{"paper_id":"1b4eb7e1-ba2d-4333-87c9-89038a946e11","body_text":"Original Article  | JOGCR. 2023; 8(5): 481-487 \n     Volume 8, September – October 2023       Journal of Obstetrics, Gynecology and Cancer Research \n Journal of Obstetrics, Gynecology and Cancer Research | ISSN: 2476-5848 \n \nEndometriosis: Clinical, Magnetic Resonance Imaging and Pathologic \nFindings \n \nBehnaz Nouri1* , Maliheh Arab2 , Mohammad Nasiri3 \n \n1. Preventative Gynecology Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran \n2. Department of Gynecology -Oncology, Imam Hossein Medical Center, Shahid Beheshti University of Medical \nSciences, Tehran, Iran \n3. Department of Surgery, Rasoole Akram Hospital, Tehran, Iran \n \nArticle Info  ABSTRACT \n  \n          10.30699/jogcr.8.5.481 \n \n \n \nBackground & Objective: Endometriosis is one of the most common diseases in the \nfemale population. The range of diagnostic delays in this disease is long and leads to \nadverse health-related consequences. The aim of this study was to evaluate diagnostic \nexperiences in patients with endometriosis who are candidates for laparoscopic surgery. \nMaterials & Methods: This cross- sectional study was performed on 433 patients with \nendometriosis who were candidates for laparoscopic surgery referred to Shohada -\nTajrish Hospital in Tehran, Iran, between January 2016 and December 2021. A \nquestionnaire including demographic and clinical information, MRI, and pathology \nreports were collected from participants .\n The MRI lesions were segmented and the \nresults were compared with pathology and clinical examination. For statistical analysis \nSPSS software, version 22 was used. \nResults: A total of 433 participated in this study with a mean age of 34.18±7.99. The \naverage estimated duration of disease symptoms (months) was 40.58±42.33. The \npredictive value of clinical symptoms is weak compared to MRI. However, the \nprobability that the disease is not present when the clinical signs are negative is \nacceptable in most of the endometriosis sites. MRI considerably shows the true negative \nrate, but its sensitivity is only relatively acceptable for the diagnosis of ascites (67.66%). \nCalculating the accuracy of MRI reports probably shows the overall classification of \nthe patients via MRI test.  \nConclusion: despite extensive research, there are no suitable and accurate non -\ninvasive methods for diagnosing endometriosis. MRI and clinical examination alone \nare not useful for definitive diagnosis and it is better to examine biomarkers and \nartificial intelligence for non-invasive and accurate diagnosis of this disease. \nKeywords: Endometriosis, Magnetic  Resonance Imaging, Pathology, MRI  \nReceived:  2022/11/07; \nAccepted: 2022/12/19; \nPublished Online: 09 Sep 2023; \n \n \nUse your device to scan and read the \narticle online \n \n \nCorresponding Information:  \nBehnaz Nouri, \nDepartment of Obstetrics and Gynecology, \nShahid Beheshti University of Medical \nSciences, Tehran, Iran \n \nEmail: b.nouri1376@gmail.com  \n \n \n \nCopyright © 2023, This is an original open-access article distributed under the terms of the Creative Commons Attribution-noncommercial 4.0 International License \nwhich permits copy and redistribution of the material just in noncommercial usages with proper citation. \n \n \nIntroduction\nThe presence of endometrial -like tissue somewhere \nother than its original site, such as the pelvic \nperitoneum, rectovaginal septum, and ovaries, \nindicates a chronic inflammatory disease and is called \nendometriosis (1, 2) . According to studies, 5 -10% of \nreproductive-aged women and up to 50% of the \npopulation of infertile women may have endometriosis \n(3-6). Fatigue, chronic pelvic pain and stress are \nsymptoms of endometriosis, so the disease can affect \nwomen's mental, physical and social wellbeing (7). \nAccording to the mentioned symptoms, this disease can \nlead to a decrease in quality of life, infertility and \ndisruption of daily activities (8, 9) . Although many \npatients with endometriosis are asymptomatic, the \ndisease can cau se pain during intercourse and during \nmenstruation. Also, the need for medical treatments \nand extensive surgeries and their substantial risks and \nfinancial burden are other problems of this disease (10-\n12). The nature and cause of this disease are not exactly \nknown, but some factors such as genetics, ecology, \nimmune system, angiogenesis and endocrinology can \nbe involved in causing endometriosis (13). \nReasons that endometriosis is difficult to diagnose \ninclude the overlap between endometriosis -related \nchronic pelvic pain and other chronic pain, and the \npossibility of normal clinical findings in women with \nendometriosis (14, 15). Based on the results of studies, \nthe diagnostic delay range of the disease in many \nwomen is about 4 to 11 years (16 -19). Delay in th e \ndiagnosis of this disease can lead to long -term chronic \npain, reduced fertility and reduced quality of life in \npatients with endometriosis (20). Therefore, due to the \nhigh prevalence and risks of this disease, appropriate \n\n\nBehnaz Nouri et al. 482 \n      Volume 8, September – October 2023       Journal of Obstetrics, Gynecology and Cancer Research \nmethods should be used by physicians for rapid and \naccurate diagnosis of the disease. \nThe aim of this study was to evaluate the \ncharacteristics, demographic information and \ndiagnostic experiences (such as MRI, clinical and \npathologic finding) in patients with endometriosis who \nare candidates for laparoscopic surgery in Shohada -\nTajrish Hospital in Tehran. The use of appropriate \nclinical diagnostic techniques may reduce the delay in \ndiagnosis time and thus lead to faster patient relief, \nprevention of disease progression and its \nconsequences. \n \nMethods \nStudy Design and Population  \nThis cross- sectional study was performed on all \npatients with endometriosis which were candidates for \nlaparoscopic surgery and referred to Shohada-e-Tajrish \nHospital as a referral educational hospital in Tehran, \nthe capital of Iran, between January 2016 an d \nDecember 2021. \n \nQuestionnaire included age, weight, height, number \nof pregnancies, abortion, delivery, painful \nmenstruation, pelvic pain, pain during intercourse, pain \nduring defecation, intermenstrual spotting, and family \nhistory of Endometriosis, history of infertility, MRI \nand pathology report. The MRI lesions were segmented \nat the site of involvement and the extent and depth of \nthe involvement, and the results were compared with \npathology. The protocol of this study was approved by \nthe Ethics committee of Shahid Beheshti University of \nMedical Sciences (code: \nIR.SBMU.RETECH.REC.1398.346). All principles of \nHelsinki’s Declaration were met throughout the study \nprocesses. \nStatistical Analysis \nSPSS platform version 22 was used for statistical \nanalysis. Frequency and mean and standard deviation \nwere reported. Pearson correlation coefficient and t -\ntest was used for data analyzing at the significance \nlevel of 0.05. \n \nResults \nA total of 433 women were participated in this study \nwith a mean age of 34.18±7.99. The mean BMI of \nstudy participants was 24.76±4.30 (16- 40). Family \nhistory of endometriosis was reported in 7.4% of \npatients. The average estimated duration of disease \nsymptoms was 40.58±42.33 months. 72.1% of study \npopulation were married and 55% had at least 1 \npregnancy. 24% of all had never experienced hormone \ntherapy and 85.2% did not receive hormone therapy 3 \nmonths before the surgery (Table 1\n). \nFindings of patients’ evaluations via various \ndiagnostic tests are reported in a suppleme ntary file . \nThe values are described using frequency and \npercentage or mean± standard deviation as well as the \nrange. Part I reports the prevalence of clinical signs for \nthe various locations interested by endometriosis, and \nTopographic laparoscopic data are summarized in part \nII. The data about the endometriosis lesions was \ncollected for all the study patients who underwent \nlaparoscopic procedures. The highest incidence was \nfound in the peritoneum and ovaries. Finally, part III \nillustrates the diagnosis outcomes based on MRI. With \nthis diagnostic technique, the most prevalent \nobservations were cysts, followed by solid cystic \ntumors (52.7% of right cysts and 61.4% of left cysts) \nand based on MRI findings, 12% rectosigmoid \ninvolvement was diagnosed among patients. \nIn Table 2 , the diagnostic value of MRI reports is \ncompared with the pathologic findings, which is \nconsidered as the gold standard. Sensitivity declares \nthe true positive rate that is relatively acceptable only \nfor diagnosis of ascites (66.67%), while the specificity \nof MRI was high in all evaluated areas with values \nhigher than 70%. It shows that MRI considerably \nshows the true negative rate. The probability that the \ndisease be present when the MRI findings were \npositive was shown by positive predictive values which \nwere lower than 50% in all areas. Whereas, the \nnegative predictive values for MRI were higher than \n70%. Finally, the accuracy of MRI reports was \ncalculated, which shows the overall probability that a \npatient is correctly classified via MRI test. \nThe diagnostic value of clinical examination is \ncompared with MRI reports in T able 3. Results show \nthe weak predictive value of clinical signs in \ncomparison with MRI. However, according to the \nnegative predictive values in \nTable 3a, the probability \nthat the disease is not present when the clinical signs be \nnegative is acceptable in most of the endometriosis \nsites. The accuracy of clinical examination in \nclassifying the endometrio sis was low, except in the \narea of the vagina. \n \nTable1. Characteristics of the patients included in the study \nVariable  Frequency Percent \nAge 34.18±7.99 (14-55) \nBMI 24.76±4.30 (16-40) \nFamily history of endometriosis \nNo 401 92.6 \nYes 32 7.4 \n\n483 Endometriosis: Clinical and Paraclinical \n      Volume 8, September – October 2023       Journal of Obstetrics, Gynecology and Cancer Research \nVariable  Frequency Percent \nAge at disease onset 28.74±11.15 (0-53) \nDuration of symptoms (months) 40.58±42.33 (0-360) \nMarital status \nsingle 86 19.8 \nmarried 312 72.1 \ndivorced 24 5.5 \nIn relationship 11 2.3 \nNumber of pregnancies \n0 195 45.0 \n1 93 21.5 \n1< 145 33.4 \nNumber of abortions \n0 347 80.1 \n1 52 12.0 \n1< 34 7.8 \nDelivery type \nNone 219 50.6 \nNatural 86 19.9 \nCS 101 23.3 \nboth 27 6.2 \nHistory of hormone therapy \nno 104 24.0 \nyes 329 76.0 \nHormone therapy 3 months before \nsurgery \nno 369 85.2 \nyes 64 14.8 \n \n \nTable2. Diagnostic value of MRI in comparison with pathologic findings \n sensitivity specificity Positive \nPredictive Value \nNegative \nPredictive Value Accuracy \nHydrosalpinx 13.48% (7.17% to \n22.37%) \n89.53% (85.81% \nto 92.56%) \n25.00% (15.33% \nto 38.03%) \n80.00% (78.53% \nto 81.40%) \n73.90% (69.50% \nto 77.98%) \nNon-\nendometrioma cyst \n20.34% (13.49% \nto 28.73%) \n79.68% (74.81% \nto 83.99%) \n27.27% (19.79% \nto 36.31%) \n72.75% (70.58% \nto 74.82%) \n63.51% (58.78% \nto 68.05%) \ncul-de-sac \ninvolvement \n33.33% (0.84% to \n90.57%) \n95.12% (92.63% \nto 96.95%) \n4.55% (0.90% to \n19.93%) \n99.51 (98.92% to \n99.78%) \n94.69% (92.14% \nto 96.60%) \nUreteral \ninvolvement \n5.26% (0.13% to \n26.03%) \n94.69% (92.06% \nto 96.64%) \n4.35% (0.64% to \n24.22%) \n95.61% (95.13% \nto 96.04%) \n90.76% (87.63% \nto 93.32%) \nUterine lesions 41.77% (33.99% \nto 49.87%) \n73.82% (68.20% \nto 78.91%) \n47.83% (41.15% \nto 54.58%) \n68.81% (65.52% \nto 71.93%) \n62.12% (57.37% \nto 66.71%) \nDiaphragm lesions 20.00% (0.51% to \n71.64%) \n98.83% (97.29% \nto 99.62%) \n16.67% (2.75% to \n58.62%) \n99.06% (98.56% \nto 99.39%) \n97.92% (96.09% \nto 99.05%) \nAscites 66.67% (9.43% to \n99.16%) \n98.83% (97.30% \nto 99.62%) \n28.57% (10.92% \nto 56.63%) \n99.76% (98.84% \nto 99.95%) \n98.61% (97.00% \nto 99.49%) \nWall \nendometriosis \n6.67% (0.17% to \n31.95%) \n96.64% (94.43% \nto 98.15%) \n6.67% (0.99% to \n33.70%) \n96.64% (96.17% \nto 97.06%) \n93.52% (90.77% \nto 95.65%) \nIliac vascular \ninvolvement \n11.11% (0.28% to \n48.25%) \n99.53% (98.31% \nto 99.94%) \n33.33% (4.74% to \n83.41%) \n98.14% (97.67% \nto 98.52%) \n97.69 (95.79% to \n98.89%) \n*The vales in () show the 95% confidence interval for the estimated measures. \n \n\nBehnaz Nouri et al. 484 \n      Volume 8, September – October 2023       Journal of Obstetrics, Gynecology and Cancer Research \nTable3. Diagnostic value of MRI in comparison with clinical examination. \n sensitivity specificity Positive \nPredictive Value \nNegative \nPredictive Value Accuracy \nVaginal \ninvolvement \n0 (0.00% to \n84.19%) \n97.22% (95.19% \nto 98.55%) 0.00 99.52% (99.52% \nto 99.53%) \n96.77% (94.63% \nto 98.22%) \nCervical motility 42.86% (29.71% \nto 56.78%) \n53.05% (47.87% \nto 58.18%) \n11.94% (8.96% to \n15.75%) \n86.21% (83.01% \nto 88.88%) \n51.73% (46.91% \nto 56.53%) \ncul-de-sac \ninvolvement \n77.27% (4.63% to \n92.18%) \n31.14% (26.69% \nto 35.87%) \n5.67% (4.53% to \n7.07%) \n96.24% (92.12% \nto 98.25%) \n33.49% (29.05% \nto 38.15%) \nRight uterosacral \n \n67.24% (53.66% \nto 78.99%) \n40.53% (35.52% \nto 45.69%) \n14.89% (12.55% \nto 17.57%) \n88.89% (84.43% \nto 92.19%) \n44.11% (3 9.37% \nto 48.93%) \nLeft uterosacral \n \n73.53% (61.43% \nto 83.50%) \n37.53% (32.55% \nto 42.72%) \n17.99% (15.70% \nto 20.52%) \n88.39% (83.37% \nto 92.04%) \n43.19% (38.47% \nto 48.00%) \nRight adnexal \nadhesion \n57.14% (44.75% \nto 68.91%) \n49.04% (43.78% \nto 54.31%) \n17.78% (14.70% \nto 21.33%) \n85.58% (81.61% \nto 88.80%) \n50.35% (45.53% \nto 55.15%) \nLeft adnexal \nadhesion \n56.98% (45.85% \nto 67.61%) \n42.94% (37.67% \nto 48.33%) \n19.84% (16.78% \nto 23.30%) \n80.11% (75.42% \nto 84.09%) \n45.73% (40.96% \nto 50.55%) \nRight adnexal \nmass \n36.40% (30.15% \nto 43.01%) \n70.73% (63.99% \nto 76.86%) \n58.04 (51.28% to \n64.52%) \n50.0% (46.71% to \n53.29%) \n52.66% (47.83% \nto 57.44%) \nLeft adnexal mass 40.23% (34.28% \nto 46.39%) \n70.06% (62.50% \nto 76.89%) \n68.15% (61.93% \nto 73.79%) \n42.39% (39.02% \nto 45.84%) \n51.73% (46.91% \nto 56.53%) \nRectovaginal \nseptum \n45.0% (29.26% to \n61.51%) \n70.74% (65.97% \nto 75.19%) \n13.53% (9.71% to \n18.56%) \n92.67% (90.46% \nto 94.40%) \n68.36% (63.75% \nto 72.72%) \n*The vales in () show the 95% confidence interval for the estimated measures. \n \nDiscussion \nIn this cross -sectional study, 433 patients with \nendometriosis who were candidates for laparoscopic \nsurgery were selected to evaluate diagnostic \nexperiences (such as MRI, clinical examination, and \npathologic). The average estimated duration of disease \nsymptoms (months) was 40.58±42.33. With MRI \ntechnique, the most prevalent observations were cysts, \nfollowed by solid cystic tumors. The predictive value \nof clinical symptoms is weak co mpared to MRI. \nHowever, the probability that the disease is not present \nwhen the clinical signs be negative is acceptable in \nmost of the endometriosis sites. MRI considerably \nshows the true negative rate, but its sensitivity is only \nrelatively acceptable f or the diagnosis of ascites \n(67.66%). \nAccording to the results of the study of G.Hudelist et \nal., the average time interval from the onset of the first \nsymptoms to the diagnosis of the disease is 10.4±7.9 \nyears, while the time interval from the onset of the first \nsymptoms to seeking a physician is 2.3 years (21) . \nThese results clearly show that the rapid diagnosis of \nthis disease is a challenging problem all over the world. \nMRI can be used as the method of choice in the \ndiagnosis of suspected deep infiltrat ing endometriosis \n(22). Also, it should be noted that the results of some \narticles show that the use of MRI is useful for the \ndiagnosis of deep endometriosis (23-27). The results of \nthis study show that cysts are the most common \nobservation by MRI and the results of the study of \nKoninckx et al. (27) , confirm it. According to a study \nby Koninckx et al., MRI is accurate for detecting \novarian cysts, but it cannot detect small, delicate, and \nabnormal lesions (28). Nowadays, clinical diagnosis is \nperformed without standardization and a unified \napproach (3, 16) . Agarwal et al. have presented a \nunified approach and an algorithm for the clinical \ndiagnosis of endometriosis. This algorithm is easily \napplicable to most physicians and the most important \nbenefit of this algorithm is faster diagnosis of the \ndisease and initiation of treatment without delay and \ninvasive methods. In general, persistent pain and \nworsening of recurrent or persistent pelvic pain, along \nwith other symptoms associated with endometriosis, \nevaluation of the patient's history and clinical \nexamination findings may indicate endometriosis. If \nclinical findings are unclear, other options such as MRI \nmay be helpful for diagnosis (29).  \nUnfortunately, despite extensive research, there are \nno suitable and a ccurate non -invasive methods for \ndiagnosing endometriosis. The evaluations of this \nstudy show that MRI and clinical examination alone \nare not useful for definitive diagnosis and it is better to \nexamine artificial intelligence and biomarkers for non-\ninvasive and accurate diagnosis of this disease. For \nexample, the results of a cohort study show that CA -\n125, which is a glycoprotein with a high molecular \n\n485 Endometriosis: Clinical and Paraclinical \n      Volume 8, September – October 2023       Journal of Obstetrics, Gynecology and Cancer Research \nweight and is produced in the epithelium, can be a \nsuitable marker for the diagnosis and follow -up of \nendometriosis (30). The results of some studies also \nshow that artificial intelligence can be used as a \nscreening method by physicians in the future and \nreplace diagnostic laparoscopy (12, 31) . Also, \nincreasing public awareness about the symptoms of \nendometriosis, such as pain during menstruation, \nintercourse, and infertility, can lead to an early visit to \nthe doctor and a faster diagnosis of the disease. In \naddition to raising public awareness, training courses \nto improve the diagnostic skills of general practitioners \nand obstetricians can help diagnose the disease more \nquickly. \n \nAcknowledgments \nWe sincerely thank the women who participated in \nthe present study. We are also grateful to Deputy for \nResearch of Shahid Beheshti University of Medical \nSciences who supported us in this project. \n \nFound or Financial Support \nThe authors received no financial support for the \nresearch, authorship, and/or publication of this article. \n \nAuthors’ Contribution \nBN: project administration, study design, data \ncollection, editing, and review; MA: contributed in the \nstudy conduct, drafting the paper; MN: contributed in \ndrafting, editing the paper, and data collection. \n \nConflict of Interest \nThe authors declare that they have no conflict of \ninterest. \n \n \n \n \n1. Moazzami B, Chaichian S, Samie S, Zolbin MM, \nJesmi F, Akhlaghdoust M, et al. Does \nendometriosis increase susceptibility to COVID -\n19 infections? A case -control study in women of \nreproductive age. BMC Women's Health. 2021;  \n21(119):1-7. [ DOI:10.1186/s12905-021-01270-z] \n[PMID] [PMCID] \n2. Hickey M, Ballard K, Farquhar C. 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Sarbazi F, Akbari E, Karimi A, Nouri B, Noori \nArdebili SH. The Clinical Outcome of \nLaparoscopic Surgery for Endometriosis on Pain, \nOvarian Reserve, and Cancer Antigen 125 (CA -\n125): A Cohort Study. Int J Fertil Steril. 2021;  \n15(4):275-9. \n31. Nouri B, Roshandel S. Is Artificial Intelligence a \nNew Diagnostic  Approach for Patients with \nEndometriosis? Interv Pain Med Neuromod. 2022; \n2(1):e128720. [DOI:10.5812/ipmn-128720] \n \n \n \n\n487 Endometriosis: Clinical and Paraclinical \n      Volume 8, September – October 2023       Journal of Obstetrics, Gynecology and Cancer Research \n \nHow to Cite This Article:  \nNouri, B., Arab, M., Nasiri, M. Endometriosis: Clinical, Magnetic Resonance Imaging and Pathologic Findings . J \nObstet Gynecol Cancer Res. 2023; 8(5):481-7. \nDownload citation:                             RIS | EndNote | Mendeley |BibTeX |","source_license":"CC0","license_restricted":false}