A prior information-based multi-population multi-objective optimization for estimating 18F-FDG PET/CT pharmacokinetics of hepatocellular carcinoma
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| Publicado en: | BMC Medical Imaging vol. 25 (2025), p. 1 |
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| 024 | 7 | |a 10.1186/s12880-024-01534-8 |2 doi | |
| 035 | |a 3175400482 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 58449 |2 nlm | ||
| 100 | 1 | |a Xiong, Yiwei | |
| 245 | 1 | |a A prior information-based multi-population multi-objective optimization for estimating <sup>18</sup>F-FDG PET/CT pharmacokinetics of hepatocellular carcinoma | |
| 260 | |b Springer Nature B.V. |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Background18F 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.MethodsPET 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.ResultsThe 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.ConclusionsThe p-MPMOO approach proposed in this paper performs well for distinguishing HCC tumors from normal liver tissue. | |
| 653 | |a Physiology | ||
| 653 | |a Particle swarm optimization | ||
| 653 | |a Algorithms | ||
| 653 | |a Tumors | ||
| 653 | |a Liver cancer | ||
| 653 | |a Multiple objective analysis | ||
| 653 | |a Medical imaging | ||
| 653 | |a Positron emission tomography | ||
| 653 | |a Blood | ||
| 653 | |a Patients | ||
| 653 | |a Evolutionary computation | ||
| 653 | |a Pharmacokinetics | ||
| 653 | |a Hepatocellular carcinoma | ||
| 653 | |a Parameter estimation | ||
| 653 | |a Genetic algorithms | ||
| 653 | |a Computed tomography | ||
| 653 | |a Positron emission | ||
| 653 | |a Fluorine isotopes | ||
| 653 | |a Veins & arteries | ||
| 653 | |a Glucose | ||
| 653 | |a Liver | ||
| 653 | |a Optimization algorithms | ||
| 653 | |a Ordinary differential equations | ||
| 700 | 1 | |a Li, Siming | |
| 700 | 1 | |a He, Jianfeng | |
| 700 | 1 | |a Wang, Shaobo | |
| 773 | 0 | |t BMC Medical Imaging |g vol. 25 (2025), p. 1 | |
| 786 | 0 | |d ProQuest |t Health & Medical Collection | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3175400482/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/3175400482/fulltext/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3175400482/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |