Molecular Pathology of Well-Differentiated Pulmonary and Thymic Neuroendocrine Tumors: What Do Pathologists Need to Know?
Bibliographic record
Abstract
Thoracic (pulmonary and thymic) neuroendocrine tumors are well-differentiated epithelial neuroendocrine neoplasms that are classified into typical and atypical carcinoid tumors based on mitotic index cut offs and presence or absence of necrosis. This classification scheme is of great prognostic value but designed for surgical specimens, only. Deep molecular characterization of thoracic neuroendocrine tumors highlighted their difference with neuroendocrine carcinomas. Neuroendocrine tumors of the lung are characterized by a low mutational burden, and a high prevalence of mutations in chromatin remodeling and histone modification-related genes, whereas mutations in genes frequently altered in neuroendocrine carcinomas are rare. Molecular profiling divided thymic neuroendocrine tumors into three clusters with distinct clinical outcomes and characterized by a different average of copy number instability. Moreover, integrated histopathological, molecular and clinical evidence supports the existence of a grey zone category between neuroendocrine tumors (carcinoid tumors) and neuroendocrine carcinomas. Indeed, cases with well differentiated morphology but mitotic/Ki-67 indexes close to neuroendocrine carcinomas have been increasingly recognized. These are characterized by specific molecular profiles and have an aggressive clinical behavior. Finally, thoracic neuroendocrine tumors may arise in the background of genetic susceptibility, being MEN1 syndrome the well-defined familial form. However, pathologists should be aware of rarer germline variants that are associated with the concurrence of neuroendocrine tumors of the lung or their precursors (such as DIPNECH) with other neoplasms, including but not limited to breast carcinomas. Therefore, genetic counseling for all young patients with thoracic neuroendocrine neoplasia and/or any patient with pathological evidence of neuroendocrine cell hyperplasia-to-neoplasia progression sequence or multifocal disease should be considered.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.014 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".