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CN 22-1108/R
Volume 41 Issue 12
Dec.  2025
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Article Navigation >  Journal of Clinical Hepatology  > 2025  >  41(12): 2553-2561

Influencing factors for portal vein tumor thrombus in patients with primary hepatic carcinoma and establishment of a nomogram model

DOI: 10.12449/JCH251217
  • 1.

    Department of Hepatology and Immunology，Yunnan Provincial Clinical Medical Center for Infectious Diseases，Kunming Third People’s Hospital，Kunming 640041，China

  • 2.

    School of Public Health，Dali University，Dali，Yunnan 671000，China

Research funding:

Kunming Science and Technology Plan Project (2024-1-NS-0035);

Research Fund Supported by Kunming Municipal Health Commission (2023-03-08-001)

More Information
  • Corresponding author: LIU Chunyun， 751440760@qq.com （ORCID： 0000-0001-5343-5305）
  • Received Date: 2025-04-24
  • Accepted Date: 2025-07-23
  • Published Date: 2025-12-25
    Abstract
  •   Objective  To investigate the influencing factors for portal vein tumor thrombus （PVTT） in patients with primary liver cancer （PHC）， to establish a nomogram predictive model， and to assess the performance of this model.  Methods  A retrospective analysis was performed for 664 patients with the initial diagnosis of PHC who were admitted to The Third People’s Hospital of Kunming from January 2018 to December 2022， and according to the presence or absence of PVTT， they were divided into case group with 368 patients and control group with 296 patients. Related data were collected from all subjects， including general information， blood biochemical parameters， T lymphocyte subsets， routine blood test results， cytokines， thyroid function parameters， Child-Pugh score， and China Liver Cancer Staging （CNLC） stage. The t-test was used for comparison of normally distributed quantitative data between two groups， and the Mann-Whitney U test was used for comparison of non-normally distributed quantitative data between two groups； the chi-square test or the Fisher’s exact test was used for comparison of qualitative data between two groups. The Lasso regression analysis was performed for the variables with statistical significance in the univariate analysis， and the binary logistic regression analysis was performed for the screened variables to determine the influencing factors for PVTT in patients with PHC. The “rms” package was used to establish a nomogram model； the “pROC” package was used to plot the receiver operating characteristic （ROC） curve and calculate the area under the ROC curve （AUC）； the “Calibration Curves” package was used to plot calibration curves， and the “rmda” package was used to plot clinical decision curves and clinical impact curves for the assessment of the predictive model.  Results  Among the 664 patients with PHC， 368 （55.42%） developed PVTT. As for the etiology of PHC， there were 575 patients with hepatitis B （86.60%） and 89 with other causes （13.40%）， with hepatitis B as the main cause of PHC. Compared with the case group， the control group had significantly higher age， prealbumin （PA）， cholinesterase， CD3+T cells， CD8+T cells， carcinoembryonic antigen， triiodothyronine， and free triiodothyronine （all P<0.05）； compared with the control group， the case group had a significantly higher proportion of patients with Child-Pugh class B/C PHC and significantly higher levels of white blood cell count， platelet count， aspartate aminotransferase， alanine aminotransferase， gamma-glutamyl transpeptidase， alkaline phosphatase， abnormal prothrombin （PIVKA-Ⅱ）， total bile acid， high-sensitivity C-reactive protein， free thyroxine （FT4）， thyroxine， alpha-fetoprotein， interleukin-6， interleukin-10， and tumor necrosis factor-α （TNF-α） （all P<0.05）. The logistic regression analysis showed that PIVKA-Ⅱ （odds ratio ［OR］=1.968， 95% confidence interval ［CI］： 1.633 — 2.370， P<0.001）， PA （OR=0.994， 95% CI： 0.991 — 0.998， P=0.002）， FT4 （OR=1.092， 95% CI： 1.030 — 1.159， P=0.003）， and TNF-α （OR=1.085， 95% CI： 1.053 — 1.119， P<0.001） were independent influencing factors for PVTT in patients with PHC， and a nomogram model was established based on these factors. The nomogram model had an AUC of 0.816 （95% CI： 0.783 — 0.849）， a sensitivity of 0.834， and a specificity of 0.652. The calibration curve showed that this model had good consistency in predicting the onset of PVTT in patients with PHC， while the clinical decision curve and the clinical impact curve showed that this model had good clinical practicability within a certain threshold.  Conclusion  PIVKA-Ⅱ， PA， FT4， and TNF-α are independent influencing factors for the onset of PVTT in patients with PHC， and combined measurement of these four indicators can effectively predict the risk of PVTT in patients with PHC.

     

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