Lung carcinoid tumours: histology and Ki‐67, the eternal rivalry
Bibliographic record
Abstract
WHO classification of Thoracic Tumours defines lung carcinoid tumours (LCTs) as well-differentiated neuroendocrine neoplasms (NENs) classified in low grade typical (TC) and intermediate grade atypical carcinoids (AC). Limited data exist concerning protein expression and morphologic factors able to predict disease aggressiveness. Though Ki-67 has proved to be a powerful diagnostic and prognostic factor for Gastro-entero-pancreatic NENs, its role in lung NENs is still debated. A retrospective series of 370 LCT from two oncology centers was centrally reviewed. Morphology and immunohistochemical markers (Ki-67, TTF-1, CD44, OTP, SSTR-2A, Ascl1, and p53) were studied and correlated with Overall Survival (OS), Cancer-specific survival (CSS) and Disease-free survival (DFS). Carcinoid histology was confirmed in 355 patients: 297 (83.7%) TC and 58 (16.3%) AC. Ki-67 at 3% was the best value in predicting DFS. Ki-67 ≥ 3% tumours were significantly associated with AC histology, stage III-IV, smoking, vascular invasion, tumour spread through air spaces OTP negativity, and TTF-1, Ascl1 and p53 positivity. After adjustment for center and period of diagnosis, both Ki-67 (≥3 versus <3) and histology (AC versus TC) alone significantly added prognostic information to OS and CSS multivariable model with age, stage and OTP; addition of both variables did not provide further prognostic information. Conversely, an improved significance of the DFS prediction model at multivariate analysis was seen by adding Ki-67 (≥3 versus <3, P adj = 0.01) to TC and AC histological distinction, age, lymph node involvement, residual tumour and OTP. Ki-67 ≥ 3% plays a potentially pivotal role in LCT prognosis, irrespective of histological grade.
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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 | 0.003 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".