Abstract:

Objective ·Based on preoperative CT imaging combined with clinical indicators, predictive models were constructed to evaluate its application value in the recurrence risk and survival status of patients with hepatocellular carcinoma (HCC) after liver transplantation. The aim was to identify patients who exceeded the Milan criteria but could still obtain an ideal prognosis after liver transplantation, and to provide a new evidence-based basis for optimizing the selection strategy of liver transplant recipients. Methods ·Patients with HCC who underwent first orthotopic liver transplantation at Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, from January 2015 to January 2025 were included. The clinical data, including laboratory examinations, pathological results, and CT imaging were collected. The follow-up period was defined as the time from the date of liver transplantation to patient death or the study endpoint (January 31, 2025). The patients included in the study were evaluated using the Milan criteria, and the distribution of patients who met and exceeded the Milan criteria was analyzed. Through univariate and multivariate Cox analyses, independent risk factors affecting tumor recurrence and overall survival after liver transplantation in patients with HCC were screened, and predictive models for recurrence risk and survival prognosis were constructed. According to the regression coefficient (β) of each variable in the multivariate Cox regression results, proportional scores were assigned. According to the principle of maximizing the Youden index, the optimal cut-off value of the risk score was determined. The patients were divided into low-risk and high-risk groups, and the differences in recurrence-free survival and cumulative survival between the two groups were evaluated. The model risk scores were cross-stratified with the UCSF (University of California, San Francisco) standard to analyze the differences between the recurrence-free survival and cumulative survival among different stratified groups. Results ·A total of 95 patients with HCC were included in the study, among whom 64 exceeded the Milan criteria and 44 had postoperative tumor recurrence. According to the results of multivariate Cox regression analysis, preoperative AFP>400 ng/mL, tumor number>3, maximum tumor diameter>3 cm, and liver-spleen maximum length ratio (LSLR)>1.43 were included to establish the recurrence-free prediction model (P<0.05). Additionally, preoperative AFP>400 ng/mL, tumor number>3, maximum tumor diameter>3 cm, and liver-to-spleen density ratio (LSR)>1.2 were included to establish the survival prediction model (P<0.05). According to the models, the patients were subsequently stratified into low-risk and high-risk groups. In the recurrence-free prediction model, the 1-year, 3-year, and 5-year recurrence-free survival rates (86.2% vs 22.7%, 78.0% vs 6.5%, 78.0% vs 3.2%; χ²=63.642, P<0.001) and cumulative survival rates (87.8% vs 74.2%, 82.8% vs 51.2%, 79.0% vs 25.6%; χ²=14.878, P<0.001) in the low-risk group were significantly higher than those in the high-risk group. In the survival prediction model, the 1-year, 3-year, and 5-year recurrence-free survival rates (82.5% vs 19.8%, 68.7% vs 4.9%, 68.7% vs 4.9%; χ²=55.999, P<0.001) and cumulative survival rates (90.8% vs 67.2%, 83.9% vs 42.2%, 80.4% vs 14.1%; χ²=27.590, P<0.001) in the low-risk group were also significantly higher than those in the high-risk group. In the recurrence-free prediction model and survival prediction model, there were no significant differences in the recurrence-free survival and cumulative survival between the low-risk group within the UCSF criteria and the low-risk group beyond the UCSF criteria. Conclusion ·The recurrence and survival prediction models constructed in this study can effectively evaluate the recurrence risk and survival status of HCC patients after liver transplantation, and can provide a new basis for screening patients who exceed the Milan criteria but still achieve favorable prognosis after liver transplantation, thereby contributing to the optimization of liver transplant recipient selection strategies.

Key words: liver transplantation, hepatocellular carcinoma, recurrence, predictive model