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.
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Diagnostic accuracy of the combined AI-assisted transvaginal ultrasound and multi-omics biomarker model for detection of endometriosis
Timeframe: At baseline diagnostic evaluation
Stefhanie Romero Andersson, MD, PhD, MSc