A Meta-Analysis of Adjuvant Therapy after Potentially Curative Treatment for Hepatocellular Carcinoma
Bibliographic record
Abstract
BACKGROUND: The high recurrence rate of hepatocellular carcinoma (HCC) after potentially curative treatment determines the long-term prognosis. OBJECTIVE: To evaluate the efficacy and safety of adjuvant therapies in patients with HCC who have undergone hepatic resection, transplantation or locoregional ablation therapy. METHODS: Several databases were searched to identify randomized controlled trials (RCTs) fulfilling the predefined selection criteria. Meta-analyses were performed to estimate the effects of adjuvant therapies of any modality on recurrence-free survival (RFS) and overall survival (OS). RESULTS: Eight adjuvant modalities were identified from 27 eligible RCTs conducted predominantly in Asian populations comparing adjuvant with no adjuvant therapy. Adjuvant chemotherapy, internal radiation and heparanase inhibitor PI-88 therapy failed to improve RFS or OS, while interferon (IFN) therapy yielded significant survival results. The findings of adjuvant vitamin analogue therapy required further examination. Adjuvant adoptive immunotherapy conferred significant benefit for RFS but not for OS. Although cancer vaccine therapy and radioimmunotherapy may improve survival after radical surgery, the results were from single, small-scale trials. Severe side effects were observed in the studies of adjuvant chemotherapy and of IFN therapy. CONCLUSIONS: Adjuvant IFN therapy can improve both RFS and OS; however, the benefits of using this agent should be weighed against its side effects. Combination of systemic and transhepatic arterial chemotherapy is not recommended for HCC after potentially curative treatment. Other adjuvant therapies produce limited success for survival. Additional RCTs with proper design are required to establish the role of adjuvant therapies for HCC.
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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 | 0.015 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.044 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 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".