Abstract PR17: Comprehensive colorectal cancer risk prediction to inform personalized screening and intervention
Notice bibliographique
Résumé
Abstract Background and Aims: Colorectal cancer (CRC) is one of the most preventable and treatable cancers when detected early via screening. The current screening guidelines for CRC recommend exams only based on age, family history, and previous screening results. Multiple environmental and lifestyle risk factors, however, have been established or suspected for CRC, as have many common genetic susceptibility loci. It is critical to utilize this information to better stratify individuals into low- and high-risk groups for optimized and personalized screening and intervention recommendations. Methods: Using data from two large consortia (8421 CRC cases and 9767 controls): the Genetics and Epidemiology of Colorectal Cancer Consortium (GECCO) and the Colorectal Transdisciplinary study (CORECT), we developed risk prediction models for men and women based on family history, environmental and lifestyle risk factors, and known CRC susceptibility loci identified through genome-wide association studies. We constructed an environmental risk score (E-score) as a weighted sum of 19 established or potential environmental and lifestyle risk factors for CRC with weights obtained from a multivariate logistic regression analysis. Similarly, we also constructed a genetic risk score (G-score) using 64 common variants associated with CRC risk. We evaluated the discriminatory accuracy of risk prediction models by calculating the area under the Receiver Operating Characteristic curve (AUC), correcting for potential overestimating by using the training data set. Our models also estimate absolute risk of developing CRC given various risk profiles, and provide recommended ages for the first endoscopic screening exam. Results: Both the E-score and the G-score are independent predictors of CRC risk, and models that incorporate both scores improve the discriminatory accuracy significantly compared to family history-only models. Compared to the model that includes only family history, the E-score significantly improves the discriminatory accuracy for both men (AUC = 0.62 vs. 0.53, p-value < 1e-5 ) and women (AUC = 0.60 vs. 0.52, p-value < 1e-5 ). The G-score also significantly improves the discriminatory accuracy for both men (AUC = 0.60 vs. 0.53, p-value < 1e-5 ) and women (AUC = 0.60 vs. 0.52, p-value < 1e-5 ) over the family history-only model. Compared to the model with family history and E-score, the inclusion of the G-score in the model further improves the discriminatory accuracy for both men (AUC = 0.65 vs. 0.62, p-value = 0.0152) and women (AUC = 0.63 vs. 0.60, p-value = 0.0005). Based on the 10-year risk estimates of developing CRC, the difference in recommended age to start screening for the top 90% and the bottom 10% of risk score ranges from 12 to 14 years depending on sex and status of CRC family history. Conclusions: Our risk prediction models incorporating both comprehensive environmental and lifestyle risk factors, and known CRC common genetic variants provide more accurate estimation of CRC risk. These models will be useful for recommending individually tailored screening and intervention strategies to prevent this common cancer. This abstract is also being presented as Poster B17. Citation Format: Jihyoun Jeon, Sonja I. Berndt, Hermann Brenner, Peter T. Campbell, Andrew T. Chan, Jenny Chang-Claude, Mengmeng Du, Graham Giles, Jian Gong, Stephen B. Gruber, Tabitha A. Harrison, Michael Hoffmeister, Loic LeMarchand, Li Li, John D. Potter, Gad Rennert, Robert E. Schoen, Martha L. Slattery, Emily White, Michael O. Woods, Ulrike Peters, Li Hsu. Comprehensive colorectal cancer risk prediction to inform personalized screening and intervention. [abstract]. In: Proceedings of the AACR Special Conference: Improving Cancer Risk Prediction for Prevention and Early Detection; Nov 16-19, 2016; Orlando, FL. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2017;26(5 Suppl):Abstract nr PR17.
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 enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,014 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,001 |
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.
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 source (Gemma direct ou Codex distillé), 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 ».