Noninvasive Blood-based Detection of Endometriosis Can Improve Standard-of-Care by Facilitating Early Diagnosis and Clinical Management among Symptomatic Women

STUDY OBJECTIVE: To develop and validate a non-invasive, blood-based diagnostic assay for endometriosis that performs accurately across menstrual cycle phases and complements existing imaging modalities. DESIGN: Multicenter case-control study with machine learning classification and independent cohort validation. SETTING: Clinical and search settings involving symptomatic women evaluated for suspected endometriosis. PATIENTS: A total of 298 reproductive-age women were included. The training cohort comprised 218 participants (137 endometriosis-positive and 81 controls). A modest, independent, and retrospective validation cohort included 80 participants (40 endometriosis-positive and 40 controls). INTERVENTIONS: Peripheral blood sampling with quantification of 3 microRNAs via qPCR, 3 protein biomarkers, one steroid hormone using immunoassay, as well as the participant's age and body mass index. Biomarker data were integrated using a random forest machine learning model to classify disease status. MEASUREMENTS AND MAIN RESULTS: In the independent validation cohort, the assay achieved an area under the curve (AUC) of 0.944, sensitivity of 0.80, and specificity of 0.975. Subgroup analysis by menstrual cycle phase demonstrated consistent performance: proliferative-phase samples achieved an AUC of 0.935, sensitivity of 0.767, and specificity of 0.962, while secretory-phase samples achieved an AUC of 0.993, sensitivity of 0.90, and specificity of 1.00. Compared with transvaginal ultrasound and/or MRI, the blood-based assay identified 61.5% histologically confirmed endometriosis cases that were missed by the imaging modalities. CONCLUSION: A minimally invasive, multi-omic blood-based assay integrating molecular biomarkers with machine learning can accurately detect endometriosis across menstrual cycle phases and provides complementary diagnostic values. This approach has the potential to improve early detection and guide timely clinical intervention. A prospective validation is ongoing in geographically and ethnically diverse populations to further assess its broad clinical utility.