Correlation study of 18F-FDG PET/CT metabolic parameters, heterogeneity index, and microvascular invasion, and its nomogram potential in predicting microvascular invasion in liver cancer before liver transplantation.

Abstract

Background: Hepatocellular carcinoma (HCC) is a highly prevalent malignant tumor worldwide, with Chinese patients accounting for more than 50%. Microvascular invasion (MVI) is a significant risk factor for postoperative recurrence of HCC. 18 F-fluorodeoxyglucose PET/computed tomography ( 18 F-FDG PET/CT), as a hybrid imaging modality integrating metabolic information from PET with anatomical details from CT. This combined approach enables simultaneous assessment of glucose metabolism and structural features. It can also evaluate tumor biological behavior through metabolic parameters and heterogeneity characteristics.

Objective: To explore the predictive value of 18 F-FDG PET/CT metabolic parameters and heterogeneity index for MVI in HCC patients before liver transplantation and to construct a nomogram prediction model.

Methods: A retrospective study involving 177 HCC patients who underwent liver transplantation (100 MVI-positive cases and 77 MVI-negative cases) was conducted to analyze the correlation between clinical characteristics, PET/CT metabolic parameters (SUVmax, SUVmean, TLG, and TLR), and heterogeneity parameters (COV and HI) with MVI. Independent predictors were identified using univariate and multivariate logistic regression, and a nomogram model was constructed. The model's performance was evaluated using calibration curves and ROC curves.

Results: Univariate analysis showed significant differences in PIVKA-II, SUVmax, TLG, TLR, COV, and HI between the two groups (all P  < 0.05). Multivariate analysis indicated that PIVKA-II (OR = 1.000, P = 0.042), TLG (OR = 0.999, P = 0.024), HI (OR = 1.022, P < 0.001), and TLR (OR = 1.618, P = 0.031) were independent predictors of MVI. The area under the ROC curve (AUC) of the combined model reached 0.815 (95% confidence interval: 0.754-0.876), significantly better than any single parameter. The nomogram calibration curve showed a high consistency between predicted probabilities and actual observed probabilities (mean absolute error = 0.025).

Conclusion: The integration of PET/CT-derived parameters-specifically TLG (metabolic burden), HI (heterogeneity), and TLR (tumor-to-liver contrast)-with serum PIVKA-II provides a robust tool for preoperative MVI prediction in HCC patients undergoing liver transplantation. The validated nomogram model (AUC = 0.815) outperforms individual parameters, offering a reliable basis for clinical decision-making.