Radiofrequency ablation vs radiation therapy vs transarterial chemoembolization vs yttrium 90 for local treatment of liver cancer – a systematic review and network meta-analysis of survival data
Bibliographic record
Abstract
INTRODUCTION: The comparative effectiveness of radiofrequency ablation (RFA), radiation therapy (RT), transarterial chemoembolization (TACE) and transarterial radioembolization with Yttrium-90 (Y90) relative to one another for the treatment of hepatocellular carcinoma (HCC) is unclear. The aim of this systematic review and network meta-analysis is to compare RFA to RT to TACE to Y90 in the treatment of HCC. METHODS: Pubmed, Embase and Cochrane CENTRAL were searched up until April 19, 2021. Observational analyses with propensity score matched (PSM) cohort analyses and randomized controlled trials (RCT) reporting on two or more treatments relative to one another with respect to overall survival (OS) and/or progression free survival (PFS) were included. Survival data were extracted from Kaplan-Meier survival curves, and meta-analyzed using a multivariate network meta-analysis. RESULTS: After exclusions, 24 RCTs or PSM observational studies reporting on 5549 patients were included. While 1-year OS was greater for Y90 than TACE (RR 0.85, 95% CI: 0.72-0.99), all other 1-year OS comparisons across the 4 modalities yielded similar OS, and there were no differences across any modalities in 2-year and 3-year OS. TACE had a modest PFS advantage relative to RFA (RR 0.81, 95% CI: 0.68-0.95) and RT (RR 0.65, 95% CI: 0.51-0.83) at 2 years. CONCLUSION: All modalities assessed resulted in similar OS, which explains the current heterogenous practice patterns. This conclusion may assist in decision making based on administrative and patient costs, and implementation of these modalities. Other factors such as toxicity rate specific to individual patients could not be assessed using network meta-analysis and may also play a role in selection of modality. Further studies, ideally using PSM cohort analyses or RCT study design, reporting on OS, PFS, local control, complete response and toxicity are needed prior to drawing definitive conclusions.
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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 | 0.013 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.036 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".