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Enregistrement W2146471206 · doi:10.1158/1078-0432.14aacriaslc-pr08

Abstract PR08: Pro-surfactant protein B as a biomarker for lung cancer prediction.

2014· article· en· W2146471206 sur OpenAlexaffabout
Don D. Sin, Martin C. Tammemägi, Stephen Lam, Matt J. Barnett, Xiaobo Duan, Anthony Raymond Tam, Heidi Auman, Ziding Feng, Gary E. Goodman, Samir Hanash, Ayumu Taguchi

Notice bibliographique

RevueClinical Cancer Research · 2014
Typearticle
Langueen
DomaineMedicine
ThématiqueChronic Obstructive Pulmonary Disease (COPD) Research
Établissements canadiensCanadian Centre for Applied Research in Cancer Control
Organismes subventionnairesnon disponible
Mots-clésLung cancerMedicineNational Lung Screening TrialLogistic regressionBiomarkerInternal medicineLung cancer screeningCancerOncologyLung

Résumé

récupéré en direct d'OpenAlex

Abstract Background: The National Lung Screening Trial reported that low-dose computed tomography (LDCT) screening reduced lung cancer mortality by 20% in adults at high risk for lung cancer. Despite these significant results, there are major concerns regarding lung cancer screening with LDCT. They include high false positivity, cost, and radiation exposure. Blood-based markers are a promising and attractive approach to complement LDCT because of the potential to identify those subjects that may be at increased risk of developing lung cancer, or that may be harboring early and potentially curable lung cancer, or that need to undergo further work-up for their indeterminate nodules. Our prior proteomic study suggested pro-surfactant protein B (SFTPB) as a promising circulating biomarker for non-small cell lung cancer (NSCLC). In this study we aimed to determine if plasma levels of pro-SFTPB are associated with lung cancer independently of known clinical risk factors, and improve lung cancer prediction beyond currently existing prediction models in individuals at high-risk for lung cancer at the screening setting. Methods: Pro-SFTPB levels were measured in 2,485 individuals, including 113 subjects later diagnosed as lung cancer, who enrolled in the Pan-Canadian Early Detection of Lung Cancer Study using plasma sample collected at the baseline visit. Multivariable logistic regression models were used to evaluate the predictive ability of pro-SFTPB in addition to known lung cancer risk factors. Calibration and discrimination were evaluated; the latter by an area under the receiver operator characteristics curve (AUC). Independent validation using a case-control study design was performed with serum samples collected in the Carotene and Retinol Efficacy Trial (CARET) participants consisting of 61 NSCLC subjects and matched 121 control subjects. Results: In the logistic model fully adjusted for lung cancer risk factors (age, sex, body mass index (BMI), personal history of cancer, family history of lung cancer, forced expiratory volume in 1 second percent predicted, average number of cigarettes smoked per day, and smoking duration), log-transformed pro-SFTPB (log-proSFTPB) was a significant independent predictor of lung cancer (odds ratio (OR) = 2.220, 95% confidence interval (CI) = 1.727-2.853, p < 0.001). The AUCs of the full model with and without pro-SFTPB were 0.741 (95% CI = 0.696-0.783) and 0.669 (95% CI = 0.620-0.717) (P value for difference in AUC = 0.0007). When the full model was estimated in 96 individuals with stage I or II lung cancer, log-proSFTPB remained a statistically significant predictor (OR = 2.195, 95%CI = 1.679-2.870; p < 0.001). In the CARET study, pro-SFTPB levels were significantly higher among NSCLC cases compared with controls (P < 0.0001) and ROC analysis yielded AUC of 0.683 (95% CI, 0.604-0.761). In multivariate logistic regression analysis, the risk of NSCLC increased along with the pro-SFTPB concentration gradient in the CARET set (Ptrend = 0.001, adjusted for matching variables, pack-years, years since quitting smoking, asbestos exposure, and BMI). Conclusions: Our study demonstrates that plasma pro-SFTPB is significantly and independently associated with lung cancer and adds to lung cancer prediction beyond those contributed by established risk factors in two independent cohorts. Furthermore, pro-SFTPB was associated with early stage lung cancer, suggesting its potential utility in predicting early staged NSCLC tumors, which may be amenable to surgical resection. This abstract is also presented as Poster A33. Citation Format: Don D. Sin, C Martin Tammemagi, Stephen Lam, Matt J. Barnett, Xiaobo Duan, Anthony Tam, Heidi Auman, Ziding Feng, Gary E. Goodman, Samir M. Hanash, Ayumu Taguchi. Pro-surfactant protein B as a biomarker for lung cancer prediction. [abstract]. In: Proceedings of the AACR-IASLC Joint Conference on Molecular Origins of Lung Cancer; 2014 Jan 6-9; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2014;20(2Suppl):Abstract nr PR08.

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 machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,002
score de la tête « metaresearch » (Gemma)0,007
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,029
Score d'incertitude au seuil0,059

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0020,007
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,000
Communication savante0,0010,000
Science ouverte0,0010,000
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0020,001

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.

Tête enseignante Opus0,164
Tête enseignante GPT0,520
Écart entre enseignants0,356 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2014
Routes d'admission2
Résumé présentoui

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