Endometriosis affects at least 1 in 10 women worldwide and is associated with a diagnostic delay of 5-10 years. Currently, definitive diagnosis requires invasive laparoscopy, which is costly and burdensome for patients.

This multicenter diagnostic validation study investigates a non-invasive laboratory test for the diagnosis of endometriosis based on the analysis of biomarkers from menstrual blood samples.

Study Objective:

The primary objective is to evaluate the diagnostic accuracy of a laboratory protocol for biomarker analysis from menstrual blood samples. Performance metrics including Area Under the Curve (AUC), sensitivity, specificity, negative predictive value (NPV), positive predictive value (PPV), positive likelihood ratio (LR+), and negative likelihood ratio (LR-) will be assessed by comparing women with confirmed endometriosis to confirmed controls.

Study Design:

This is a prospective multicenter diagnostic study. Menstrual blood samples are self-collected by participants.

Participants:

A total of 200 menstruating women aged 14-49 years will be enrolled across study sites. Participants are allocated to one of two cohorts.

Study Sites:

The study is conducted at multiple gynecological centers in Austria and Germany, with planned expansion to additional sites.

Sponsor: Diamens FlexCo, Linz, Austria

Expected Study Duration: 3 years

Inclusion criteria:

• Women aged 14 to 49

• Menstruating

• Endometriosis group

  • Histologically confirmed endometriosis
  • Imaging-verified endometriosis

• Laparoscopy-negative group:

  - Laparoscopy with no evidence of endometriosis

• Imaging-negative group:

  • Healthy women (no symptoms of endometriosis)
  • Imaging shows no evidence of endometriosis

Exclusion criteria:

• Amenorrhea
• Pregnancy
• Oncological diseases
• Use of GnRH agonists or antagonists

Participants will be recruited at gynecology departments and affiliated outpatient clinics in Austria and Germany, including university hospitals, regional hospitals, and private gynecology practices.

Individual participant data will not be shared because the dataset contains highly sensitive health information from a relatively small and potentially identifiable patient population with a stigmatizing condition.