Abstract 2298: Is smoking a risk factor for second primary lung cancer
Notice bibliographique
Résumé
Abstract Background: Lung cancer (LC) survivors in the U.S. are increasing in number, with 5-year survival rates improving by 26% over the past decade. Although LC survivors are at high risk of developing a second primary lung cancer (SPLC), risk factors for SPLC have not been established and the impact of tobacco smoking remains controversial. In this study, we examined risk factors for SPLC among participants in the Multiethnic Cohort (MEC) study, validated our findings with two epidemiologic cohorts–the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial (PLCO) and the European Prospective Investigation into Cancer and Nutrition (EPIC)–and evaluated the impact of smoking cessation on SPLC risk. Methods: We analyzed data from 7,299 initial primary lung cancer (IPLC) cases in MEC who were diagnosed from 1993-2017. Incident IPLC and SPLC were identified via linkage to SEER registries, with SPLC defined by Martini and Melamed criteria. Baseline smoking data were obtained at the time of enrollment (1993-1996) and updated with 10-year follow-up data close to IPLC diagnosis, if available. Fine-Gray regression was used to take into account competing risks and to evaluate the associations between risk factors and SPLC, adjusting for age at IPLC diagnosis and IPLC histology and stage. We conducted validation studies with PLCO (N=3,423 LC patients) and EPIC (N=4,605 LC patients) and evaluated the combined effects of risk factors from all three cohorts using meta-analysis. Results: Among 7,299 MEC participants with IPLC, 167 (2.3%) developed a SPLC. Fine-Gray regression analyses identified several factors that were significantly associated with SPLC risk (P<0.05), which included smoking pack-years (HR 1.12 per 10 pack-years (PY); P=0.004) and smoking intensity (HR 1.21 per 10 cigarettes per day (CPD); P=0.017). Individuals who met the U.S. Preventative Services Task Force's (USPSTF) screening criteria (i.e., aged 55-80, smoked ≥30 PY, and ≤15 years since smoking cessation) at the time of IPLC had a 68% increase in SPLC risk (HR 1.68; P=0.001). Validation studies with PLCO and EPIC showed consistent results; the combined effects based on meta-analysis showed a HR 1.15 per 10 PY (Pmeta=0.022) for smoking pack-years, HR 1.18 per 10 CPD (Pmeta=6.0x10-4) for smoking intensity, and HR 1.70 (Pmeta = 1.9x10-5) for meeting the USPSTF criteria. Subset analysis of MEC participants (N=156) who were current smokers at baseline, had 10-year follow-up smoking data, and were diagnosed with IPLC between baseline and 10-year follow-up showed that smoking cessation was associated with a reduced risk of SPLC (HR=0.25; P=0.005). Conclusions: Smoking is a risk factor for SPLC among LC patients and the USPSTF criteria can potentially aid in identifying those at high risk of SPLC. Smoking cessation may reduce SPLC risk after IPLC diagnosis. Further analysis is required to stratify SPLC risk based on comprehensive risk factors and identify LC survivors at high risk of SPLC for CT screening. Citation Format: Jacqueline V. Aredo, Sophia J. Luo, Rebecca Gardner, Thomas P. Hickey, Thomas L. Riley, Lynne R. Wilkens, Loic Le Marchand, Christopher I. Amos, Rayjean J. Hung, Mattias Johansson, Iona Cheng, Heather A. Wakelee, Summer S. Han. Is smoking a risk factor for second primary lung cancer [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 2298.
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 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,036 | 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 ».