Lenvatinib for the Treatment of Radioiodine-Refractory Differentiated Thyroid Cancer: Treatment Optimization for Maximum Clinical Benefit
Bibliographic record
Abstract
BACKGROUND: Lenvatinib is a multitargeted tyrosine kinase inhibitor approved for treating patients with locally recurrent or metastatic progressive radioiodine-refractory differentiated thyroid cancer (RR-DTC). In this review, we discuss recent developments in the optimization of RR-DTC treatment with lenvatinib. SUMMARY: Initiation of lenvatinib treatment before a worsening of Eastern Cooperative Oncology Group performance status and elevated neutrophil-to-lymphocyte ratio could benefit patients with progressive RR-DTC. The median duration of response with lenvatinib was inversely correlated with a smaller tumor burden, and prognosis was significantly worse in patients with a high tumor burden. An 18 mg/day starting dose of lenvatinib was not noninferior to 24 mg/day and had a comparable safety profile. Timely management of adverse events is crucial, as patients with shorter dose interruptions benefitted more from lenvatinib treatment. Caution should be exercised when initiating lenvatinib in patients who have tumor infiltration into the trachea or other organs, or certain histological subtypes of DTC, as these are risk factors for fistula formation or organ perforation. The Study of (E7080) LEnvatinib in Differentiated Cancer of the Thyroid (SELECT) eligibility criteria should be considered prior to initiating lenvatinib treatment. CONCLUSIONS: Current evidence indicates that patients benefit most from lenvatinib treatment that is initiated earlier in advanced disease when the disease burden is low. A starting dose of lenvatinib 24 mg/day, with dose modifications as required, yields better outcomes as compared to 18 mg/day. Appropriate supportive care, including timely identification of adverse events, is essential to manage toxicities associated with lenvatinib, avoid longer dose interruptions, and maximize efficacy.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".