A phase II trial of second‐line axitinib following prior antiangiogenic therapy in advanced hepatocellular carcinoma
Bibliographic record
Abstract
BACKGROUND: Second-line treatment options in advanced hepatocellular carcinoma (HCC) are limited. Axitinib, a selective potent tyrosine kinase inhibitor (TKI) of vascular endothelial growth factor VEGF) receptors 1, 2, and 3, merits exploration in HCC. METHODS: This was a single-arm phase II trial of axitinib in advanced HCC. Eligible patients were Child-Pugh A/B7, with measurable progressive disease after TKIs/antiangiogenic drugs. Axitinib was started at 5 mg twice daily orally, titrated from 2 to 10 mg twice daily as tolerated. The primary end point was tumor control at 16 weeks by RECIST1.1; secondary end points were response rate, comparing response by RECIST1.1 to Choi and modified RECIST, exploring dynamic contrast-enhanced imaging models, safety, progression-free (PFS), and overall survival (OS). RESULTS: Thirty patients were treated. Of 26 patients evaluable for response, there were 3 partial responses (PR) per RECIST1.1; 13 PR by Choi, 6 PR and 1 complete response by modified RECIST. Tumor control rate at 16 weeks was 42.3%. Two-week perfusion changes were noted on functional imaging. Of 21 patients with evaluable α-fetoprotein response, 43% had >50% decrease from baseline. Most common axitinib-related grade 3/4 adverse events (AEs) were hypertension, thrombocytopenia and diarrhea. Of 11 patients with any grade hypertension, 7 had disease control >36 wks. Four patients discontinued treatment due to AEs. Median PFS was 3.6 months. Median OS was 7.1 months. CONCLUSIONS: With 42.3% tumor control at 16 weeks, primary endpoint was met. Axitinib has shown encouraging tolerable clinical activity in VEGF-pretreated HCC patients but further study should be in a selected population incorporating potential biomarkers of response.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".