Characterization of Tumor Size Changes Over Time From the Phase 3 Study of Lenvatinib in Thyroid Cancer
Bibliographic record
Abstract
CONTEXT: Lenvatinib improved the progression-free survival (PFS) and overall response rate of patients with radioiodine-refractory differentiated thyroid cancer vs placebo in the Phase 3 Study of (E7080) Lenvatinib in Differentiated Cancer of the Thyroid (SELECT). OBJECTIVE: The objective of the study was to characterize tumor size changes with lenvatinib treatment. DESIGN: SELECT was a phase 3, randomized, double-blind, multicenter study. SETTING: In this clinical trial, tumor assessments of lenvatinib (n = 261) and placebo-treated (n = 131) patients were performed by independent radiological review per Response Evaluation Criteria in Solid Tumors version, 1.1 at 8-week intervals. PATIENTS: Patients with complete or partial response were defined as responders to lenvatinib (n = 169). Of the 92 nonresponders, 76 had at least one postbaseline tumor assessment and were included in this analysis. INTERVENTIONS: Lenvatinib (24 mg once daily) or placebo in 28-day cycles until unacceptable toxicity, disease progression, or death. MAIN OUTCOME MEASURES: This was an exploratory analysis of key end points from SELECT, including PFS, overall response rate, and tumor reduction. RESULTS: The median maximum percentage change in tumor size was -42.9% for patients receiving lenvatinib (responders, -51.9%; nonresponders, -20.2%). Tumor size reduction was most pronounced at first assessment (median, -24.7% at 8 wk after randomization); thereafter, the rate of change was slower but continuous (-1.3% per mo). In a multivariate model, percentage change in tumor size at the first assessment was a marginally significant positive predictor for PFS (P = .06). CONCLUSIONS: The change in tumor size conferred by lenvatinib was characterized by two phases: an initial, rapid decline, followed by slower, continuous shrinkage.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".