Neoadjuvant and adjuvant systemic therapy in HCC: Current status and the future
Bibliographic record
Abstract
Surgical therapies in patients with early-stage HCC can afford long-term survival but are often limited by the continued risk of recurrence, underscoring an interest in (neo)adjuvant strategies. Prior attempts at adjuvant therapy using tyrosine kinase inhibitors failed to yield significant improvements in recurrence-free survival or overall survival. Advances in the efficacy of systemic therapy options, including the introduction of immune checkpoint inhibitors, have fueled renewed interest in this area. Indeed, the IMBrave050 trial recently demonstrated significant improvements in recurrence-free survival with 1 year of adjuvant atezolizumab plus bevacizumab in high-risk patients undergoing surgical resection or ablation, with several other ongoing trials in this space. There is a strong rationale for consideration of the administration of these therapies in the neoadjuvant setting, supported by early clinical data demonstrating high rates of objective responses, although larger trials examining downstream outcomes are necessary, particularly considering the possible risks of this strategy. In parallel, there has been increased interest in using systemic therapies as a bridging or downstaging strategy for liver transplantation. Current data suggest the short-term safety of this approach, with acceptable rates of rejection, so immunotherapy is not considered a contraindication to transplant; however, larger studies are needed to evaluate the incremental value of this approach over locoregional therapy. Conversely, the use of immunotherapy is currently discouraged after liver transplantation, given the high risk of graft rejection and death. The increasing complexity of HCC management and increased consideration of (neo)adjuvant strategies highlight the critical role of multidisciplinary care when making these decisions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".