How to Improve Non-Invasive Diagnosis of Endometriosis with Advanced Statistical Methods

Abstract

Background and Objectives: Endometriosis is one of the most common gynecological disorders in women of reproductive age. Causing pelvic pain and infertility, it is considered one of the most serious health problems, being responsible for work absences or productivity loss. Its diagnosis is often delayed because of the need for an invasive laparoscopic approach. Despite years of studies, no single marker for endometriosis has been discovered. The aim of this research was to find an algorithm based on symptoms and laboratory tests that could diagnose endometriosis in a non-invasive way. Materials and Methods: The research group consisted of 101 women hospitalized for diagnostic laparoscopy, among which 71 had confirmed endometriosis. Data on reproductive history were collected in detail. CA125 (cancer antigen-125) level and VEGF1(vascular endothelial growth factor 1) were tested in blood samples. Among the used statistical methods, the LASSO regression—a new important statistical tool eliminating the least useful features—was the only method to have significant results. Results: Out of 19 features based on results of LASSO, 7 variables were chosen: body mass index, age of menarche, cycle length, painful periods, information about using contraception, CA125, and VEGF1. After multivariate logistic regression with a backward strategy, the three most significant features were evaluated. The strongest impact on endometriosis prediction had information about painful periods, CA125 over 15 u/mL, and the lowest BMI, with a sensitivity of 0.8800 and a specificity of 0.8000, respectively. Conclusions: Advanced statistical methods are crucial when creating non-invasive tests for endometriosis. An algorithm based on three easy features, including painful menses, BMI level, and CA125 concentration could have an important place in the non-invasive diagnosis of endometriosis. If confirmed in a prospective study, implementing such an algorithm in populations with a high risk of endometriosis will allow us to cover patients suspected of endometriosis with proper treatment.

<span data-contrast="auto">Evaluating the Prognostic Potential of CA-125 and miRNA Levels in Endometriosis: A Narrative Review <span data-ccp-props="{">

“…A diagnostic model using HGB, CA199, CA-125, and HE4 showed a sensitivity of 85.4%, specificity of 78.83%, and an AUC of 0.900, suggesting enhanced diagnostic accuracy for early endometriosis detection [34]. Szubert et al 2023 aimed to create a non-invasive endometriosis diagnostic algorithm. They pinpointed 7 key features, including painful periods, CA-125 levels, and BMI, as strong predictors.…”

“…They pinpointed 7 key features, including painful periods, CA-125 levels, and BMI, as strong predictors. Their algorithm boasted a sensitivity of 0.88 and specificity of 0.80, emphasizing the potential of using accessible features for diagnosis [35].…”

“…CA-125 offers a broad perspective, while miRNAs provide detailed molecular insights. Research from Szubert et al (2023), Zhao and Qu (2021), and Herranz-Blanco et al (2023) indicates that combining clinical features with CA-125 and miRNA profiles could lead to more precise diagnoses, marking progress towards a more complete diagnostic method.…”