Liver resection <i>versus</i> radiofrequency ablation or trans-arterial chemoembolization for early-stage (BCLC A) oligo-nodular hepatocellular carcinoma: meta-analysis
Bibliographic record
Abstract
BACKGROUND: The 2022 Barcelona Clinic Liver Cancer (BCLC) algorithm does not recommend liver resection (LR) in BCLC A patients with oligo-nodular (two or three nodules ≤3 cm) hepatocellular carcinoma (HCC). This sharply contrasts with the therapeutic hierarchy concept, implying a precise treatment order exists within each BCLC stage. This study aimed to compare the outcomes of LR versus radiofrequency ablation (RFA) or trans-arterial chemoembolization (TACE) in BCLC A patients. METHODS: A meta-analysis adhering to PRISMA guidelines and the Cochrane Handbook was performed. All RCT, cohort and case-control studies that compared LR versus RFA or TACE in oligo-nodular BCLC A HCC published between January 2000 and October 2023 were comprehensively searched on PubMed, Embase, the Cochrane Library and China Biology Medicine databases. Primary outcomes were overall survival (OS) and disease-free survival (DFS) at 3 and 5 years. Risk ratio (RR) was computed as a measure of treatment effect (OS and DFS benefit) to calculate common and random effects estimates for meta-analyses with binary outcome data. RESULTS: 2601 patients from 14 included studies were analysed (LR = 1227, RFA = 686, TACE = 688). There was a significant 3- and 5-year OS benefit of LR over TACE (RR = 0.55, 95% c.i. 0.44 to 0.69, P < 0.001 and RR 0.57, 95% c.i. 0.36 to 0.90, P = 0.030, respectively), while there was no significant 3- and 5-year OS benefit of LR over RFA (RR = 0.78, 95% c.i. 0.37 to 1.62, P = 0.452 and RR 0.74, 95% c.i. 0.50 to 1.09, P = 0.103, respectively). However, a significant 3- and 5-year DFS benefit of LR over RFA was found (RR = 0.70, 95% c.i. 0.54 to 0.93, P = 0.020 and RR 0.82, 95% c.i. 0.72 to 0.95, P = 0.015, respectively). A single study comparing LR and TACE regarding DFS showed a significant superiority of LR. The Newcastle-Ottawa Scale quality of studies was high in eight (57%) and moderate in six (43%). CONCLUSIONS: In BCLC A oligo-nodular HCC patients, LR should be preferred to RFA or TACE (therapeutic hierarchy concept). Additional comparative cohort studies are urgently needed to increase the certainty of this evidence.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.055 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".