Abstract 6095: Lung cancer risk assessment with the INTEGRAL protein panel: Preliminary results from development and validation in the Lung Cancer Cohort Consortium
Notice bibliographique
Résumé
Abstract Background: In the NCI-funded Integrative Analysis of Lung Cancer Etiology and Risk (INTEGRAL) program, the Lung Cancer Cohort Consortium (LC3) recently identified 36 proteins associated with lung cancer risk in pre-diagnostic blood samples after evaluating 1,161 proteins in 731 case-control pairs (Nature Communications, 2023). Based on these data, we designed the INTEGRAL panel, which measures absolute concentrations of 21 proteins, to optimize lung cancer risk assessment for screening beyond prediction models (e.g. PLCOm2012) or categorical screening criteria (e.g. USPSTF-2021). Methods: To evaluate the performance of the INTEGRAL panel, we assayed blood from participants selected as a representative case-cohort sample from 14 LC3 cohorts, divided into development and validation sets (Annals of Epidemiology, 2023). Here, we present preliminary results from the 7 cohorts in the development set, including 807 lung cancer cases diagnosed within 3 years of blood draw and 1,144 sub-cohort representatives, who were weighted to represent all current and former smokers in their corresponding cohorts. We first fit a flexible parametric survival model in 4 cohorts (n=478 cases) including a subset of proteins and age, smoking duration, and smoking intensity. The model was subsequently evaluated in the remaining 3 cohorts (n=329 cases) and benchmarked against the PLCOm2012 risk score and USPSTF2021 screening criteria. Results: The preliminary model includes 4 proteins (CEACAM5, MMP12, SCF, LPL). Compared with the PLCOm2012 score, the model improved discrimination of future lung cancer for cases occurring over 3 years (AUC=0.81 vs. 0.76, p<0.0001) and over 1 year (AUC=0.86 vs. 0.78, p<0.0001). At the USPSTF-2021 specificity of 76%, the protein model increased sensitivity by an absolute difference of 14% over 3 years and 25% over 1 year compared to USPSTF-2021, and by 9% over 3 years and 17% over 1 year compared to the PLCOm2012 model. When screening the same number of participants as PLCOm2012, the protein model identified more future cases (223 vs 193), and the cases identified only by the protein model were predicted to gain more life-years from 3 LDCT screens compared with the PLCOm2012-identified cases (4.2 vs. 3.6 life-years). Conclusions: Preliminary results suggest that the INTEGRAL protein panel can improve risk discrimination beyond questionnaire-based risk prediction models, as well as identify future cases who have more life-years to gain from screening. Final validation is planned for completion in early 2024, by analysis of fully independent data from 7 additional LC3 cohorts (n=560 cases and 1,126 sub-cohort representatives). Citation Format: Hana Zahed, Karine Alcala, David C. Muller, Rayjean Hung, Mattias Johansson, Hilary A. Robbins, The Lung Cancer Cohort Consortium. Lung cancer risk assessment with the INTEGRAL protein panel: Preliminary results from development and validation in the Lung Cancer Cohort Consortium [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6095.
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 | 0,005 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| 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) | 0,001 | 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 ».