Comparative analysis of treatment modalities for solitary, small (≤3 cm) hepatocellular carcinoma: A systematic review and network meta-analysis of oncologic outcomes
Bibliographic record
Abstract
BACKGROUND: Solitary hepatocellular carcinoma measuring ≤3 cm represents approximately 30% of hepatocellular carcinoma cases, yet treatment guidelines lack robust evidence. This study compares oncologic outcomes after ablation, liver resection, and liver transplantation for solitary, small hepatocellular carcinoma. METHODS: We systematically searched databases up to 7 February 2022, for studies including adults with solitary hepatocellular carcinoma ≤3 cm treated by any ablation, liver resection, or liver transplantation. We excluded non-hepatocellular carcinoma cancers, recurrent/metastatic diseases, and alternative therapies. A frequentist network meta-analysis assessed 5-year overall survival and recurrence-free survival using only adjusted effect estimates while accounting for bias risk. RESULTS: We identified 80 studies (4 randomized controlled trials, 72 retrospectives, and 4 prospective cohorts) with 28,211 patients. In the network meta-analysis for 5-year overall survival (26 studies), liver transplantation was associated with the lowest mortality hazard (hazard ratio, 0.47; 95% confidence interval, 0.31-0.73, referenced to liver resection), followed by liver resection (reference), whereas ablation had the greatest mortality hazard (hazard ratio, 1.32; 95% confidence interval, 1.16-1.49, referenced to liver resection). For 5-year recurrence-free survival (19 studies), liver transplantation had the best outcome (hazard ratio, 0.36; 95% confidence interval, 0.20-0.63, referenced to liver transplantation), followed by liver resection (reference), with ablation showing the least favorable outcome (hazard ratio, 1.67; 95% confidence interval, 1.45-1.93, referenced to liver resection). CONCLUSIONS: This network meta-analysis provides the evidence for comparing treatment modality outcomes for solitary, small (≤3 cm) hepatocellular carcinoma. LT emerges as the superior choice for achieving a better 5-year OS, followed by liver resection, then ablation. When feasible to preserve liver function, liver resection can be prioritized. Ablation with close surveillance should be reserved for individuals unfit for surgery.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.028 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".