Endometriosis classification, staging and reporting systems: a review on the road to a universally accepted endometriosis classification

Association between Ovarian Endometriomas and Stage of Endometriosis

Objectives: To determine the association between ovarian endometriomas and stage of endometriosis. Methods: A total of 222 women aged 18-55 years old, who underwent minimally invasive surgery between January 2016 and December 2021 for treatment of endometriosis were included in the study. Patients underwent laparoscopic and/or robotic treatment of endometriosis by a single surgeon (FRN) and were staged using the ASRM revised classification of endometriosis. Pre-operative imaging studies, and operative and pathology reports were reviewed for the presence of endometriomas and the final stage of endometriosis. Using univariate analyses for categorical variables and the two-sample t-test or Mann-Whitney test for continuous data, association between endometriomas, stage of endometriosis, type of endometrioma, and other patient parameters such as age, gravidity, parity, laterality of endometriomas, prior medical treatment, and indication for surgery was analyzed. Results: Of the 222 patients included in the study, 86 patients had endometrioma(s) and were found to have stage III-IV disease. All 36 patients with bilateral endometriomas and 70% of patients with unilateral endometriomas had stage IV disease. Conclusions: The presence of ovarian endometrioma(s) indicates a higher stage of disease, correlating most often with stage IV endometriosis. Understanding the association between endometriomas and anticipated stage of disease can aid in appropriate pre-operative planning and patient counseling.

Non-invasive imaging techniques for diagnosis of pelvic deep endometriosis and endometriosis classification systems: an International Consensus Statement†,‡

The International Society of Ultrasound in Obstetrics and Gynecology (ISUOG) and International Deep Endometriosis Analysis (IDEA) group, the European Endometriosis League (EEL), the European Society for Gynaecological Endoscopy (ESGE), the European Society of Human Reproduction and Embryology (ESHRE), the International Society for Gynecologic Endoscopy (ISGE), the American Association of Gynecologic Laparoscopists (AAGL) and the European Society of Urogenital Radiology (ESUR) elected an international, multidisciplinary panel of gynecological surgeons, sonographers and radiologists, including a steering committee, which searched the literature for relevant articles in order to review the literature and provide evidence-based and clinically relevant statements on the use of imaging techniques for non-invasive diagnosis and classification of pelvic deep endometriosis. Preliminary statements were drafted based on a review of the relevant literature. Following two rounds of revisions and voting orchestrated by chairs of the participating societies, consensus statements were finalized. A final version of the document was then resubmitted to the society chairs for approval. Twenty statements were drafted, of which 14 reached strong and three moderate agreement after the first voting round. The remaining three statements were discussed by all members of the steering committee and society chairs and rephrased, followed by an additional round of voting. At the conclusion of the process, 14 statements had strong and five statements moderate agreement, with one statement left in equipoise. This consensus work aims to guide clinicians involved in treating women with suspected endometriosis during patient assessment, counselling and planning of surgical treatment strategies.

Assessing the Utility of artificial intelligence in endometriosis: Promises and pitfalls

Endometriosis, a chronic condition characterized by the growth of endometrial-like tissue outside of the uterus, poses substantial challenges in terms of diagnosis and treatment. Artificial intelligence (AI) has emerged as a promising tool in the field of medicine, offering opportunities to address the complexities of endometriosis. This review explores the current landscape of endometriosis diagnosis and treatment, highlighting the potential of AI to alleviate some of the associated burdens and underscoring common pitfalls and challenges when employing AI algorithms in this context. Women's health research in endometriosis has suffered from underfunding, leading to limitations in diagnosis, classification, and treatment approaches. The heterogeneity of symptoms in patients with endometriosis has further complicated efforts to address this condition. New, powerful methods of analysis have the potential to uncover previously unidentified patterns in data relating to endometriosis. AI, a collection of algorithms replicating human decision-making in data analysis, has been increasingly adopted in medical research, including endometriosis studies. While AI offers the ability to identify novel patterns in data and analyze large datasets, its effectiveness hinges on data quality and quantity and the expertise of those implementing the algorithms. Current applications of AI in endometriosis range from diagnostic tools for ultrasound imaging to predicting treatment success. These applications show promise in reducing diagnostic delays, healthcare costs, and providing patients with more treatment options, improving their quality of life. AI holds significant potential in advancing the diagnosis and treatment of endometriosis, but it must be applied carefully and transparently to avoid pitfalls and ensure reproducibility. This review calls for increased scrutiny and accountability in AI research. Addressing these challenges can lead to more effective AI-driven solutions for endometriosis and other complex medical conditions.

Insights from Mendelian randomization and genetic correlation analyses into the relationship between endometriosis and its comorbidities

BACKGROUND

Endometriosis remains a poorly understood disease, despite its high prevalence and debilitating symptoms. The overlap in symptoms and the increased risk of multiple other traits in women with endometriosis is becoming increasingly apparent through epidemiological data. Genetic studies offer a method of investigating these comorbid relationships through the assessment of causal relationships with Mendelian randomization (MR), as well as identification of shared genetic variants and genes involved across traits. This has the capacity to identify risk factors for endometriosis as well as provide insight into the aetiology of disease.

OBJECTIVE AND RATIONALE

We aim to review the current literature assessing the relationship between endometriosis and other traits using genomic data, primarily through the methods of MR and genetic correlation. We critically examine the limitations of these studies in accordance with the assumptions of the utilized methods.

SEARCH METHODS

The PubMed database was used to search for peer-reviewed original research articles using the terms 'Mendelian randomization endometriosis' and '"genetic correlation" endometriosis'. Additionally, a Google Scholar search using the terms '"endometriosis" "mendelian randomization" "genetic correlation"' was performed. All relevant publications (n = 21) published up until 7 October 2022 were included in this review. Upon compilation of all traits with published MR and/or genetic correlation with endometriosis, additional epidemiological and genetic information on their comorbidity with endometriosis was sourced by searching for the trait in conjunction with 'endometriosis' on Google Scholar.

OUTCOMES

The association between endometriosis and multiple pain, gynaecological, cancer, inflammatory, gastrointestinal, psychological, and anthropometric traits has been assessed using MR analysis and genetic correlation analysis. Genetic correlation analyses provide evidence that genetic factors contributing to endometriosis are shared with multiple traits: migraine, uterine fibroids, subtypes of ovarian cancer, melanoma, asthma, gastro-oesophageal reflux disease, gastritis/duodenitis, and depression, suggesting the involvement of multiple biological mechanisms in endometriosis. The assessment of causality with MR has revealed several potential causes (e.g. depression) and outcomes (e.g. ovarian cancer and uterine fibroids) of a genetic predisposition to endometriosis; however, interpretation of these results requires consideration of potential violations of the MR assumptions.

WIDER IMPLICATIONS

Genomic studies have demonstrated that there is a molecular basis for the co-occurrence of endometriosis with other traits. Dissection of this overlap has identified shared genes and pathways, which provide insight into the biology of endometriosis. Thoughtful MR studies are necessary to ascertain causality of the comorbidities of endometriosis. Given the significant diagnostic delay of endometriosis of 7-11 years, determining risk factors is necessary to aid diagnosis and reduce the disease burden. Identification of traits for which endometriosis is a risk factor is important for holistic treatment and counselling of the patient. The use of genomic data to disentangle the overlap of endometriosis with other traits has provided insights into the aetiology of endometriosis.

Endometriosis and the Temporomandibular Joint-Preliminary Observations

(1) Background: The complete picture of the disease is not fully recognized and extends far beyond the pelvis. The disease's impacts lead to systemic inflammation, in turn resulting in sensitization to pain. The aim of this study was to check whether statistical correlations exist in women with endometriosis with regard to their experience of pain: headache, pelvic pain, temporomandibular joint pain, along with teeth clenching and the treatment of the disease. We constructed contingency tables, followed by Pearson's chi-square test and Cramer's V coefficient values. (2) Methods: A survey was conducted among 128 women aged 33.43 ± 5.79 with a diagnosis of endometriosis (disease duration 6.40 ± 5.88 years). (3) Results: There was a correlation between the occurrence of pain on the right and left sides of the pelvis and pain on the right and left sides of the temporomandibular joint, p-value = 0.0397, V = 0.2350, and between the presence of pelvic pain and the treatment of endometriosis, p-value = 0.0104, V = 0.3709, and between the presence of pain outside the pelvis and the treatment of endometriosis, p-value = 0.0311, V = 0.4549. There was a highly significant correlation between teeth clenching and temporomandibular joint pain, p-value = 0.0005, V = 0.3695. (4) Conclusions: The study revealed a correlation between pelvic endometriosis symptoms and symptoms in the temporomandibular joint.

Expert opinion on the use of transvaginal sonography for presurgical staging and classification of endometriosis

Gynecological ultrasonography plays a central role in the management of endometriosis. The rapid technical development as well as the currently increasing evidence for non-invasive diagnostic methods require an updated compilation of recommendations for the use of ultrasound in the management of endometriosis. The present work aims to highlight the accuracy of sonography for diagnosing and classifying endometriosis and will formulate the present list of key messages and recommendations. This paper aims to demonstrate the accuracy of TVS in the diagnosis and classification of endometriosis and to discuss the clinical applications and consequences of TVS findings for indication, surgical planning and assessment of associated risk factors. (1) Sophisticated ultrasound is the primary imaging modality recommended for suspected endometriosis. The examination procedure should be performed according to the IDEA Consensus. (2) Surgical intervention to confirm the diagnosis alone is not recommended. A preoperative imaging procedure with TVS and/or MRI is strongly recommended. (3) Ultrasound examination does not allow the definitive exclusion of endometriosis. (4) The examination is primarily transvaginal and should always be combined with a speculum and a bimanual examination. (5) Additional transabdominal ultrasonography may enhance the accuracy of the examination in case of extra pelvic disease, extensive findings or limited transvaginal access. (6) Sonographic assessment of both kidneys is mandatory when deep endometriosis (DE) and endometrioma are suspected. (7) Endometriomas are well defined by sonographic criteria. When evaluating the ovaries, the use of IOTA criteria is recommended. (8) The description of sonographic findings of deep endometriosis should be systematically recorded and performed using IDEA terminology. (9) Adenomyosis uteri has sonographically well-defined criteria (MUSA) that allow for detection with high sensitivity and specificity. MRI is not superior to differentiated skilled ultrasonography. (10) Classification of the extent of findings should be done according to the #Enzian classification. The current data situation proves the best possible prediction of the intraoperative situs of endometriosis (exclusive peritoneum) for the non-invasive application of the #Enzian classification. (11) Transvaginal sonographic examination by an experienced examiner is not inferior to MRI diagnostics regarding sensitivity and specificity in the prediction of the extent of deep endometriosis. (12) The major advantage of non-invasive imaging and classification of endometriosis is the differentiated planning or possible avoidance of surgical interventions. The recommendations represent the opinion of experts in the field of non-invasive and invasive diagnostics as well as therapy of endometriosis. They were developed with the participation of the following national and international societies: DEGUM, ÖGUM, SGUM, SEF, AGEM/DGGG, and EEL.

Endometriosis Classification Systems: An International Survey to Map Current Knowledge and Uptake

OBJECTIVE

In the field of endometriosis, several classification, staging and reporting systems have been developed, but do clinicians routinely use these classification systems, which system do they use and what are the clinicians' motivations?

DATA SOURCES

A cross-sectional study was performed to gather data on the current use of endometriosis classification systems, problems encountered and interest in a new simple surgical descriptive system for endometriosis. Of particular focus were three systems most commonly used: the Revised American Society for Reproductive Medicine (rASRM) classification, the Endometriosis Fertility Index (EFI), and the ENZIAN classification. Data were analysed by SPSS. A survey was designed using the online SurveyMonkey tool consisting of 11 questions concerning three domains-participants background, existing classification systems and intentions with regards to a new classification system for endometriosis. Replies were collected between 15 May and 1 July 2020.

METHODS OF STUDY SELECTION

na TABULATION, INTEGRATION AND RESULTS: The final dataset included the replies of 1178 clinicians, including surgeons, gynecologists, reproductive endocrinologists, fertility specialists and sonographers, all managing women with endometriosis in their clinical practice. Overall, 75.5% of the professionals indicate that they currently use a classification system for endometriosis. The rASRM classification system was the best known and used system, the EFI system and ENZIAN system were known by a majority of the professionals but used by only a minority. The lack of clinical relevance was most often selected as a problem with using any system. The findings of the survey suggest that clinicians worldwide are open to using a new classification system for endometriosis that can achieve standardized reporting, and is clinically relevant and simple. The findings therefore support future initiatives for the development of a new descriptive system for endometriosis and provide information on user expectations and conditions for universal uptake of such a system.

CONCLUSION

Even with a high uptake of the existing endometriosis classification systems (rASRM, ENZIAN and EFI), most clinicians managing endometriosis would like a new simple surgical descriptive system for endometriosis.

Site-Specific Regulation of Sulfatase and Aromatase Pathways for Estrogen Production in Endometriosis

Endometriosis is a highly prevalent gynecological disease characterized by lesions in different sites. Regulation of specific estrogen pathways may favor the formation of distinct microenvironments and the progression of endometriosis. However, no study has simultaneously evaluated the gene and protein regulation of the main estrogen-synthesizing enzymes in endometriosis. Thus, our goals were to study the relationship between gene and protein expression of aromatase (CYP19A1 or ARO), steroid sulfatase (STS), and hydroxysteroid 17-beta dehydrogenase (HSD17B1) in superficial (SUP), ovarian (OMA), and deep infiltrating (DIE) endometriotic lesion sites as well as in the eutopic endometrium of patients with (EE) and without (control) endometriosis in the same and large cohort of patients. The site-specific expression of these enzymes within different cells (glandular and stromal components) was also explored. The study included 108 patients surgically diagnosed with endometriosis who provided biopsies of EE and endometriotic lesions and 16 disease-free patients who collected normal endometrium tissue. Our results showed that CYP19A1 was detected in all endometriosis tissues and was in higher levels than in control. Unique patterns of the STS and HSD17B1 levels showed that they were most closely regulated in all tissues, with manifestation at greater levels in DIE compared to the other endometriotic lesion sites, OMA and SUP. Gene and protein expression of ARO, STS, and HSD17B1 occurred at different rates in endometriotic sites or EE. The distinctive levels of these estrogen-synthesizing enzymes in each endometriotic site support the hypothesis of a tissue microenvironment that can both influence and be influenced by the expression of different estrogenic pathways, locally affecting the availability of estrogen needed for maintenance and progression of endometriotic lesions.

Endometriosis classification systems: an international survey to map current knowledge and uptake

Background

In the field of endometriosis, several classification, staging and reporting systems have been developed and published, but there are no data on the uptake of these systems in clinical practice.

Objectives

The objective of the current study was to examine whether clinicians routinely use the existing endometriosis classification systems, which system do they use and what are the clinicians’ motivations?

Materials and Methods

A cross-sectional study was performed to gather data on the current use of endometriosis classification systems, problems encountered and interest in a new simple surgical descriptive system for endometriosis. Of particular focus were three systems most commonly used: the Revised American Society for Reproductive Medicine (rASRM) classification, the Endometriosis Fertility Index (EFI), and the ENZIAN classification. Data were analysed by SPSS. A survey was designed using the online SurveyMonkey tool consisting of 11 questions concerning three domains— participants’ background, existing classification systems and intentions with regards to a new classification system for endometriosis. Replies were collected between 15 May and 1 July 2020.

Main outcome measures

Uptake, feedback and future intentions.

Results

The final dataset included the replies of 1178 clinicians, including surgeons, gynaecologists, reproductive endocrinologists, fertility specialists and sonographers, all managing women with endometriosis in their clinical practice. Overall, 75.5% of the professionals indicate that they currently use a classification system for endometriosis. The rASRM classification system was the best known and used system, the EFI system and ENZIAN system were known by a majority of the professionals but used by only a minority. The lack of clinical relevance was most often selected as a problem with using any system. The findings of the survey suggest that clinicians worldwide are open to using a new classification system for endometriosis that can achieve standardised reporting, and is clinically relevant and simple.

Conclusions

Even with a high uptake of the existing endometriosis classification systems (rASRM, ENZIAN and EFI), most clinicians managing endometriosis would like a new simple surgical descriptive system for endometriosis.

What is new?

The findings therefore support future initiatives for the development of a new descriptive system for endometriosis and provide information on user expectations and conditions for universal uptake of such a system.