Streptozocin-based treatment in advanced neuroendocrine tumors (NETs): A single institution experience.
Bibliographic record
Abstract
e15179 Background: NETs represent a rare and miscellaneous group of tumors with a variable clinical activity. Streptozocin (STZ) is widely used in the treatment of advanced NETs, despite its controversial effectiveness and risk of toxicity. The aim of the study is to review Streptozocin-based treatment for advanced/metastatic NETs, its toxicities and its impact on progression free survival (PFS) and overall survival (OS). Methods: This retrospective study includes 22 pts treated with STZ for advanced NETs between 2006 – 2014 at CHUM. Kaplan-Meier analysis were used to evaluate PFS and OS of the entire group and of adrenal NETs vs. others. Results: The group included 12 women and 10 men with a median age of 49 years old. Twelve patients (pts) had the primary tumor coming from adrenals and 8 pts from pancreas. 14 pts presented initially with metastases (met); 50% located in the liver. The median time to develop met was 17 months. All pts had G1/G2 disease. The median number of cycles was 3.5, with concomitant doxorubicin in 46%. 32% started STZ in first intention and 68% received at least 1 previous treatment. Gastrointestinal toxicities were the most frequent ones; nausea and vomiting in 36% and diarrhea or constipation in 23%. There was no grade 3/4 hematologic toxicity. Proteinuria, hyperglycemia and elevated hepatic enzymes were found in < 20% of pts. Only 4 pts had acute renal failure (ARF); 3 of them were mild. One pt with massive hepatic metastases died of a tumor lysis syndrome and ARF. STZ was discontinued for disease progression in 55%, disease stabilisation in 18% and toxicity in 14%. PFS was 59 days overall; 51 days for adrenals NETs and 195 days for pancreatic NETs. Median OS was 41 months. Conclusions: STZ is mainly prescribed for advanced adrenal and pancreatic NETs, with no clear benefit on PFS. The toxicity profile and the presence of alternative agents should supplant its usage.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".