Abstract A011: Early Onset Cancer Incidence: A Model for the Role of Diagnostic Advances
Notice bibliographique
Résumé
Abstract Background Recent increases in the diagnosis of certain cancers among younger individuals are generating intense concern. Many studies attribute the increase in early-onset cancer to an etiologic cause but questions have also arisen about the role of earlier diagnosis. Observed trends incidence reflect changes in the incidence of disease diagnosis, which is a product of the development of preclinical disease and its diagnosis after preclinical onset. Etiologic factors will generally relate to the onset of preclinical disease, but the interval from one to clinical diagnosis is also driven by diagnostic technologies and practices which have changed dramatically over the past several decades. In this study we use disease modeling to separate the risk of diagnosis into these two components to study the potential role of diagnostic changes in the observed early-onset incidence trends. Methods We simulate incidence trends from a natural history model that includes healthy, preclinical, and clinical disease states, where the transition from the healthy to the preclinical state represents disease onset and the transition from the preclinical to the clinical state represents diagnosis. We superimpose birth-cohort effects on the rate of disease onset and period effects on the rate of disease diagnosis to identify those that match patterns of incidence by age group and 5-year calendar interval from 2005 to 2019 relative to 2000-2004 for 6 cancers (colon, rectum, female breast, stomach, pancreas, and kidney) with increasing incidence in younger age groups. As a concrete example, we explore whether changes in sensitivity of breast cancer screening technologies between 2000 and 2019 may have contributed to increasing incidence of breast cancer diagnosis in women under 50. Results Two types of effects are broadly consistent with the observed increasing incidence trends in younger individuals: (1) a birth-cohort effect on disease onset that begins around 1970 and becomes more pronounced in later birth years or (2) a period effect consistent with progressive reduction over time in the interval from preclinical onset to diagnosis. An earlier, protective birth-cohort effect is consistent with recent declining trends in incidence in older individuals for colon, rectal, and stomach cancers. Conclusions A disease model provides clues about the possible drivers of cancer incidence trends, suggests constraints on the patterns of exposures that might be implicated etiologically, and indicates that the role of diagnostic changes warrants consideration alongside potential etiologic explanations. Thiswork prompts us to recommend replacing the term "early-onset cancer" with "early-diagnosed cancer" to clarify that the observed trends reflect changes in the incidence of cancer diagnosis. Citation Format: Ruth D. Etzioni, Lukas Owens. Early Onset Cancer Incidence: A Model for the Role of Diagnostic Advances [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: The Rise in Early-Onset Cancers—Knowledge Gaps and Research Opportunities; 2025 Dec 10-13; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(23_Suppl):Abstract nr A011.
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 | 0,002 | 0,005 |
| 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 | 0,000 | 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) | 0,000 | 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 ».