{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,17]],"date-time":"2026-02-17T05:43:22Z","timestamp":1771307002372,"version":"3.50.1"},"reference-count":49,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2022,12,3]],"date-time":"2022-12-03T00:00:00Z","timestamp":1670025600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82160347"],"award-info":[{"award-number":["82160347"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["aapm.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Medical Physics"],"published-print":{"date-parts":[[2023,5]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Background<\/jats:title><jats:p>Dynamic PET\/CT combined with a dual\u2010input three\u2010compartment model can be applied to assess the kinetic parameters of hepatocellular carcinoma (HCC). The nonlinear least squares (NLLS) method is the most common method for fitting model parameters; however, some limitations remain.<\/jats:p><\/jats:sec><jats:sec><jats:title>Purpose<\/jats:title><jats:p>A novel Bayesian\u2010based method was compared with the NLLS method to estimate the kinetic parameters for differentiating HCCs from background liver tissue.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>The proposed Bayesian method combined a priori knowledge of the physiological range and likelihood functions of HCC lesions to obtain HCC PET\/CT measurements. Metropolis\u2010Hastings sampling was used to numerically estimate the posterior distribution. This study used 5\u2010minute dynamic PET imaging and 1\u2010minute static PET imaging acquired 60 min post\u2010injection from 19 HCC lesions and 17 background liver regions.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The NLLS method indicated that <jats:italic>k<jats:sub>3<\/jats:sub><\/jats:italic> (<jats:italic>p<\/jats:italic>\u00a0=\u00a00.001) and <jats:italic>fa<\/jats:italic> (<jats:italic>p<\/jats:italic>\u00a0&lt;\u00a00.001) were higher in HCCs than in background liver tissue, while <jats:italic>K<jats:sub>1<\/jats:sub><\/jats:italic> (<jats:italic>p<\/jats:italic>\u00a0=\u00a00.603), <jats:italic>k<jats:sub>2<\/jats:sub><\/jats:italic> (<jats:italic>p<\/jats:italic>\u00a0=\u00a00.405), <jats:italic>k<jats:sub>4<\/jats:sub><\/jats:italic> (<jats:italic>p<\/jats:italic>\u00a0=\u00a00.492), <jats:italic>V<jats:sub>b<\/jats:sub><\/jats:italic> (<jats:italic>p<\/jats:italic>\u00a0=\u00a00.112), and <jats:italic>K<jats:sub>i<\/jats:sub><\/jats:italic> (<jats:italic>p<\/jats:italic>\u00a0=\u00a00.091) were not significantly different. The Bayesian method showed that <jats:italic>k<jats:sub>3<\/jats:sub><\/jats:italic> (<jats:italic>p<\/jats:italic>\u00a0&lt;\u00a00.001), <jats:italic>fa<\/jats:italic> (<jats:italic>p<\/jats:italic>\u00a0&lt;\u00a00.001), and <jats:italic>K<jats:sub>i<\/jats:sub><\/jats:italic> (<jats:italic>p<\/jats:italic>\u00a0=\u00a00.002) were higher in HCCs than in background liver tissue, while <jats:italic>K<jats:sub>1<\/jats:sub><\/jats:italic> (<jats:italic>p<\/jats:italic>\u00a0=\u00a00.195), <jats:italic>k<jats:sub>2<\/jats:sub><\/jats:italic> (<jats:italic>p<\/jats:italic>\u00a0=\u00a00.028), <jats:italic>k<jats:sub>4<\/jats:sub><\/jats:italic> (<jats:italic>p<\/jats:italic>\u00a0=\u00a00.723), and <jats:italic>V<jats:sub>b<\/jats:sub><\/jats:italic> (<jats:italic>p<\/jats:italic>\u00a0=\u00a00.018) were not significantly different. For <jats:italic>k<jats:sub>3<\/jats:sub><\/jats:italic> and <jats:italic>fa<\/jats:italic>, the Bayesian method showed a higher AUC value for diagnostic performance in differentiating HCCs from background liver tissue than the NLLS method (0.853\u00a0vs. 0.745 and 0.928\u00a0vs. 0.886). Additionally, the Bayesian method had smaller Akaike information criteria and residual sum of squares values, as well as fewer parameter estimation outliers, than the NLLS method.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>The proposed Bayesian method can accurately and robustly estimate liver kinetic parameters, effectively distinguish between lesions and background liver tissue, and provide accurate information about the uncertainty in parameter estimation.<\/jats:p><\/jats:sec>","DOI":"10.1002\/mp.16139","type":"journal-article","created":{"date-parts":[[2022,11,27]],"date-time":"2022-11-27T11:21:54Z","timestamp":1669548114000},"page":"2860-2871","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Kinetic parameter estimation of hepatocellular carcinoma on <sup>18<\/sup>F\u2010FDG PET\/CT based on Bayesian 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China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yinglei","family":"Deng","sequence":"additional","affiliation":[{"name":"PET\/CT Center Affiliated Hospital of Kunming University of Science and Technology First People's Hospital of Yunnan Province  Kunming China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shaobo","family":"Wang","sequence":"additional","affiliation":[{"name":"PET\/CT Center Affiliated Hospital of Kunming University of Science and Technology First People's Hospital of Yunnan Province  Kunming 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