Description Module
The Description Module contains narrative descriptions of the clinical trial, including a brief summary and detailed description. These descriptions provide important information about the study's purpose, methodology, and key details in language accessible to both researchers and the general public.
Description Module path is as follows:
Study -> Protocol Section -> Description Module
Description Module
Ignite Creation Date:
2025-12-25 @ 4:13 AM
Ignite Modification Date:
2025-12-25 @ 4:13 AM
NCT ID:
NCT07253220
Brief Summary:
Study Objective: To compare the efficacy and prognosis of systemic cancer therapy between TAF monotherapy and ETV plus TAF combination therapy in patients with unresectable, advanced hepatitis-B-related hepatocellular carcinoma (HBV-HCC).
Study Design: Prospective, interventional cohort study. Participants: Patients with histologically or radiologically confirmed unresectable, advanced HBV-HCC who are scheduled to receive immune-based systemic therapy at The Third Affiliated Hospital of Sun Yat-sen University. Detailed inclusion/exclusion criteria are provided below.
Intervention: Enrolled participants will be assigned to receive either TAF monotherapy or ETV combined with TAF for HBV suppression.
Primary Outcome: Overall survival (OS) at 24 months after initiation of systemic therapy, compared between the two HBV-treatment strategies.
Secondary Outcomes: Decline in HBV DNA and HBsAg levels at 1, 3, 12 and 24 months.
Sample Size: 120 HCC patients (60 per arm). Statistical Analysis: All analyses will be performed with SPSS. Continuous variables will be tested for normality (Shapiro-Wilk). Normally distributed data are presented as mean ± SD; non-normally distributed data as median (IQR). Twenty-four-month OS will be estimated by Kaplan-Meier curves and compared with a Cox proportional-hazards model adjusted for age, BCLC stage, AFP level, and ICI regimen. PFS will be compared using the log-rank test; ORR and HBV DNA undetectable rate will be compared with χ² tests. Inverse-probability-of-treatment weighting (IPTW) will address selection bias, and multiple imputation will handle missing data.
Study:
NCT07253220
Study Brief:
Protocol Section:
NCT07253220