Treatment of advanced BP-NETS with lanreotide autogel/depot vs placebo: the phase III SPINET study
Bibliographic record
Abstract
Prospective data are lacking on early somatostatin analog (SSA) therapy in bronchopulmonary neuroendocrine tumors (BP-NETs; typical carcinoids and atypical carcinoids (TCs and ACs)). SPINET (EudraCT: 2015-004992-62; NCT02683941) was a phase III, double-blind study of lanreotide autogel/depot (LAN; 120 mg every 28 days) plus best supportive care (BSC) vs placebo plus BSC, with an optional open-label treatment phase (LAN plus BSC). Patients had metastatic/unresectable, somatostatin receptor (SSTR)-positive TCs or ACs. Recruitment was stopped early owing to slow accrual; eligible patients from the double-blind phase transitioned to open-label LAN. The adapted primary endpoint was progression-free survival (PFS) during either phase for patients receiving LAN. Seventy-seven patients were randomized (LAN, n = 51 (TCs, n = 29; ACs, n = 22); placebo, n = 26 (TCs, n = 16; ACs, n = 10)). Median (95% CI) PFS during double-blind and open-label phases in patients receiving LAN was 16.6 (11.3; 21.9) months overall (primary endpoint), 21.9 (12.8, not calculable (NC)) months in TCs, and 13.8 (5.4; 16.6) months in ACs. During double-blind treatment, median (95% CI) PFS was 16.6 (11.3; 21.9) months for LAN vs 13.6 (8.3; NC) months for placebo (not significant); corresponding values were 21.9 (13.8; NC) and 13.9 (13.4; NC) months, respectively, in TCs and 13.8 (5.4; 16.6) and 11.0 (2.8; 16.9) months, respectively, in ACs. Patients' quality of life did not deteriorate and LAN was well tolerated. Although recruitment stopped early and the predefined sample size was not met, SPINET is the largest prospective study to date of SSA therapy in SSTR-positive TCs and ACs and suggests clinical benefit in TCs.
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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) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".