Iranian Society of Gynecology Oncology

Document Type : Original Research Article


1 Preventative Gynecology Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran

2 Department of Gynecology-Oncology, Imam Hossein Medical Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran

3 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 433 patients with endometriosis who were candidates for laparoscopic surgery referred to Shohada-Tajrish Hospital in Tehran, Iran, between January 2016 and December 2021. 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 22 was used.
Results: A total of 433 participated in this study with a mean age of 34.18±7.99. The average estimated duration of disease symptoms (months) was 40.58±42.33. 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 (67.66%). 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.


Main Subjects

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