ArticleQuantitative imaging in medicine and surgery2025
Dual-phase multiobjective Bayesian optimization method for estimating hepatocellular carcinoma dynamics parameters from PET/CT scans.
Article in Quantitative imaging in medicine and surgery, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: Optimization algorithms provide robust analytical frameworks for assessing hepatocellular carcinoma (HCC) pharmacokinetics based on dynamic positron emission tomography/computed tomography (PET/CT) scans. The aim of this study was to assess the role of estimating HCC pharmacokinetics from PET/ CT scans via the Bayesian optimization (BO) method and the dual-phase (DP) and multiobjective (MO) strategies into BO (DPMO-BO) method. Methods: Five-minute dynamic and one-minute static PET/CT imaging data derived from 27 HCC tumors were used to estimate kinetic parameters Results: The results demonstrated that the BM significantly distinguished HCC from background liver tissues with Conclusions: The BO method can be used to assess HCC pharmacokinetics, whereas the DPMO-BO method further enhances diagnostic performance by achieving improved fitting accuracy.
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