Dual-phase multiobjective Bayesian optimization method for estimating hepatocellular carcinoma dynamics parameters from PET/CT scans.

[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 via a double-input three-compartment model. The role of pharmacokinetic parameters in distinguishing HCC was compared among the Bayesian method (BM), BO method, and DPMO-BO method. The fitting deviation between the predictions of the model and the actual observations was assessed via the root mean square error (RMSE).

[RESULTS] The results demonstrated that the BM significantly distinguished HCC from background liver tissues with , , , and (all P<0.05), whereas the BO method achieved this degree of differentiation for and (both P<0.001). The DPMO-BO method resulted in significant differences in all of these parameters (all P<0.05). DPMO-BO yielded greater area under the receiver operating characteristic (ROC) curve (AUC) values for (AUC =0.709) than did BO (AUC =0.595, P<0.001). Additionally, reduced RMSEs for HCC and normal liver tissues were observed with DPMO-BO (1.226 and 1.051, respectively) relative to those values obtained with the BM (1.324 and 1.118, respectively) and BO (1.308 and 1.143, respectively).

[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.