The Management and Outcomes of Patients with Extra-Pulmonary Neuroendocrine Neoplasms and Brain Metastases
Bibliographic record
Abstract
Background: Brain metastases (BMs) in patients with extra-pulmonary neuroendocrine neoplasms (EP–NENs) are rare, and limited clinical information is available. The aim of this study was to detail the clinicopathological features, management and outcomes in patients with EP–NENs who developed BMs. Methods: A retrospective single-centre analysis of consecutive patients with EP–NENs (August 2004–February 2020) was conducted. Median overall survival (OS)/survival from BMs diagnosis was estimated (Kaplan–Meier). Results: Of 730 patients, 17 (1.9%) had BMs, median age 61 years (range 15–77); 8 (53%) male, unknown primary NEN site: 40%. Patients with BMs had grade 3 (G3) EP–NENs 11 (73%), G2: 3 (20%), G1: 1 (7%). Eight (53%) had poorly differentiated NENs, 6 were well-differentiated and 1 was not recorded. Additionally, 2 (13%) patients had synchronous BMs at diagnosis, whilst 13 (87%) developed BMs metachronously. The relative risk of developing BMs was 7.48 in patients with G3 disease vs. G1 + G2 disease (p = 0.0001). Median time to the development of BMs after NEN diagnosis: 15.9 months (range 2.5–139.5). Five patients had a solitary BM, 12 had multiple BMs. Treatment of BMs were surgery (n = 3); radiotherapy (n = 5); 4: whole brain radiotherapy, 1: conformal radiotherapy (orbit). Nine (53%) had best supportive care. Median OS from NEN diagnosis was 23.6 months [95% CI 15.2–31.3]; median time to death from BMs diagnosis was 3.0 months [95% CI 0.0–8.3]. Conclusion: BMs in patients with EP–NENs are rare and of increased risk in G3 vs. G1 + G2 EP–NENs. Survival outcomes are poor, and a greater understanding is needed to improve therapeutic outcomes.
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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 | 0.000 |
| 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) | 0.001 | 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".