Immunotherapy Responses in Viral Hepatitis-Induced HCC: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: Hepatocellular carcinoma (HCC) is a prevalent liver cancer with poor prognosis, often linked to hepatitis B (HBV) and C (HCV) infections. This meta-analysis evaluates the efficacy of immunotherapy in HCC, particularly in cases arising from viral hepatitis. Methods: In adherence to PRISMA Statement 2020 guidelines, the immunotherapeutic outcomes comprised objective response rate (ORR), progression-free survival (PFS), and overall survival (OS). Data were analyzed from randomized controlled trials up to April 2024 using the fixed-effects models in R (V.4.3.3.) and RevMan (Cochrane). Results: This study included 9 trials with 5316 patients. The ORR was slightly higher in the viral group at 27.93% compared to 24.07% in the non-viral group, though this difference was not significant (p = 0.15). Viral HCC patients exhibited a median PFS of 7.3 months (IQR: 6.2–8.4) compared to 5.8 months (IQR: 5.48–6.13) in non-viral patients, a significant improvement (p = 0.005). Similarly, median OS was longer in the viral group at 16.8 months (IQR: 12.99–20.61) versus 15.2 months (IQR: 13.25–17.15) for non-viral HCC, which was also significant (p < 0.0001). The median OS for viral HCC was 16.8 months (IQR: 14.11–19.49 months), with HBV patients experiencing slightly higher survival at 17.15 months (IQR: 14.3–20 months) compared to 16.8 months (IQR: 12.99–20.61 months) for HCV patients; this difference was not statistically significant (p = 0.89). Conclusions: Immunotherapy shows potential in treating HCC, with significantly better outcomes in viral HCC, particularly HBV-associated cases. The heterogeneity highlights the need for personalized treatment approaches based on the viral background of HCC patients. Further research should aim to optimize these therapies to improve survival rates.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.034 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".