How to Improve Non-Invasive Diagnosis of Endometriosis with Advanced Statistical Methods
<i>Background and Objectives</i>: 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 dia...
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| Format: | Article |
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MDPI AG
2023-03-01
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| Series: | Medicina |
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| Online Access: | https://www.mdpi.com/1648-9144/59/3/499 |
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| author | Maria Szubert Aleksander Rycerz Jacek R. Wilczyński |
| author_facet | Maria Szubert Aleksander Rycerz Jacek R. Wilczyński |
| author_sort | Maria Szubert |
| collection | DOAJ |
| description | <i>Background and Objectives</i>: 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. <i>Materials and Methods</i>: 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. <i>Results</i>: 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. <i>Conclusions</i>: 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. |
| first_indexed | 2024-03-11T06:12:16Z |
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| issn | 1010-660X 1648-9144 |
| language | English |
| last_indexed | 2024-03-11T06:12:16Z |
| publishDate | 2023-03-01 |
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| record_format | Article |
| series | Medicina |
| spelling | doaj.art-50162d9664e6482e909c78da6ee56bd42023-11-17T12:31:30ZengMDPI AGMedicina1010-660X1648-91442023-03-0159349910.3390/medicina59030499How to Improve Non-Invasive Diagnosis of Endometriosis with Advanced Statistical MethodsMaria Szubert0Aleksander Rycerz1Jacek R. Wilczyński2Department of Surgical and Oncological Gynecology, 1st Department of Gynecology and Obstetrics, Medical University of Lodz, M. Pirogow’s Teaching Hospital, Wilenska 37 St., 94-029 Lodz, PolandDepartment of Surgical and Oncological Gynecology, 1st Department of Gynecology and Obstetrics, Medical University of Lodz, M. Pirogow’s Teaching Hospital, Wilenska 37 St., 94-029 Lodz, PolandDepartment of Surgical and Oncological Gynecology, 1st Department of Gynecology and Obstetrics, Medical University of Lodz, M. Pirogow’s Teaching Hospital, Wilenska 37 St., 94-029 Lodz, Poland<i>Background and Objectives</i>: 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. <i>Materials and Methods</i>: 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. <i>Results</i>: 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. <i>Conclusions</i>: 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.https://www.mdpi.com/1648-9144/59/3/499endometriosisnon-invasive diagnosismachine learning algorithmCA125LASSO |
| spellingShingle | Maria Szubert Aleksander Rycerz Jacek R. Wilczyński How to Improve Non-Invasive Diagnosis of Endometriosis with Advanced Statistical Methods Medicina endometriosis non-invasive diagnosis machine learning algorithm CA125 LASSO |
| title | How to Improve Non-Invasive Diagnosis of Endometriosis with Advanced Statistical Methods |
| title_full | How to Improve Non-Invasive Diagnosis of Endometriosis with Advanced Statistical Methods |
| title_fullStr | How to Improve Non-Invasive Diagnosis of Endometriosis with Advanced Statistical Methods |
| title_full_unstemmed | How to Improve Non-Invasive Diagnosis of Endometriosis with Advanced Statistical Methods |
| title_short | How to Improve Non-Invasive Diagnosis of Endometriosis with Advanced Statistical Methods |
| title_sort | how to improve non invasive diagnosis of endometriosis with advanced statistical methods |
| topic | endometriosis non-invasive diagnosis machine learning algorithm CA125 LASSO |
| url | https://www.mdpi.com/1648-9144/59/3/499 |
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