Dosing, efficacy and safety of lenvatinb in the real‐world treatment of hepatocellular carcinoma: Results from a Canadian database
Bibliographic record
Abstract
Abstract Background and Aims A phase 3 trial showed lenvatinib to be effective and safe in the treatment of unresectable hepatocellular carcinoma (HCC), however, its performance in the real world and effect of dosing on survival are unclear. Methods From July 2018 to June 2020, HCC patients treated with lenvatinib from 10 Canadian cancer centres were included. Overall survival (OS) and progression‐free survival (PFS) were retrospectively analysed and compared across first‐ and later lines use of lenvatinib. In patients receiving lenvatinib first‐line, OS between different mean dose intensities and starting doses were compared. Results A total of 220 patients were included, of which 79% received lenvatinib as first‐line therapy. For first‐line versus later line treatment, median OS was 12.5 versus 11.8 months (P = .83) and median PFS was 7.6 versus 4.6 months (P = .27) respectively. Of patients receiving lenvatinib first‐line, 54% started at full dose according to their weight. Median OS for patients starting lenvatinib at full‐ and reduced‐dose was 12.3 and 15.8 months (P = .75) respectively. Median OS for patients with a mean dose intensity >66.7% compared ≤66.7% was 13.7 and 7.7 months (P = .01). In the multivariate analysis, dose intensity (>66.7 vs ≤66.7%) did not predict for OS [HR 0.70, 95% CI 0.42–1.18; P = .18]. The most common side effects were fatigue (59%), hypertension (41%) and decreased appetite (25%). Conclusions Lenvatinib appears to be effective in real‐world practice regardless of the line of therapy. Dose modifications at the start or during treatment did not appear to significantly affect survival.
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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".