Endometriosis is a prevalent gynecological condition affecting approximately 10% of women of reproductive age worldwide. It presents with nonspecific but often severe symptoms, including chronic pelvic pain (in 70% of cases) and infertility (in up to 40% of cases), imposing significant physical, psychological, economic, and societal burdens. Despite its widespread occurrence, the exact etiology and pathogenesis of endometriosis remain unclear, and no definitive cure exists. Early diagnosis and management are crucial for improving patient outcomes; however, major diagnostic delays persist. Current imaging techniques such as transvaginal ultrasound (TVUS) examination and magnetic resonance imaging, along with biochemical markers lack sufficient specificity. Consequently, confirmation of diagnosis still requires surgical procedures under general anesthesia, i.e.
laparoscopy ("key-hole surgery") and tissue biopsy. This delay exacerbates the disease burden and healthcare costs, underscoring the urgent need for non-invasive, precise diagnostic strategies. This project proposes a multi-modal approach integrating advanced ultrasound imaging with novel biomarkers identified via comprehensive multi-omics analyses, including proteomics, transcriptomics, and immune profiling, of patient-derived endometrial organoids. It aims to understand the underlying mechanisms of reduced endometrial receptivity of embryos in patients with endometriosis. Additionally, we will explore personalized treatment strategies by utilizing patient-specific organoids for drug screening and evaluation of treatment response.
This project aims to develop a non-invasive diagnostic strategy by integrating:
1. AI-enhanced TVUS for improved lesion detection. 2. Multi-omics biomarker discovery through proteomics, transcriptomics, and immune profiling. 3. Underpinning the mechanisms of reduced endometrial receptivity in endometriosis using an in vitro model of embryo-endometrium interaction. 4. Endometrial organoid models to enable precision medicine-based drug testing. The development of a reliable noninvasive or minimally invasive diagnostic test-or a combination of tests-could revolutionize the diagnostic pathway by reducing delays, avoiding the need for surgery, and facilitating disease monitoring and treatment evaluation.
Women aged 18-45
Understands and speaks the Swedish language
Suspected or confirmed endometriosis
Do not have a uterus
Women aged 18-45 years with suspected or confirmed endometriosis that visit the Endometriosis Centre at the Gynaecology Department at Karolinska University Hospital (KUS), Huddinge
Cohort
Prospective
Non-invasive diagnosis of endometriosis
Diagnostic testDiagnosis using AI-enhanced transvaginal ultrasound, multi-omics, and organoids
Diagnostic accuracy of the combined AI-assisted transvaginal ultrasound and multi-omics biomarker model for detection of endometriosis
Sensitivity and specificity of a combined diagnostic model integrating AI-assisted interpretation of transvaginal ultrasound images with plasma, saliva, urine, and endometrial-derived biomarkers for the detection of endometriosis, using laparoscopic diagnosis with or without histological confirmation as the reference standard.
Diagnostic accuracy of AI-assisted transvaginal ultrasound alone
To evaluate the performance of AI-assisted transvaginal ultrasound (TVUS) for detection and classification of endometriosis lesions.
Diagnostic performance of plasma and endometrial biomarker panel
To assess the ability of plasma and endometrial-derived biomarkers to detect endometriosis, disease stage, and correlate to clinical symptoms.
Implantation rate in in vitro endometrial models
To compare the attachment rate of embryo-derived or stem cell-derived embryonic structures (blastoids) to endometrial tissue from women with and without endometriosis.
Molecular characteristics associated with embryo/blastoid attachment
To evaluate transcriptomic and hormonal differences between successfully and unsuccessfully attaching embryos or blastoids.
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Region Stockholm
Lead sponsor
Karolinska Institutet
Collaborator
This trial is not recruiting at the moment. You can still explore other options:
