NON-INVASIVE DIAGNOSIS OF ENDOMETRIOSIS: EMERGING BIOMARKERS AND FUTURE PERSPECTIVES

Abstract

Endometriosis is a chronic, estrogen-dependent gynecological disorder characterized by the presence of endometrial-like tissue outside the uterine cavity, resulting in chronic pelvic pain, dysmenorrhea, dyspareunia, infertility, and a substantial reduction in quality of life. Affecting approximately 10% of women of reproductive age worldwide, the disease remains significantly underdiagnosed due to its heterogeneous clinical presentation and the lack of reliable, non-invasive diagnostic tools (Zondervan et al., 2020). Currently, laparoscopic visualization with histopathological confirmation is considered the diagnostic gold standard; however, its invasive nature, high cost, surgical risks, and prolonged diagnostic delay underscore the urgent need for accurate, accessible, and non-invasive alternatives.

Recent advances in molecular biology, genomics, proteomics, metabolomics, and artificial intelligence-assisted bioinformatics have accelerated the discovery of novel biomarkers capable of facilitating earlier disease detection. Blood-based biomarkers, including cancer antigen 125 (CA-125), inflammatory cytokines, circulating microRNAs (miRNAs), long non-coding RNAs (lncRNAs), extracellular vesicles, and cell-free nucleic acids, have demonstrated varying degrees of diagnostic potential. Similarly, menstrual fluid, saliva, urine, and endometrial tissue-derived biomarkers are emerging as promising, minimally invasive sources for disease identification. Advances in high-throughput multi-omics technologies have further improved the understanding of the complex molecular mechanisms underlying endometriosis, enabling the identification of biomarker panels with greater diagnostic accuracy than single-marker approaches (Chapron et al., 2019; Taylor et al., 2021).

In parallel, modern imaging modalities, including high-resolution transvaginal ultrasonography and magnetic resonance imaging (MRI), continue to enhance the non-invasive assessment of deep infiltrating endometriosis. Nevertheless, imaging alone may fail to detect superficial peritoneal lesions or early-stage disease, highlighting the importance of integrating imaging findings with molecular biomarkers. Emerging machine learning-based diagnostic models capable of analyzing multidimensional clinical and molecular datasets offer additional opportunities to improve diagnostic precision and reduce dependence on invasive surgical procedures (Becker et al., 2022).

Despite encouraging progress, several challenges hinder the clinical translation of candidate biomarkers, including limited reproducibility, heterogeneous study populations, small sample sizes, lack of standardized laboratory protocols, and insufficient multicenter validation. Future research should prioritize large-scale prospective studies, standardized biomarker validation frameworks, and the integration of multi-omics data with advanced computational approaches to establish robust, clinically applicable diagnostic algorithms. Ultimately, the development of reliable non-invasive diagnostic strategies has the potential to shorten diagnostic delays, facilitate earlier therapeutic intervention, improve fertility outcomes, reduce healthcare costs, and enhance the overall quality of life for women affected by endometriosis.