Optimizing Survival and the Changing Landscape of Targeted Therapy for Intermediate and Advanced Hepatocellular Carcinoma: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Systemic therapy for hepatocellular carcinoma (HCC) consisting of the tyrosine kinase inhibitor sorafenib has remained unchanged for over a decade, although results from phase III targeted therapy trials have recently emerged. This review considers available phase III evidence on the use and sequencing of targeted therapy for intermediate and advanced non-locoregional therapy (LRT) eligible HCC and discusses implications for clinical practice. METHODS: Published and presented literature on phase III data reporting on targeted therapy for advanced HCC that was not eligible for loco-regional therapies was identified using the key search terms "hepatocellular cancer" AND "advanced" AND "targeted therapy" AND "phase III" OR respective aliases (PRISMA). RESULTS: Ten phase III trials assessed targeted therapy first-line and eight following sorafenib. In the first-line, atezolizumab plus bevacizumab statistically significantly improved overall survival (OS) and patient-reported outcomes (PROs) compared with sorafenib, while lenvatinib demonstrated non-inferior OS. Following progression on sorafenib, statistically significant OS improvements over placebo were seen for cabozantinib and regorafenib in unselected patients and for ramucirumab in those with baseline α-fetoprotein≥400 ng/mL. Based on improved OS and PROs, atezolizumab plus bevacizumab appears to be a preferred first-line treatment option for intermediate or advanced non-LRT eligible HCC. Phase III data informing sequencing of later lines of treatment is lacking. Therefore, sequencing principles are proposed that can be used to guide treatment selection. CONCLUSIONS: Ongoing trials will continue to inform optimal therapy. Multiple targeted therapies have improved OS in intermediate or advanced non-LRT eligible HCC, although optimal sequencing is an area of ongoing investigation.
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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".