Contemporary Incidence and Survival of Lung Neuroendocrine Neoplasms
Notice bibliographique
Résumé
Importance: While the epidemiology of overall and gastrointestinal neuroendocrine neoplasms (NENs) has been reported, data specific to lung NENs remain scarce. Objective: To examine the incidence, overall survival (OS), and lung cancer-specific death for lung NENs. Design, Setting, and Participants: Population-based retrospective cohort study in Ontario, Canada, of adult patients with incident lung NENs from 2000 to 2020. Data were analyzed from July to December 2024. Main outcomes and measures: Yearly incidence rates of lung NENs. OS examined with Kaplan-Meier curves and Cox regression models. Lung cancer-specific deaths using cumulative incidence function and Fine-Gray models accounting for the competing risk of death from other causes. Results: Among 4479 total patients, the median (IQR) age at diagnosis was 67 (57-74) years, and 2521 (56.3%) were female; 2056 (45.9%) had typical neuroendocrine tumors (NET), 370 (8.3%) atypical NET, 998 (22.3%) large cell neuroendocrine carcinoma (NEC, including small cell and mixed NEC), and 1055 (23.6%) other NEC, as well as 1103 (24.6%) who presented as stage IV. The incidence of lung NENs increased 2.87-fold from 0.87 to 2.50 per 100 000 from 2000 to 2020. This rise in incidence was observed mostly for typical NET (from 0.51 to 1.09) and for stage I (0.68 to 1.18). With a median (IQR) follow-up of 34 (9-87) months, 5- and 10-year OS were 50% (95% CI, 49%-51%) and 40% (95% CI, 39%-41%) overall. Advancing age, lower socioeconomic status, type of lung NEN, and advancing stage were independently associated with inferior OS. Cumulative incidence of lung cancer-specific deaths was 41% (95% CI, 40%-42%) at 5 years and 46% (95% CI, 45%-47%) at 10 years. Advancing age, type of lung NEN, and increasing stage were independently associated with higher hazards of lung cancer-specific deaths. Lung cancer-specific deaths were exceeded by deaths from other causes starting 2 year after diagnosis for typical NET and 3 years after diagnosis for stage I disease. Conclusions and relevance: The incidence of lung NENs has increased over 20 years, mostly associated with stage I disease. Prolonged OS was observed after lung NEN diagnosis. Patients with typical lung NET and stage I disease were more likely to die of causes other than lung cancer after 1 and 3 years, respectively. These data are important to direct efforts in care, research, and patient counseling.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».