Significance of surgical resection and resection margins for hepatocellular carcinoma with microvascular invasion: a systematic review and meta-analysis
Bibliographic record
Abstract
OBJECTIVE: For hepatocellular carcinoma (HCC) with microvascular invasion (MVI), the choice of surgical resection (SR) and resection margins (RMs) remains to be determined. The aim of this study was to discuss the relationship between SR and RM and MVI-positive HCC. METHODS: PubMed, Embase, Web of Science, and Cochrane Library were searched up to 1 September 2024. The methodological quality of eligible articles was assessed using the Newcastle-Ottawa Scale (NOS). Effect models were selected to pool the HR and 95% CI of recurrence and overall survival (OS) based on the presence of heterogeneity to assess the impact of SR and RM in MVI-positive HCC. RESULTS: A total of 12 articles with 6747 cases were included. NOS scale indicated that the studies were of high quality. The results showed that narrow RM were a risk factor for postoperative recurrence and OS in MVI-positive HCC, with a pooled HR of 1.76 (95% CI: 1.49, 2.07) and 1.99 (95% CI: 1.58, 2.49), respectively; whereas nonanatomical resection (NAR) was another risk factor for postoperative recurrence and OS, with a pooled HR of 1.33 (95% CI: 1.15, 1.54) and 1.42 (95% CI: 1.15, 1.75), so wide RM and anatomical resection (AR) was beneficial for postoperative recurrence and long-term survival. In the subgroups, narrow RM were more than twice the risk factor for TTR compared with wide RM; and in the SR subgroup, studies from the Japanese had more than double the risk factor for postoperative recurrence and OS compared with China. CONCLUSION: For HCC with MVI, treatment modalities recommending anatomical resection and wide margins will have beneficial effects on postoperative recurrence and long-term survival.
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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 | 0.012 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.041 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".