Characterization of Tumor Responses in Patients with Unresectable Hepatocellular Carcinoma Treated with Lenvatinib in the Phase 3 Randomized Trial: REFLECT
Bibliographic record
Abstract
Introduction: In REFLECT, lenvatinib was noninferior to sorafenib in terms of overall survival (OS) in patients with unresectable hepatocellular carcinoma (uHCC; median 13.6 vs. 12.3 months; HR 0.92, 95% CI 0.79-1.06). The objective response rate (ORR) with lenvatinib was 18.8% by blinded independent imaging review (IIR) per Response Evaluation Criteria in Solid Tumors version 1.1 (RECIST v1.1); per modified RECIST (mRECIST), the ORR was 40.6%. We sought to further characterize these tumor responses and explore ORR's importance among outcomes for patients with HCC. Methods: Efficacy assessments included all patients randomly assigned to receive lenvatinib treatment (if bodyweight ≥60 kg, 12 mg/day; if <60 kg, 8 mg/day). Time to first objective response (TTR) and duration of response (DOR) included patients who achieved a partial or complete tumor response. Tumors were assessed by IIR per RECIST v1.1 or mRECIST. Results: Four hundred seventy-eight patients were randomly assigned to receive lenvatinib. By IIR, 90 patients (18.8%) achieved an objective response per RECIST v1.1, and 194 (40.6%) had an objective response per mRECIST. Median TTR/DOR were 2.8 months/7.4 months in responders per RECIST v1.1, and 1.9 months/7.3 months in responders per mRECIST, respectively. Per baseline disease characteristics, ORRs by Child-Pugh score (A5/A6) were 21.2%/11.2% per RECIST v1.1 and 42.9%/33.6% per mRECIST, respectively. By baseline alpha-fetoprotein level (<400/≥400 ng/mL), ORRs were 21.4%/15.4% per RECIST v1.1 and 45.6%/33.8% per mRECIST, respectively. Incidences of treatment-related treatment-emergent adverse events were 98.9% in responders per RECIST v1.1 and 97.9% in responders per mRECIST. Conclusions: Responses were seen even in those patients with more severe disease at baseline. Tumor responses occurred early and were durable.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".