Everolimus in advanced, progressive, well‐differentiated, non‐functional neuroendocrine tumors: <scp>RADIANT</scp>‐4 lung subgroup analysis
Bibliographic record
Abstract
In the phase III RADIANT-4 study, everolimus improved median progression-free survival (PFS) by 7.1 months in patients with advanced, progressive, well-differentiated (grade 1 or grade 2), non-functional lung or gastrointestinal neuroendocrine tumors (NETs) vs placebo (hazard ratio, 0.48; 95% confidence interval [CI], 0.35-0.67; P < .00001). This exploratory analysis reports the outcomes of the subgroup of patients with lung NETs. In RADIANT-4, patients were randomized (2:1) to everolimus 10 mg/d or placebo, both with best supportive care. This is a post hoc analysis of the lung subgroup with PFS, by central radiology review, as the primary endpoint; secondary endpoints included objective response rate and safety measures. Ninety of the 302 patients enrolled in the study had primary lung NET (everolimus, n = 63; placebo, n = 27). Median PFS (95% CI) by central review was 9.2 (6.8-10.9) months in the everolimus arm vs 3.6 (1.9-5.1) months in the placebo arm (hazard ratio, 0.50; 95% CI, 0.28-0.88). More patients who received everolimus (58%) experienced tumor shrinkage compared with placebo (13%). Most frequently reported (≥5% incidence) grade 3-4 drug-related adverse events (everolimus vs. placebo) included stomatitis (11% vs. 0%), hyperglycemia (10% vs. 0%), and any infections (8% vs. 0%). In patients with advanced, progressive, well-differentiated, non-functional lung NET, treatment with everolimus was associated with a median PFS improvement of 5.6 months, with a safety profile similar to that of the overall RADIANT-4 cohort. These results support the use of everolimus in patients with advanced, non-functional lung NET. The trial is registered with ClinicalTrials.gov (no. NCT01524783).
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.008 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".