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

IF 2.9 3区医学 Q2 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

BMC Medical Imaging Pub Date : 2025-02-24 DOI:10.1186/s12880-024-01534-8

Yiwei Xiong, Siming Li, Jianfeng He, Shaobo Wang

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引用次数: 0

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 [Formula: see text]) 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.

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基于先验信息的多人群多目标优化，用于估计肝细胞癌的 18F-FDG PET/CT 药代动力学。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

BMC Medical Imaging RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING-

CiteScore

4.60

自引率

3.70%

发文量

198

审稿时长

27 weeks

期刊介绍： BMC Medical Imaging is an open access journal publishing original peer-reviewed research articles in the development, evaluation, and use of imaging techniques and image processing tools to diagnose and manage disease.