Behnaz Nouri - Department of Obstetrics and Gynecology, Shahid Beheshti University of Medical Sciences, Tehran, Iran
Maliheh Arab - Department of Gynecology-Oncology, Imam Hossein Medical Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
Mohammad Nasiri - Department of Surgery, Rasoole Akram Hospital, Tehran, Iran

Background & Objective: Endometriosis is one of the most common diseases in the female population. The range of diagnostic delays in this disease is long and leads to adverse health-related consequences. The aim of this study was to evaluate diagnostic experiences in patients with endometriosis who are candidates for laparoscopic surgery.Materials & Methods: This cross-sectional study was performed on ۴۳۳ patients with endometriosis who were candidates for laparoscopic surgery referred to Shohada-Tajrish Hospital in Tehran, Iran, between January ۲۰۱۶ and December ۲۰۲۱. A questionnaire including demographic and clinical information, MRI, and pathology reports were collected from participants. The MRI lesions were segmented and the results were compared with pathology and clinical examination. For statistical analysis SPSS software, version ۲۲ was used.Results: A total of ۴۳۳ participated in this study with a mean age of ۳۴.۱۸±۷.۹۹. The average estimated duration of disease symptoms (months) was ۴۰.۵۸±۴۲.۳۳. The predictive value of clinical symptoms is weak compared to MRI. However, the probability that the disease is not present when the clinical signs are negative is acceptable in most of the endometriosis sites. MRI considerably shows the true negative rate, but its sensitivity is only relatively acceptable for the diagnosis of ascites (۶۷.۶۶%). Calculating the accuracy of MRI reports probably shows the overall classification of the patients via MRI test.Conclusion: despite extensive research, there are no suitable and accurate non-invasive methods for diagnosing endometriosis. MRI and clinical examination alone are not useful for definitive diagnosis and it is better to examine biomarkers and artificial intelligence for non-invasive and accurate diagnosis of this disease.

Endometriosis, Magnetic resonance imaging, pathology, MRI