Advanced Typical and Atypical Carcinoid Tumours of the Lung: Management Recommendations
Bibliographic record
Abstract
Background: Neuroendocrine tumours (nets) are classified by site of origin, with lung being the second most common primary site after the gastrointestinal tract. Lung nets are rare and heterogeneous, with varied pathologic and clinical features. Typical and atypical carcinoid tumours are low-grade lung nets which, compared with the more common high-grade nets, are associated with a more favourable prognosis. Still, optimal treatment strategies are lacking. Methods: This review concentrates on classification and treatment strategies for metastatic low-grade lung nets, considering both typical and atypical carcinoids. The terminology can be confusing, and an attempt is made to simplify it. Promising results from recent trials that included lung nets are presented and discussed. Finally, guidelines from Europe and North America are discussed, and differences are noted. Results: Even within the group of patients with low-grade nets, the presentation, the locations of metastasis, and the speed of progression can be very different. The initial work-up and an understanding of the tumour's biology are key in making management decisions. Various treatment options-including somatostatin analogs, peptide receptor radioligand therapy, and biologic systemic therapy, specifically with the mtor (mechanistic target of rapamycin) inhibitor everolimus-are now available and are presented in a treatment algorithm. Summary: Although lung nets are rare and evidence supporting optimal treatment strategies is lacking, the recent publication of trials that have included patients with lung nets advances evidence-based therapy for these tumours. Many variables have to be considered in managing these tumours that have received little attention. Education for treating physicians is needed.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".