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Updated: Jul 17, 2025
A Three-Dimensional Spheroid Model to Investigate the Tumor-Stromal Interaction in Hepatocellular Carcinoma
Published on: September 30, 2021
Development and application of hepatocellular carcinoma risk prediction model based on clinical characteristics and
Zhi-Jie Liu1, Yue Xu2, Wen-Xuan Wang3
1Department of Clinical Transfusion, Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, Sichuan Province, China.
Background:
Hepatocellular carcinoma (HCC) is difficult to diagnose with poor therapeutic effect, high recurrence rate and has a low survival rate. The survival of patients with HCC is closely related to the stage of diagnosis. At present, no specific serological indicator or method to predict HCC, early diagnosis of HCC remains a challenge, especially in China, where the situation is more severe.
Aim:
To identify risk factors associated with HCC and establish a risk prediction model based on clinical characteristics and liver-related indicators.
Methods:
The clinical data of patients in the Affiliated Hospital of North Sichuan Medical College from 2016 to 2020 were collected, using a retrospective study method. The results of needle biopsy or surgical pathology were used as the grouping criteria for the experimental group and the control group in this study. Based on the time of admission, the cases were divided into training cohort (n = 1739) and validation cohort (n = 467). Using HCC as a dependent variable, the research indicators were incorporated into logistic univariate and multivariate analysis. An HCC risk prediction model, which was called NSMC-HCC model, was then established in training cohort and verified in validation cohort.
Results:
Logistic univariate analysis showed that, gender, age, alpha-fetoprotein, and protein induced by vitamin K absence or antagonist-II, gamma-glutamyl transferase, aspartate aminotransferase and hepatitis B surface antigen were risk factors for HCC, alanine aminotransferase, total bilirubin and total bile acid were protective factors for HCC. When the cut-off value of the NSMC-HCC model joint prediction was 0.22, the area under receiver operating characteristic curve (AUC) of NSMC-HCC model in HCC diagnosis was 0.960, with sensitivity 94.40% and specificity 95.35% in training cohort, and AUC was 0.966, with sensitivity 90.00% and specificity 94.20% in validation cohort. In early-stage HCC diagnosis, the AUC of NSMC-HCC model was 0.946, with sensitivity 85.93% and specificity 93.62% in training cohort, and AUC was 0.947, with sensitivity 89.10% and specificity 98.49% in validation cohort.
Conclusion:
The newly NSMC-HCC model was an effective risk prediction model in HCC and early-stage HCC diagnosis.
