Development and Validation of an Endometriosis Diagnostic Method Based on Serum Biomarkers and Clinical Variables

An update on endometriosis biomarkers

Endometriosis is a debilitating gynecologic disorder characterized by chronic pelvic pain, pelvic adhesions and infertility. The gold standard diagnostic modality is histologically by tissue biopsy, although it can be diagnosed empirically if symptoms improve with medical treatment. A delayed diagnosis of endometriosis often leads to a significant impairment in quality of life and work productivity; hence, significant morbidity has been shown to bear a detrimental impact on society and the economy. The ongoing novel investigation into biomarkers for diagnostic or prognostic evaluation of endometriosis may aid in earlier detection, and thereby, improve patient quality-of-life as well as minimize morbidity. Currently, no single biomarker has been validated for endometriosis; however, there are emerging data on the utility of microRNA for diagnosis and prognosis of disease activity. In this brief review, we will identify and categorize the novel biomarkers for endometriosis.

Validation of an In Vitro Diagnostic Test for Endometriosis: Impact of Confounding Medical Conditions and Lesion Location

With the aim to shorten the time for diagnosis and accelerate access to correct management, a non-invasive diagnostic test for endometriosis was developed and validated. The IVD test combines an ELISA test kit to quantify CA125 and BDNF concentrations in serum and a data treatment algorithm hosted in medical software processing results from the ELISA test and responses to six clinical variables. Serum samples and clinical variables extracted from psychometric questionnaires from 77 patients were collected from the Oxford Endometriosis CaRe Centre biobank (UK). Case/control classification was performed based on laparoscopy and histological verification of the excised lesions. Biomarkers serum concentrations and clinical variables were introduced to the software, which generates the qualitative diagnostic result ("positive" or "negative"). This test allowed the detection of 32% of cases with superficial endometriosis, which is an added value given the limited efficacy of existing imaging techniques. Even in the presence of various confounding medical conditions, the test maintained a specificity of 100%, supporting its suitability for use in patients with underlying medical conditions.

A Comprehensive Review of the Diagnostic Landscape of Endometriosis: Assessing Tools, Uncovering Strengths, and Acknowledging Limitations

Endometriosis is a prevalent yet often underdiagnosed condition characterized by the presence of endometrial-like tissue outside the uterus, leading to significant morbidity and impaired quality of life. A timely and accurate diagnosis of endometriosis is essential for effective management and improved patient outcomes. This review provides a comprehensive overview of the current diagnostic landscape of endometriosis, including clinical evaluation, imaging modalities, biomarkers, and laparoscopy. The strengths and limitations of each diagnostic approach are critically evaluated, alongside challenges such as delayed diagnosis and misinterpretation of findings. The review emphasizes the importance of multidisciplinary collaboration, standardized diagnostic protocols, and ongoing research to enhance diagnostic accuracy and facilitate early intervention. By addressing these challenges and leveraging emerging technologies, healthcare professionals can improve the diagnosis and management of endometriosis, ultimately enhancing the well-being of affected individuals.

Developing a Predictive Model for Minimal or Mild Endometriosis as a Clinical Screening Tool in Infertile Women: Uterosacral Tenderness as a Key Predictor

STUDY OBJECTIVE

To develop a noninvasive predictive model based on patients with infertility for identifying minimal or mild endometriosis.

DESIGN

A retrospective cohort study.

SETTING

This study was conducted at a tertiary referral center.

PATIENTS

A total of consecutive 1365 patients with infertility who underwent laparoscopy between January 2013 and August 2020 were divided into a training set (n = 910) for developing the predictive model and a validation set (n = 455) to confirm the model's prediction efficiency. The patients were randomly assigned in a 2:1 ratio.

INTERVENTIONS

Sensitivities, specificities, area under the curve, the Hosmer-Lemeshow goodness of fit test, Net Reclassification Improvement index, and Integrated Discrimination Improvement index were evaluated in the training set to select the optimum model. In the validation set, the model's discriminations, calibrations, and clinical use were tested for validation.

MEASUREMENTS AND MAIN RESULTS

In the training set, there were 587 patients with minimal or mild endometriosis and 323 patients without endometriosis. The combination of clinical parameters in the model was evaluated for both statistical and clinical significance. The best-performing model ultimately included body mass index, dysmenorrhea, dyspareunia, uterosacral tenderness, and serum cancer antigen 125 (CA-125). The nomogram based on this model demonstrated sensitivities of 87.7% and 93.3%, specificities of 68.6% and 66.4%, and area under the curve of 0.84 (95% confidence interval 0.81-0.87) and 0.85 (95% confidence interval 0.80-0.89) for the training and validation sets, respectively. Calibration curves and decision curve analyses also indicated that the model had good calibration and clinical value. Uterosacral tenderness emerged as the most valuable predictor.

CONCLUSION

This study successfully developed a predictive model with high accuracy in identifying infertile women with minimal or mild endometriosis based on clinical characteristics, signs, and cost-effective blood tests. This model would assist clinicians in screening infertile women for minimal or mild endometriosis, thereby facilitating early diagnosis and treatment.

Crimson clues: advancing endometriosis detection and management with novel blood biomarkers

Endometriosis is an inflammatory condition affecting approximately 10% of the female-born population. Despite its prevalence, the lack of noninvasive biomarkers has contributed to an established global diagnostic delay. The intricate pathophysiology of this enigmatic disease may leave signatures in the blood, which, when detected, can be used as noninvasive biomarkers. This review provides an update on how investigators are utilizing the established disease pathways and innovative methodologies, including genome-wide association studies, next-generation sequencing, and machine learning, to unravel the clues left in the blood to develop blood biomarkers. Many blood biomarkers show promise in the discovery phase, but because of a lack of standardized and robust methodologies, they rarely progress to the development stages. However, we are now seeing biomarkers being validated with high diagnostic accuracy and improvements in standardization protocols, providing promise for the future of endometriosis blood biomarkers.