{"title":"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。","authors":"Yiwei Xiong, Siming Li, Jianfeng He, Shaobo Wang","doi":"10.1186/s12880-024-01534-8","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong><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><p><strong>Methods: </strong>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 (K<sub>1</sub>, k<sub>2</sub>, k<sub>3</sub>, k<sub>4</sub>, f<sub>a</sub>, 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.</p><p><strong>Results: </strong>The areas under the curve (AUCs) of the three p-MPMO methods were significantly higher than other methods in K<sub>1</sub> and k<sub>4</sub> (P < 0.05 in the DeLong test) and the single population optimization in k<sub>2</sub> and k<sub>3</sub> (P < 0.05), and did not differ from other methods in f<sub>a</sub> and v<sub>b</sub> (P > 0.05). Compared with single-population optimization, the three p-MPMO methods improved the significant differences between K<sub>1</sub>, k<sub>2</sub>, k<sub>3</sub>, and k<sub>4</sub>. The p-MPMOPSO showed significant differences (P < 0.05) in the parameter estimation of k<sub>2</sub>, k<sub>3</sub>, k<sub>4</sub>, and f<sub>a</sub>. The p-MPMODE is implemented on K<sub>1</sub>, k<sub>2</sub>, k<sub>3</sub>, k<sub>4</sub>, and f<sub>a</sub>; The p-MPMOGA does it on all six parameters.</p><p><strong>Conclusions: </strong>The p-MPMOO approach proposed in this paper performs well for distinguishing HCC tumors from normal liver tissue.</p>","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":"25 1","pages":"59"},"PeriodicalIF":2.9000,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11854238/pdf/","citationCount":"0","resultStr":"{\"title\":\"A prior information-based multi-population multi-objective optimization for estimating <sup>18</sup>F-FDG PET/CT pharmacokinetics of hepatocellular carcinoma.\",\"authors\":\"Yiwei Xiong, Siming Li, Jianfeng He, Shaobo Wang\",\"doi\":\"10.1186/s12880-024-01534-8\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong><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><p><strong>Methods: </strong>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 (K<sub>1</sub>, k<sub>2</sub>, k<sub>3</sub>, k<sub>4</sub>, f<sub>a</sub>, 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.</p><p><strong>Results: </strong>The areas under the curve (AUCs) of the three p-MPMO methods were significantly higher than other methods in K<sub>1</sub> and k<sub>4</sub> (P < 0.05 in the DeLong test) and the single population optimization in k<sub>2</sub> and k<sub>3</sub> (P < 0.05), and did not differ from other methods in f<sub>a</sub> and v<sub>b</sub> (P > 0.05). Compared with single-population optimization, the three p-MPMO methods improved the significant differences between K<sub>1</sub>, k<sub>2</sub>, k<sub>3</sub>, and k<sub>4</sub>. The p-MPMOPSO showed significant differences (P < 0.05) in the parameter estimation of k<sub>2</sub>, k<sub>3</sub>, k<sub>4</sub>, and f<sub>a</sub>. The p-MPMODE is implemented on K<sub>1</sub>, k<sub>2</sub>, k<sub>3</sub>, k<sub>4</sub>, and f<sub>a</sub>; The p-MPMOGA does it on all six parameters.</p><p><strong>Conclusions: </strong>The p-MPMOO approach proposed in this paper performs well for distinguishing HCC tumors from normal liver tissue.</p>\",\"PeriodicalId\":9020,\"journal\":{\"name\":\"BMC Medical Imaging\",\"volume\":\"25 1\",\"pages\":\"59\"},\"PeriodicalIF\":2.9000,\"publicationDate\":\"2025-02-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11854238/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"BMC Medical Imaging\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1186/s12880-024-01534-8\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"BMC Medical Imaging","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1186/s12880-024-01534-8","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING","Score":null,"Total":0}
A prior information-based multi-population multi-objective optimization for estimating 18F-FDG PET/CT pharmacokinetics of hepatocellular carcinoma.
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 [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 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.
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.
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