>, <i>k</i><sub>2</sub>, <i>k</i><sub>3</sub>, <i>k</i><sub>4</sub>, and <i>f</i><sub><i>a</i></sub>; The p-MPMOGA does it on all six parameters.</p> Conclusions <p>The p-MPMOO approach proposed in this paper performs well for distinguishing HCC tumors from normal liver tissue.</p>

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

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  • 发表日期：

    2025年2月24日

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  • Yiwei Xiong,
  • Siming Li,
  • Jianfeng He,
  • Shaobo Wang

摘要

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, fa, 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 fa and vb (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 fa. The p-MPMODE is implemented on K1, k2, k3, k4, and fa; 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.