Temozolomide in grade III neuroendocrine neoplasms (G3 NENs): A multicenter retrospective review.
Bibliographic record
Abstract
321 Background: G3 NENs are aggressive, and optimal systemic treatment is unclear. Temozolomide (TEM)-based regimens have been used to treat grade 1-2 NETs, but their efficacy in G3 NENs (Ki-67 > 20%) remains undetermined. Aims: To assess the clinical efficacy of TEM-containing regimens in advanced grade III gastroenteropancreatic NENs (GEPNENs). Methods: A multicentre retrospective review (2008-2017) of patients with metastatic/unresectable G3 GEPNENs who received a TEM-containing regimen. The primary endpoint was time to treatment failure (TTF). Radiologic response was extracted from local reports without formal RECIST criteria. Results: 118 patients in six centers were included (median age 55, 65% male, 15% functional, 75% pancreatic NEN). 57% were well-differentiated, 35% poorly-differentiated, and 18% unknown based on local pathology reports. The regimen used was CAPTEM in 93% and TEM in 7%. Best radiological responses were: complete response (1%), partial response (39%), stable disease (22%), progressive disease (31%), unknown (7%) not by RECIST. Median TTF was 150 days and median overall survival (OS) 18.0 months. Fifteen patients (14%) required dose reductions/discontinuation due to adverse events. TTF was shorter for patients on TEM alone (p = 0.02, Table 1). Well-differentiated NENs had better response rate (52% vs 26%, p = 0.02) and overall survival (30.1 vs 12.0 mo, p = 0.008) compared to poorly-differentiated NEN. Conclusions: This is the largest TEM treatment series in G3 NEN, involving collaboration of several major North American NET centers. 40% of patients showed some degree of response, and treatment was generally well-tolerated. TEM-based regimens should be considered a viable treatment option in this setting. Prospective confirmatory trials (such as EA2142) may face difficulties in accrual due to disease rarity. [Table: see text]
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".