Non-Invasive Diagnosis of Endometriosis by Questionnaires in Patients Using Contraception

<b>Background/Objectives:</b> The assessment of endometriosis (EMS)-associated pain is important, but only very few studies address the potential use of questionnaires for non-invasive prediction of the disease. <b>Methods:</b> In a prospective observational study from 2016 to 2024 with patients (n = 228) using hormonal contraception, all women with suspected EMS answered two questionnaires and were examined physically and with transvaginal ultrasound (TVUS). If deep infiltrating EMS (DIE) was suspected, magnetic resonance imaging (MRI) was performed. EMS diagnosis was confirmed by histological examination. Statistical analysis was mainly performed using 2 × 2 contingency tables. The decision tree was created manually. <b>Results:</b> The mean numerical rating scales (NRSs) of EMS-positive compared to EMS-negative patients were ~4-fold higher (4.45 and 1.15, respectively). Patients with EMS have, significantly, ~3 times more significant parameters compared to patients without EMS (18.5 and 5.9, respectively). In combination with dysuria and lightning-like pain, this resulted in very good prediction. A decision tree yielded a sensitivity of 0.924, a specificity of 0.917, a positive predictive value (PPV) of 0.924, a negative predictive value (NPV) of 0.917, and a positive likelihood ratio of 11.2, indicating a very good diagnostic test. There is no typical endometriosis pain, but various pain patterns are predictive of EMS. <b>Conclusions:</b> Thus, a reliable non-invasive EMS diagnosis by questionnaires is possible and could reduce the delay in the detection of EMS.

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Publication Details

Journal
Journal of Clinical Medicine
Published
2025-12-20
DOI
https://doi.org/10.3390/jcm15010030
Primary Topic
Endometriosis Research and Treatment
Type
article
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article

Non-Invasive Diagnosis of Endometriosis by Questionnaires in Patients Using Contraception

Felix Alexander Zeppernick, Sebastian Harth, Lutz Konrad, Ivo Meinhold-Heerlein et al.
Journal of Clinical Medicine
Endometriosis Research and Treatment
article

Non-Invasive Diagnosis of Endometriosis by Questionnaires in Patients Using Contraception

Felix Alexander Zeppernick, Sebastian Harth, Lutz Konrad, Ivo Meinhold-Heerlein, Manuela Schuler, Muhammad A. Riaz, Florian Sibelius, Samira Balimuttajjo, Anna Löffelmann, Christian Schorr, Sarah Seeger
article en

Abstract

<b>Background/Objectives:</b> The assessment of endometriosis (EMS)-associated pain is important, but only very few studies address the potential use of questionnaires for non-invasive prediction of the disease. <b>Methods:</b> In a prospective observational study from 2016 to 2024 with patients (n = 228) using hormonal contraception, all women with suspected EMS answered two questionnaires and were examined physically and with transvaginal ultrasound (TVUS). If deep infiltrating EMS (DIE) was suspected, magnetic resonance imaging (MRI) was performed. EMS diagnosis was confirmed by histological examination. Statistical analysis was mainly performed using 2 × 2 contingency tables. The decision tree was created manually. <b>Results:</b> The mean numerical rating scales (NRSs) of EMS-positive compared to EMS-negative patients were ~4-fold higher (4.45 and 1.15, respectively). Patients with EMS have, significantly, ~3 times more significant parameters compared to patients without EMS (18.5 and 5.9, respectively). In combination with dysuria and lightning-like pain, this resulted in very good prediction. A decision tree yielded a sensitivity of 0.924, a specificity of 0.917, a positive predictive value (PPV) of 0.924, a negative predictive value (NPV) of 0.917, and a positive likelihood ratio of 11.2, indicating a very good diagnostic test. There is no typical endometriosis pain, but various pain patterns are predictive of EMS. <b>Conclusions:</b> Thus, a reliable non-invasive EMS diagnosis by questionnaires is possible and could reduce the delay in the detection of EMS.

Journal of Clinical MedicineVol. 15(1)
Justus-Liebig-Universität Gießen (DE), Deutsches Forschungsnetz (DE), German Research Centre for Artificial Intelligence (DE)
Good health and well-being
Openalex Percentile: Top 28%
Endometriosis Research and Treatment
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