Foretell Ovary: A Blood-Based Ensemble Algorithm Integrating Tumor-Educated Platelet RNA and Routine Hematology for...

Background: Ovarian cancer is the most lethal gynecologic malignancy, and CA-125 has limited early-stage sensitivity. We developed and evaluated Foretell Ovary, an ensemble machine-learning classifier that integrates tumor-educated-platelet RNA markers and routine hematologic parameters for non-invasive ovarian cancer detection, and compared it head-to-head with CA-125. Methods: A multi-site prospective cohort study enrolled 460 South Korean participants (67 ovarian cancer, 15 borderline ovarian tumors, 197 benign gynecological conditions, and 181 asymptomatic controls) at six clinical sites between September 2024 and March 2026. The cohort was split into training (n = 279) and independent test (n = 181) sets under a pre-specified ACTB cycle threshold ≤ 28 per-protocol rule (test n = 169). The classifier blended a random forest and two calibrated logistic-regression sub-models built on eight platelet RNA markers, 24 complete blood count parameters, and 30 marker-by-hematology combination features. Performance was assessed at a pre-frozen cutoff of 0.44 and compared against CA-125 (35 U/mL cutoff) in a matched subset (n = 100). Results: Foretell Ovary achieved an area under the receiver operating characteristic curve (AUC) of 0.923, sensitivity of 87.5% (100.0% for Stage II-IV), specificity of 89.0%, and negative predictive value of 97.7% in the test set. In the matched subset, Foretell Ovary outperformed CA-125 on every metric (AUC 0.839 vs. 0.723; sensitivity 85.0% vs. 65.0%), with the largest gap in pre-menopausal participants (+25 percentage points in sensitivity). A three-tier risk-grading scheme retained no ovarian cancer in the low-risk grade. Conclusions: Foretell Ovary offers more accurate non-invasive detection of ovarian cancer than CA-125, particularly in pre-menopausal women, offering the flexibility to be used alone or combined with CA-125. Prospective validation studies are warranted.