Clinical management of typical and atypical carcinoids/neuroendocrine tumors in <scp>ENETS</scp> centres of excellence ( <scp>CoE</scp> ): Survey from the <scp>ENETS</scp> lung <scp>NET</scp> task force
Bibliographic record
Abstract
Lung carcinoid tumours are neuroendocrine neoplasms originating from the bronchopulmonary tract's neuroendocrine cells, accounting for only 1%-3% of all lung cancers but 30% of all neuroendocrine tumours. The incidence of lung carcinoids, both typical and atypical, has been increasing over the years due to improved diagnostic methods and increased awareness among clinicians and pathologists. The most recent WHO classification includes a subgroup of lung carcinoids with atypical morphology and higher mitotic count and/or Ki67 labelling index. Despite appropriate surgery, the 5-year survival rate for atypical carcinoids barely exceeds 50%-70%. The role of adjuvant therapy in lung carcinoids is not well-defined, and clinical decisions are generally based on the presence of high-risk features. Long-term follow-up is essential to monitor for recurrence, although the optimal follow-up protocol remains unclear. To address the lack of consensus in clinical management decisions, the European Neuroendocrine Tumor Society (ENETS) initiated a survey among 20 expert centres. The survey identified varied opinions on approaches to imaging, surgery, use of adjuvant therapy, and follow-up protocols. Notably, the absence of dedicated multidisciplinary lung neuroendocrine tumour boards in some centres was evident. Experts agreed on the need for a prospective adjuvant trial in high-risk patients, emphasizing the feasibility of such a study. In conclusion, the study highlights the need for a more uniform adoption of existing guidelines in the management of lung carcinoid tumours and emphasizes the importance of international collaboration to advance research and patient care. Close collaboration between healthcare providers and patients is vital for effective long-term surveillance and management of these rare tumours.
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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 | 0.002 | 0.007 |
| 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 | 0.001 | 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".