Pretreatment Non-Invasive Biomarkers as Predictors to Estimate Portal Vein Tumor Thrombosis (PVTT) Risk and Long-Term Survival in HBV-Related Hepatocellular Carcinoma Patients Without PVTT

Abstract

Background: PVTT is a hallmark of advanced hepatocellular carcinoma (HCC). We aim to explore the influence of non-invasive biomarkers on the occurrence of PVTT and develop and validate models for predicting prognosis in HBV-related HCC patients without PVTT.

Methods: A total of 1026 HBV-related HCC patients without PVTT were enrolled, with 515 in the training cohort, 216 in the internal validation cohort, and 295 in the external validation cohort. We conducted Cox regression analyses to discern the independent risk factors associated with PVTT events, PFS, and OS, then constructed and validated predictive models. The predictive and discriminatory capabilities of models were assessed using the calibration, time-dependent ROC, and DCA curves.

Results: In our study, 136 patients (13.3%) experienced PVTT events during the follow-up period. The Cox regression analysis unveiled that male gender, AAPR ≤0.49, APRI >0.48, extrahepatic metastasis, and multiple tumors were independent risk factors for PVTT. In the training cohort, non-invasive biomarkers (AAR and APRI), AFP, ascites, and tumor-related characteristics (extrahepatic metastasis, tumor diameter, tumor number, and PVTT event) were independent risk factors for both OS and PFS, whereas age and ALBI grade independently correlated with OS. The C-indexes of OS and PFS nomogram models were 0.795 and 0.733 in the training cohort, 0.765 and 0.716 in the internal validation cohort, and 0.780 and 0.722 in the external validation cohort, respectively. Our models demonstrated strong predictive and discriminative abilities in all cohorts and yielded a greater net benefit compared to three traditional staging systems.

Conclusion: Non-invasive biomarkers are expected to be reliable predictors for assessing PVTT risk and predicting prognosis among HBV-related HCC patients without PVTT.

Keywords: HBV-related HCC; nomogram; non-invasive biomarker; portal vein tumor thrombosis; prognosis.

Figure 1

Comparison of clinical characteristics and prognosis between PVTT group and non-PVTT group. The values of AAPR (A), APRI (B), ALBI score (C), and tumor diameter (D) were marked differences between the PVTT group and the non-PVTT group. KM survival curves revealed that patients occurred PVTT events had poorer OS (E) and PFS (F) than patients without PVTT.

Figure 2

Cox regression analysis and forest plot of PVTT in the entire cohort.

Figure 3

Cox regression analysis and forest plots of OS (A) and PFS (B) in the training cohort.

Figure 4

Construction and validation of the OS nomogram model. Nomogram predicting the OS for HBV-related HCC patients without PVTT (A). The calibration curves of the OS model for predicting 1‐, 3‐, and 5‐year OS in the training cohort (B), internal validation cohort (C), and external validation cohort (D). The time-dependent ROC curves of the OS model were used to evaluate the predictive ability of 1‐, 3‐, and 5‐year OS in the training cohort (E), internal validation cohort (F), and external validation cohort (G). The DCA curves of the OS model were performed to assess the clinical utility of 5‐year OS in the training cohort (H), internal validation cohort (I), and external validation cohort (J). KM survival analysis of OS among different risk groups based on the score of the OS nomogram model in the training cohort (K), internal validation cohort (L), and external validation cohort (M).

Figure 5

Construction and validation of the PFS nomogram model. Nomogram predicting the PFS for HBV-related HCC patients without PVTT (A). The calibration curves of the PFS model for predicting 1‐, 3‐, and 5‐year PFS in the training cohort (B), internal validation cohort (C), and external validation cohort (D). The time-dependent ROC curves of the PFS model were used to evaluate the predictive ability of 1‐, 3‐, and 5‐year PFS in the training cohort (E), internal validation cohort (F), and external validation cohort (G). The DCA curves of the PFS model were performed to assess the clinical utility of 5‐year PFS in the training cohort (H), internal validation cohort (I), and external validation cohort (J). KM survival analysis of PFS among different risk groups based on the score of the PFS nomogram model in the training cohort (K), internal validation cohort (L), and external validation cohort (M).

Figure 6

Subgroup survival analysis for the entire cohort stratified by the BCLC staging system. KM survival analysis of OS and PFS among different risk groups based on the score of the OS nomogram model and PFS nomogram model in BCLC stage 0/A patients (A and D), BCLC stage B patients (B and E), and BCLC stage C/D patients (C and F), respectively.