Investigation of the relationship betweensymptoms, BMI, and endometrioticlesions according to the #ENZIAN classification

Endometriosis is a prevalent gynecological condition defined by the growth of endometriumlike tissue at ectopic sites outside the uterine cavity. It predominantly affects women of reproductive age and, alongside adenomyosis, is associated with major symptoms including various forms of pain, such as dysmenorrhea, dyspareunia, dysuria, and dyschezia, as well as infertility, all of which can severely impair quality of life. Despite its high prevalence, diagnosis is often delayed. To assess disease extent, several classification systems have been proposed. The recently introduced #ENZIAN score (2021) combines the revised American Society for Reproductive Medicine (rASRM) system with the ENZIAN classification, providing a comprehensive assessment of peritoneal, ovarian, and deep infiltrating lesions, as well as adenomyosis and tubo-ovarian adhesions. This cumulative thesis aims to bridge the gap between clinical presentation and intraoperative classification by employing the #ENZIAN system to systematically analyze associations between symptom profiles, lesion distribution, and body mass index (BMI) in endometriosis. Two retrospective studies were conducted: the first, based on 233 preoperative questionnaires, showed that while overall symptom severity did not differ across #ENZIAN-defined groups, specific symptoms such as dyschezia and dyspareunia were associated with distinct lesion localizations. The second, comprising 219 patients, revealed that BMI was associated with clinical outcomes but not with lesion distribution; normal-weight women reported higher pelvic pain intensity, while obese women had higher rates of infertility. Taken together, these findings indicate that while symptoms, potentially modulated by BMI, may aid in the identification of endometriosis, they do not reliably reflect its anatomical extent. Overall, this thesis underscores the value of integrating the #ENZIAN classification with metabolic and clinical parameters to refine diagnosis and guide personalized, multimodal treatment strategies beyond surgery and hormonal therapy.