Pancreatic neuroendocrine tumors (pNET) in adolescent and young adult (AYA) population: A multi-institutional study of characteristics and outcomes.
Bibliographic record
Abstract
e15173 Background: Pancreatic neuroendocrine tumors (pNETS) are rare entity that represents 5-10% of all pancreatic tumors. To date, limited studies detail characteristics and outcomes of pNET in AYA pts (AYAp). A multi-institutional retrospective study was conducted to elucidate the aforementioned. Methods: AYAp (18-44 years old), diagnosed with pNETs at the Institut Paoli-Calmettes France, Segal Cancer Centre Canada and Military Medical Academy Bulgaria between Aug 2002 to Dec 2013. Pts characteristics and outcomes were pooled for analysis. Survival data used Kaplan Meier methodology. Results: 28 AYAp (median 37 yo, range 19-44, 19 female) diagnosed with pNETS were identified. 3 VHL disease or 1 MNN were reported. The most frequent presenting symptoms were: abdominal cramps (46%), weight loss (25%), diarrhea (10%) and flushing (3%). Seven pts (25%) had a functioning tumor and 1 secreting. 53% were already metastatic (met) at the diagnosis with liver predominance (70%). Tumors were well differentiated (D)(60%), moderately-D (4%) and poorly-D (7%) with 25% unknown; 75% grade 1 or 2, 65% Ki-67 rate between 3-20%. Primary tumor was resected in 17 pts (61%) with 4 further undergoing liver resection. Seven pts received adjuvant or neoadjuvant chemotherapy. 13 pts went on to receive palliative chemotherapy, mainly platinum-based, with 30% receiving it in combination with Sandostatine LR. Only 1 pt everolimus. 46% and 10% went on to receive 2nd and 3d line respectively. The median follow-up was 28.2 months (mo) (0.2-185). Survival data are presented in the Table. Conclusions: pNETs in AYAp are a heterogeneous and poorly understood disease with prognosis dependent on tumor biology, extensiveness of surgery and response to systemic treatments. Half of pts in our cohort had met disease at the diagnosis and were associated with poorer outcomes. With approval of tyrosine kinase inhibitors in 2011, their role in AYAp remain to be answered. Further international collaboration will be required. Median OS survival (mo) OS 1 year 2 year 5year Median DFS mo (range) Median PFS mo (range) Total n = 28 170 72% 58% 52% - - Non Met disease n = 13 ongoing - 42 (4-131) - Met disease n = 15 34.4 - - 13 (0.4-35)
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".