Abstract PR001: Risk prediction modeling for colorectal adenomas in persons under age 50: a risk-stratified approach to early onset colorectal cancer prevention
Notice bibliographique
Résumé
Abstract Background: As early-onset colorectal cancer (EOCRC) incidence rises, identifying colorectal adenomas (CRAs) under age 50 remains a key prevention strategy. We previously published a model to predict CRA risk in individuals under 50, achieving an area under the curve (AUC) of 0.71 (PMID: 39280910). Here, we validate this in a separate cohort. We also explore whether accelerated aging could be associated with CRA risk, as prior studies have suggested. PhenoAge, a proposed biological aging metric derived from clinical lab values and associated with chronic disease and mortality risk, may capture physiological decline not reflected in chronological age. Methods: Retrospective cohort study of adults under age 50 who had a colonoscopy between 2014-2024 for benign indications (excluding those with high-risk indications, alarm symptoms, or inadequate bowel preparation). The primary outcome was pathologically confirmed CRA. The previous model was applied without refitting. Performance was assessed using area under the curve (AUC), Hosmer–Lemeshow goodness-of-fit test, and diagnostic metrics (sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV)) at a pre-specified probability cutoff of 0.16. For the PhenoAge analysis, we included patients within the cohort who had available labs within 60 days of colonoscopy for the calculation of PhenoAge. We evaluated whether age acceleration (PhenoAge – chronological age) were different between persons with and without CRAs. Results: We identified 2874 persons who met inclusion criteria for inclusion in the validation cohort, median age 44 years, 230 were male (49.0%). Of these, 469 (16%) had at least 1 CRA. Applying the prior model, AUC was 0.67 (95% CI: 0.64–0.69). The overall sensitivity and specificity were 67.0% and 55.2%, respectively, with a PPV of 22.6% and a NPV of 89.5%. Performance differed across age groups: for participants under 45 years, sensitivity was 29.0% and specificity was 80.6%, while for the 45-49 age group, sensitivity was 85.7% and specificity was 21.6%. A total of 202 persons had labs available to calculate PhenoAge (median age 44 years, 32% male). Of these, 25 (12%) had >1 CRA. CRA group did not show statistically significant degrees of age acceleration (-0.3 years vs -2.8 years, p=0.12), but did show higher BMI (30 vs 26, p=0.01). There were no significant differences in race, ethnicity, marital status, or aspirin use between groups. Conclusion: External validation of our previously published risk prediction model demonstrates moderate discrimination, with higher sensitivity and precision for adults under age 45. Model refinement is needed to improve performance, but this shows excellent promise for a risk-stratified approach to EOCRC prevention in persons outside screening age. Given the median age of EOCRC diagnosis is 44, such strategies are essential. Biological age as measured by PhenoAge does not appear to improve model performance, but future studies should continue to explore risk factors and strategies for personalized EOCRC prevention. Citation Format: Ritika Modi, Ryan Hood, Divya Dasani, Catherine Blandon, Shria Kumar. Risk prediction modeling for colorectal adenomas in persons under age 50: a risk-stratified approach to early onset colorectal cancer prevention [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 PR001.
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 | 0,003 | 0,009 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 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 ».