Abstract A010: Are We Delaying Ovarian Cancer Diagnosis in Young Women?: Sensitivity of CA-125 Thresholds for Early-Onset Ovarian cancer in Large Cohort
Bibliographic record
Abstract
Abstract Background: Cancer antigen 125 (CA-125) thresholds are used in diagnostic guidelines for ovarian cancer, especially around which patients need prompt referral to gynecologic oncology. Based on expert opinion, CA-125 ≥ 35 units/mL are considered abnormal for postmenopausal patients, while levels up to 250 units/mL are considered normal for younger patients. However, these thresholds have not been evaluated in modern practice. Our objective was to compare the sensitivity of different CA-125 thresholds for early-onset ovarian cancer. Methods: The University Institutional Review Board reviewed and exempted this study. We identified patients with newly-diagnosed ovarian cancer from the Penn Medicine Cancer Registry, 2009-2023 (n=2,785) supplemented with patients identified by ICD codes, 2020-2023 (n=1,342). CA-125 values obtained within 60 days of diagnosis were abstracted from the electronic health record. We examined the sensitivity of current CA-125 thresholds by patient age (<50, ≥50 years). Results: Among 1670 patients with ovarian cancer and CA-125 at diagnosis, the median CA-125 at diagnosis was 94.0 (range 29.0, 490.0) for patients with early-onset ovarian cancer (n=790) compared to 272.0 (range 53.0,1008.0) in women age 50 and older. At diagnosis, 30% patients with early-onset ovarian cancer had CA-125 <35 units/mL and 65% had CA-125 <250 units/mL. Among patients with early-onset high-grade serous ovarian cancer, 13% had CA-125 <35 units/mL at diagnosis. Lowering the “normal” CA-125 threshold to 15 units/mL achieved similar sensitivity among individuals <50 years (8.6%) and those 50 years and older (7.7%). Conclusion: Current thresholds of abnormal CA-125, and gynecologic oncology referral, miss individuals with early-onset ovarian cancer. Updated thresholds are needed to avoid delays in diagnosis, and the potential for cure, in younger individuals with ovarian cancer. Citation Format: Anna Jo B. Smith, Camille McAllister, Anne Marie McCarthy, Elizabeth A. Howell. Are We Delaying Ovarian Cancer Diagnosis in Young Women?: Sensitivity of CA-125 Thresholds for Early-Onset Ovarian cancer in Large Cohort [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 A010.
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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 | 0.006 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".