Current Landscape of Immune Checkpoint Inhibitor Therapy for Hepatocellular Carcinoma
Bibliographic record
Abstract
The liver maintains a balance between immune tolerance and activation in its role as a filtration system. Chronic inflammation disrupts this immune microenvironment, thereby allowing for the rise and progression of cancer. Hepatocellular carcinoma (HCC) is a liver tumor generally diagnosed in the setting of chronic liver disease. When diagnosed early, the primary treatment is surgical resection, liver transplantation, or liver directed therapies. Unfortunately, patients with HCC often present at an advanced stage or with poor liver function, thereby limiting options. To further complicate matters, most systemic therapies are relatively limited and ineffective among patients with advanced disease. Recently, the IMbrave150 trial demonstrated that the combination of atezolizumab and bevacizumab was associated with better survival compared to sorafenib among patients with advanced HCC. As such, atezolizumab and bevacizumab is now recommended first-line therapy for these patients. Tumor cells work to create an immunotolerant environment by preventing the activation of stimulatory immunoreceptors and upregulating expression of proteins that bind inhibitory immunoreceptors. ICIs work to block these interactions and bolster the anti-tumor function of the immune system. We herein provide an overview of the use of ICIs in the treatment of HCC.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".