Role of immune checkpoint inhibitor combinations in resectable and unresectable, embolization-eligible hepatocellular carcinoma
Bibliographic record
Abstract
Immune checkpoint inhibitor (ICI) combination regimens have recently become the new standard of care for advanced hepatocellular carcinoma (HCC). Large, phase III registrational trials of ICI-containing regimens in resectable and embolization-eligible settings are now reading out. This review summarizes and critically appraises efficacy and safety data from these studies with consideration of the optimal use of ICIs in conjunction with antiangiogenic agents including important issues in management of HCC across the continuum of care such as those related to patient selection, treatment sequencing, and liver preservation. IMbrave050 assessed atezolizumab plus bevacizumab in resected HCC and EMERALD-1 and LEAP-012 evaluated addition of durvalumab-bevacizumab and pembrolizumab-lenvatinib to transarterial chemoembolization in unresectable, embolization-eligible HCC. Both EMERALD-1 and LEAP-012 met the primary endpoint of progression-free survival. While IMbrave050 initially met its primary endpoint of recurrence-free survival, the adjuvant atezolizumab-bevacizumab benefit was not maintained in an updated analysis. Survival benefits remain unclear for all phase III trials. Safety outcomes can be generally described as predictable based on experience with the respective experimental regimens in the advanced setting. Treatment selection in embolization-eligible settings should consider risks and benefits with special consideration of liver preservation. Additional research is required to optimize ICI combination use in the perioperative and peri-embolization settings.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".