Targeted Therapies Provide Treatment Options for Poorly Differentiated Pancreatic Neuroendocrine Carcinomas
Bibliographic record
Abstract
Poorly differentiated pancreatic neuroendocrine carcinoma (PD pNECs) is a rare disease that has a poor prognosis and is treated with systemic chemotherapy as the standard of care. We present 6 cases of chemo-naïve patients diagnosed with PD pNECs who refused systemic chemotherapy and received targeted therapies with sunitinib (37.5 mg/day, 5 patients) or the mammalian target of rapamycin (mTOR) inhibitor everolimus (10 mg/day, 1 patient) as the first-line treatment. We evaluated the drugs' toxicities and survival. The median age of the patients was 55 years (4 males, 2 females, functioning tumor in 1 of 6 patients). The median of the Ki67 index was 45% (range 20-80). Targeted therapies were combined with somatostatin analogues in 4 of 6 patients (30 mg Sandostatine LAR monthly). Toxicities (acute and late) were manageable and no toxicities necessitated cessation of treatment. All patients had progression-free survival during the 15-month treatment and an overall survival of more than 2 years after diagnosis. Even though this is a small cohort of selected patients, we conclude that sunitinib or everolimus are both feasible and safe and have encouraging results of efficacy as first-line therapies for PD pNEC.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.003 | 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".