Drug Treatment for Advanced Hepatocellular Carcinoma: First-Line and Beyond
Bibliographic record
Abstract
Hepatocellular carcinoma (HCC) has high mortality. The option of systemic therapy has increased significantly over the past five years. Sorafenib was the first multikinase inhibitor, introduced in 2007, as a treatment option for HCC, and it was the only effective systemic treatment for more than ten years. It was not until 2017 that several breakthroughs were made in the development of systemic strategies. Lenvatinib, another multikinase inhibitor, stood out successfully after sorafenib, and has been applied to clinical use in the first-line setting. Other multikinase inhibitors such as regorafenib, ramucirumab and cabozantinib, were approved in quick succession as second-line therapies. Concurrently, immune checkpoint inhibitors (ICIs) have readily become established treatments for many solid tumors, including HCC. The most studied ICIs to date, target programmed cell death-1 (PD-1), its ligand PD-L1, and cytotoxic T-lymphocyte-associated protein 4 (CTLA-4). These ICIs have demonstrated efficacy in treating advanced HCC. More recently, combination of bevacizumab and atezolizumab (ICI targeting PD-L1) was approved as the gold-standard first-line therapy. Combination of ICIs with nivolumab and ipilimumab was also approved in the second-line setting for those who failed sorafenib. At the moment, numerous clinical trials in advanced HCC are underway, which will bring continuous change to the management, and increase the survival, for patients with advanced HCC. Our review article: (1) summarizes United States Food and Drug Administration (US FDA) approved systemic therapies in advanced HCC, (2) reports the evidence of currently approved treatments, (3) discusses potential drugs/drug combinations being currently tested in phase III clinical trials, and (4) proposes possible future directions in drug development for advanced 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".