SUN-108 - Non-Invasive Blood-Based Detection of Endometriosis Has Potential to Improve Standard-of-Care Among Symptomatic Women

Endometriosis affects 5–10% of reproductive-age women and is a leading cause of infertility and chronic pelvic pain. Diagnosis is often delayed due to heterogeneous symptoms and limitations of imaging, with laparoscopic visualization remaining invasive and operator-dependent. We developed a non-invasive blood-based assay integrating three miRNAs, three proteins and one steroid serum biomarkers, quantified by qPCR and immunoassay, combined with random forest machine-learning to classify disease status. The training set comprised 218 participants (137 endometriosis-positive, 81 controls) and the test was validated on an independent cohort of 80 participants (40 positive, 40 controls). In this validation, the assay achieved 0.944 AUC, 0.80 sensitivity, and 0.975 specificity, demonstrating robust generalizability. Subgroup analysis by menstrual cycle phase confirmed consistent performance across phases: proliferative samples achieved 0.935 AUC, 0.767 sensitivity, and 0.962 specificity; secretory samples achieved 0.993 AUC, 0.9 sensitivity, and 1.0 specificity. Compared with imaging, the blood test identified 16 histologically confirmed positive cases (61.5% of true positives) missed by transvaginal ultrasound and/or MRI, highlighting its complementary diagnostic value in clinical settings. These results demonstrate that a minimally invasive molecularly informed blood test that can detect endometriosis across menstrual phases with high accuracy is not only feasible, but also can clinically aid in confirming the presence of endometriosis when suspicion of endometriosis exists. Ongoing validation in geographically and ethnically diverse populations aims to confirm broad clinical utility. This multi-omic assay represents a scalable diagnostic tool with the potential to shorten diagnostic delays, improve early detection, and guide timely intervention for women with endometriosis.

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