Use of alkylating chemotherapy in high-grade neuroendocrine tumours: Evaluation of real-world data.
Bibliographic record
Abstract
78 Background: High-grade neuroendocrine tumours (NETs) are believed to have activity to certain alkylating agents, although data are scant. These regimens include streptozotocin (STZ) (used in combination with doxorubicin or 5-fluorouracil) and dacarbazine (DTIC). Current series report variable responses between 6 – 69%. Our objective was to evaluate our real world data to better understand treatment decision-making and clinical outcomes with alkylating agents in advanced high-grade NETs. Methods: We reviewed the medical records of 36 patients with metastatic NETs who received alkylating systemic chemotherapy with either a DTIC regimen (n = 15) or STZ based regimen (n = 21). Patient cases were evaluated for age, time to treatment failure (TTF), time to progression (TTP Results: Among 36 patients treated, the predominant primary NET was pancreas (n = 28) with a median age at treatment of 61.9 years. Observed TTF was similar with both regimens (STZ: 3 months and DTIC: 4 months), however there was prolonged TTP of 11 months with STZ vs. 5.3 months with DTIC (p = 0.047). There was no significant difference in OS with a mean of 48.7 months (DTIC) vs. 47.6 months (STZ) (p = 0.47). Baseline progression at treatment initiation was higher in DTIC at 77% versus 57% in STZ. The predominant cause of treatment discontinuation in both groups was progressive disease; DTIC (71%) versus STZ (42%). Toxicity resulted in treatment discontinuation in 19% for STZ vs 7% for DTIC. Other causes of treatment cessation were completion of the intended treatment. Conclusions: In the groups evaluated, STZ containing regimens demonstrated prolonged PFS in comparison to DTIC, but there was no difference in OS between the two groups. Additionally, despite STZ appearing to have an increased toxicity rate, the rate of cessation between the groups was similar. This real world evaluation suggests similar efficacy with improved tolerability of DTIC based chemotherapy as a potential alternative to other alkylating agents.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".