Real-world outcomes of lenvatinib therapy for advanced neuroendocrine neoplasms
Bibliographic record
Abstract
Advanced gastroenteropancreatic neuroendocrine neoplasms (GEP-NENs) constitute a heterogeneous group of incurable cancers. Lenvatinib is an oral multiple kinase inhibitor that showed activity in grade 1/2 GEP-NENs in the phase II TALENT trial, but a confirmatory phase III study has yet to be conducted. To investigate the real-world use of lenvatinib in treating patients with advanced GEP-NENs, we retrospectively analyzed a cohort of adults with unresectable neuroendocrine neoplasms (NENs) from two academic centers in Canada who received palliative treatment with lenvatinib. Progression-free survival (PFS), overall survival (OS) and the treating clinician assessment of best therapeutic response were analyzed in the entire cohort and in the subgroup of patients with GEP-NENs that would have been eligible for the TALENT trial. Overall, 33 patients, with mostly G1/G2 (78.8%) metastatic NENs, received lenvatinib. The pancreas was the most common primary site (n = 16, 48.5%), followed by the small bowel (n = 12, 36.4%). The median number of prior lines of systemic therapy was 2 (range 1-5). The median initial, maximal and minimal doses (mg) were 12 (range 4-24), 12 (range 8-24) and 8 (range 4-24), respectively. The median PFS was 11.9 months (95% CI, 9.5-NA), and the median OS was 17.5 months (95% CI, 12.7-NA), with disease burden reduction seen in 21.9% (95% CI, 11.0-38.7) and 87.5% (95% CI, 71.9-95.3) of patients achieving disease control. The most frequent side effects reported were hypertension (60.6%), fatigue (39.4%), hypothyroidism (21.2%) and diarrhea (18.2%). This real-world cohort demonstrates encouraging evidence of lenvatinib activity in metastatic NENs, even when used at lower doses than previously studied in NENs.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".