Nerve Bundle Density and Expression of NGF and IL-1ß Are Intra-Individually Heterogenous in Subtypes of Endometriosis.

Endometriosis is a gynecological disorder associated with local inflammation and neuroproliferation. Increased nerve bundle density has been attributed to increased expression of nerve growth factor (NGF) and interleukin-1ß (IL-1ß). Immunohistochemical analysis was carried out on 12 patients presenting with all three anatomic subtypes of endometriosis (deep, superficial peritoneal, endometrioma) at surgery, with at least two surgically excised subtypes available for analysis. Immunolocalization for nerve bundle density around endometriosis using protein gene product 9.5 (PGP9.5), as well as NGF and IL-1ß histoscores in endometriosis epithelium/stroma, was performed to evaluate differences in scores between lesions and anatomic subtypes per patient. Intra-individual heterogeneity in scores across lesions was assessed using the coefficient of variation (CV). The degree of score variability between subtypes was evaluated using the percentage difference between mean scores from one subtype to another subtype for each marker. PGP9.5 nerve bundle density was heterogenous across multiple subtypes of endometriosis, ranging from 50.0% to 173.2%, where most patients (8/12) showed CV ≥ 100%. The percentage difference in scores showed that PGP9.5 nerve bundle density and NGF and IL-1ß expression were heterogenous between anatomic subtypes within the same patient. Based on these observations of intra-individual heterogeneity, we conclude that markers of neuroproliferation in endometriosis should be stratified by anatomic subtype in future studies of clinical correlation.

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.

Standardized protocol for quantification of nerve bundle density as a biomarker for endometriosis.

Introduction: We propose a standardized protocol for measurement of nerve bundle density in endometriosis as a potential biomarker, including in deep endometriosis (DE), ovarian endometriomas (OMA) and superficial peritoneal endometriosis (SUP). Methods: This was a prospective cohort of surgically excised endometriosis samples from Dec 1st 2013 and Dec 31st 2017 at a tertiary referral center for endometriosis in Vancouver, BC, Canada. Surgical data were available from linked patient registry. Protein gene product 9.5 (PGP9.5) was used to identify nerve bundles on immunohistochemistry. PGP9.5 nerve bundles were counted visually. To calculate nerve bundle density, PGP9.5 nerve bundle count was divided by the tissue surface area (total on the slide). All samples were assessed using NHS Elements software for semi-automated measurement of the tissue surface area. For a subset of samples, high power fields (HPFs) were also counted as manual measurement of the tissue surface area. Intraclass correlation was used to assess intra observer and inter observer reliability. Generalized linear mixed model (GLMM) with random intercepts only was conducted to assess differences in PGP9.5 nerve bundle density by endometriosis type (DE, OMA, SUP). Results: In total, 236 tissue samples out of 121 participants were available for analysis in the current study. Semi-automated surface area measurement could be performed in 94.5% of the samples and showed good correlation with manually counted HPFs (Spearman's rho = 0.781, p < 0.001). To assess intra observer reliability, 11 samples were assessed twice by the same observer; to assess inter observer reliability, 11 random samples were blindly assessed by two observers. Intra observer reliability and inter observer reliability for nerve bundle density were excellent: 0.979 and 0.985, respectively. PGP9.5 nerve bundle density varied among samples and no nerve bundles could be found in 24.6% of the samples. GLMM showed a significant difference in PGP9.5 nerve bundle density between the different endometriosis types (X2 = 87.6, P < 0.001 after adjusting for hormonal therapy, with higher density in DE and SUP in comparison to OMA). Conclusion: A standardized protocol is presented to measure PGP9.5 nerve bundle density in endometriosis, which may serve as a biomarker reflecting local neurogenesis in the endometriosis microenvironment.

Pelvic pain comorbidities associated with quality of life after endometriosis surgery.

BACKGROUND: After endometriosis surgery, pain can persist or recur in a subset of patients. A possible reason for persistent pain after surgery is central nervous system sensitization and associated pelvic pain comorbidities. Surgery addresses the peripheral component of endometriosis pain pathophysiology (by lesion removal) but may not treat this centralized pain. Therefore, endometriosis patients with pelvic pain comorbidities related to central sensitization may experience worse pain-related outcomes after surgery, such as lower pain-related quality of life. OBJECTIVE: This study aimed to determine whether baseline (preoperative) pelvic pain comorbidities are associated with pain-related quality of life at follow-up after endometriosis surgery. STUDY DESIGN: This study used longitudinal prospective registry data from the Endometriosis Pelvic Pain Interdisciplinary Cohort at the BC Women's Centre for Pelvic Pain and Endometriosis. Participants were aged ≤50 years with confirmed or clinically suspected endometriosis, and underwent surgery (fertility-sparing or hysterectomy) for endometriosis pain. Participants completed the pain subscale of the Endometriosis Health Profile-30 quality of life questionnaire preoperatively and at follow-up (1-2 years). Linear regression was performed to measure the individual relationships between 7 pelvic pain comorbidities at baseline and follow-up Endometriosis Health Profile-30 score, controlling for baseline Endometriosis Health Profile-30 and type of surgery received. These baseline (preoperative) pelvic pain comorbidities included abdominal wall pain, pelvic floor myalgia, painful bladder syndrome, irritable bowel syndrome, Patient Health Questionnaire 9 depression score, Generalized Anxiety Disorder 7 score, and Pain Catastrophizing Scale score. Least absolute shrinkage and selection operator regression was then performed to select the most important variables associated with follow-up Endometriosis Health Profile-30 from 17 covariates (including the 7 pelvic pain comorbidities, baseline Endometriosis Health Profile-30 score, type of surgery, and other endometriosis-related factors such as stage and histologic confirmation of endometriosis). Using 1000 bootstrap samples, we estimated the coefficients and confidence intervals of the selected variables and generated a covariate importance rank. RESULTS: The study included 444 participants. The median follow-up time was 18 months. Pain-related quality of life (Endometriosis Health Profile-30) of the study population significantly improved at follow-up after surgery (P<.001). The following pelvic pain comorbidities were associated with lower quality of life (higher Endometriosis Health Profile-30 score) after surgery, controlling for baseline Endometriosis Health Profile-30 score and type of surgery (fertility-sparing vs hysterectomy): abdominal wall pain (P=.013), pelvic floor myalgia (P=.036), painful bladder syndrome (P=.022), Patient Health Questionnaire 9 score (P<.001), Generalized Anxiety Disorder 7 score (P<.001), and Pain Catastrophizing Scale score (P=.007). Irritable bowel syndrome was not significant (P=.70). Of the 17 covariates included for least absolute shrinkage and selection operator regression, 6 remained in the final model (lambda=3.136). These included 3 pelvic pain comorbidities that were associated with higher follow-up Endometriosis Health Profile-30 scores or worse quality of life: abdominal wall pain (ß=3.19), pelvic floor myalgia (ß=2.44), and Patient Health Questionnaire 9 depression score (ß=0.49). The other 3 variables in the final model were baseline Endometriosis Health Profile-30 score, type of surgery, and histologic confirmation of endometriosis. CONCLUSION: Pelvic pain comorbidities present at baseline before surgery, which may reflect underlying central nervous system sensitization, are associated with lower pain-related quality of life after endometriosis surgery. Particularly important were depression and musculoskeletal/myofascial pain (abdominal wall pain and pelvic floor myalgia). Therefore, these pelvic pain comorbidities should be candidates for a formal prediction model of pain outcomes after endometriosis surgery.