Abstract B018: Early-onset ovarian cancer in U.S. veterans: A National Cancer Database study
Bibliographic record
Abstract
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".