Abstract B018: Early-onset ovarian cancer in U.S. veterans: A National Cancer Database study
Notice bibliographique
Résumé
Abstract Objective Women account for nearly 10% of the U.S. veteran population, a figure expected to nearly double by 2040. Despite their growing numbers, little is known about ovarian cancer in veterans, a group with unique environmental exposures at young ages due to military service that include exposure to endocrine-disrupting chemicals (EDCs), burn pits, and volatile organic compounds (VOCs). Early-onset ovarian cancer, defined as diagnosis before age 40, is often associated with differences in non-epithelial histologies. Our objective was to characterize early-onset ovarian cancer in veterans and compare histology, stage, and treatment patterns with non-veterans using age-stratified analyses. Methods We conducted a retrospective cohort study using the National Cancer Database from 2004–2021. Veteran status was inferred from insurance type, with patients identified as veterans if covered under Veterans Affairs, Military, or TRICARE insurance. Patients with ovarian, peritoneal, and fallopian tube cancers were included. We used descriptive statistics to compare demographic, tumor, and treatment characteristics between veterans and non-veterans, stratified by age <40 and ≥40 years. Logistic regression models were used to evaluate associations between veteran status and histology, stage, and treatment timeliness, adjusting for demographic and clinical covariates. Results Among 353,045 individuals with ovarian cancer, 3,260 were veterans; 20,829 of the total were under 40 years of age, and 332,216 were over the age of 40. Veterans were younger at ovarian cancer diagnosis (median of 58 years vs. 63 years, p < 0.0001). In patients <40 years, veterans were significantly less likely to have epithelial tumors (66.0% vs. 71.3%, p = 0.04) and more likely to present with germ cell histology (25.5% vs. 20.1%, p = 0.02) than non-veterans. Rates of sex cord-stromal tumors were also slightly higher in veterans <40 (7.2% vs. 6.2%), though not statistically significant. In patients 40 and older, these histology differences largely disappeared; for example, germ cell tumors were rare in both veterans and non-veterans (0.3% vs. 0.4%, p = 0.59). Stage distribution did not differ significantly by veteran status in the <40 group, though across the overall cohort, veterans were modestly more likely to present with Stage I disease (21.6% vs. 19.6%, p = 0.048). Conclusion Veterans with early-onset ovarian cancer display distinct histologic patterns compared to non-veterans, including fewer epithelial and more germ cell tumors. These differences were specific to patients <40, with no significant variation among older patients. Despite histology differences, veterans did not experience treatment delays or disparities, in contrast to non-veterans. Early-onset ovarian cancer in veterans may be biologically distinct, emphasizing the need for research into exposures and outcomes in this unique group. Citation Format: Sanjana E. Kashyap, Xingmei Wang, Haley Moss, Leah Zullig, Anna Jo Smith. Early-onset ovarian cancer in U.S. veterans: A National Cancer Database study [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 B018.
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 | 0,001 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,006 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 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 ».