Advances in Systemic Therapy for Hepatocellular Carcinoma and Future Prospects
Bibliographic record
Abstract
The focus of treatment of hepatocellular carcinoma (HCC) has shifted significantly from local therapy to systemic drug therapy. Recently, the efficacy of drug therapy for HCC has made rapid progress. We have transitioned from eras of sorafenib monotherapy and sequential therapy with multiple tyrosine kinase inhibitors that slightly improved patient prognoses, to an era where the introduction of immunotherapy combining atezolizumab and bevacizumab has achieved further improvements in patient prognosis. The availability of highly effective drugs has expanded the range of diseases treatable by drug therapy. Additionally, instead of initiating drug therapy at advanced stages, combining it with local therapies such as transarterial chemoembolization at an earlier stage with the aim of achieving a cure has become possible, improving treatment outcomes further. Currently, the number of regimens available for HCC, including combinations of multiple drugs and local therapies, has increased, leading to numerous clinical trials. Additionally, HCC cases that were previously unresectable are now resectable after drug therapy, necessitating the establishment of a resectability classification system. This review summarizes the current evidence for drug therapy for HCC and discusses future treatment strategies, treatment combinations, and prospects.
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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 | 0.002 | 0.002 |
| 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 | 0.001 |
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".