Immunotherapy before liver transplant in unresectable hepatocellular carcinoma: a case report
Bibliographic record
Abstract
Background: Hepatocellular carcinoma (HCC) is a leading cause of global cancer mortality, with liver transplantation as the sole curative treatment. For advanced disease, first-line systemic therapies including immune checkpoint inhibitors (ICIs) have shown a survival benefit, but there is scarce data on clinical outcomes when used prior to transplantation. Case Description: We present three case studies of patients who received immunotherapy with atezolizumab/bevacizumab or ipilimumab/nivolumab before liver transplant. We reviewed clinical outcomes including disease response, adverse events related to systemic therapy, as well as graft function post-operatively. One case demonstrated a 45% reduction in total HCC tumour burden whereas another showed stable disease with ICIs. No adverse clinical outcomes such as graft rejection or poor wound healing were noted post-transplant. Indeed, all three patients were successfully transplanted with excellent graft function at the last follow-up. Conclusions: Our observations and data suggest ICIs are a viable option for treatment in the pre-transplant setting. It does not routinely lead to fatal graft rejection and may lengthen eligibility times until a donor organ is available.
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 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".