The role of shear wave elastography in predicting clinical symptoms in adenomyosis: A prospective observational study with a machine learning approach

Uğurcan Zorlu, Sezer Nil Yılmazer Zorlu, Burak Elmas
Journal of Obstetrics and Gynaecology Research · 2025 · vol. 51(8) , pp. e70037 · doi:10.1111/jog.70037 · PMID:40785103
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⚙ AI-generated summary by claude@2026-08, 2026-08-10 ⓘ

This study found shear wave elastography (SWE) values correlated with dysmenorrhea, dyspareunia, and chronic pelvic pain in adenomyosis, with machine learning models predicting these symptoms.

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⚙ AI-generated deep summary by claude@2026-06, 2026-06-13 · read from full text ⓘ

This prospective observational study enrolled 63 women diagnosed with focal adenomyosis and used shear wave elastography (SWE) to measure uterine tissue stiffness, then applied several machine learning algorithms to predict specific clinical symptoms from SWE velocity values and clinical features. The study found significant associations between SWE velocity (SWV) and symptoms including dysmenorrhea, dyspareunia, and non-cyclic chronic pelvic pain, with K-nearest neighbors performing best for dyspareunia and non-cyclic chronic pelvic pain and random forest performing best for dysmenorrhea, while menorrhagia showed no significant SWE differences. The authors reported symptom-related cutoff values (e.g., 4.69 m/s for dysmenorrhea), but the study’s limitation included that the dataset was not publicly available due to ethical and legal restrictions on patient confidentiality. This paper is centrally about adenomyosis — it evaluates SWE plus machine learning to predict adenomyosis-related clinical symptoms.

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Abstract

OBJECTIVE: Adenomyosis is a gynecological condition characterized by the invasion of endometrial tissue into the myometrium, causing symptoms such as dysmenorrhea, menorrhagia, and chronic pelvic pain. Its diagnosis remains challenging due to overlapping features with other uterine disorders, and the variability in symptom presentation makes management complex. This study aims to evaluate the utility of shear wave elastography (SWE) in predicting clinical symptoms of adenomyosis and to explore the potential of machine learning (ML) models in enhancing diagnostic precision and predicting patient outcomes. METHODS: A total of 63 women diagnosed with focal adenomyosis were included in this prospective observational study. SWE was performed to assess uterine tissue stiffness, with ML algorithms (logistic regression, random forest, support vector machine, K-nearest neighbors) applied to predict symptoms based on SWE measurements and clinical features. Clinical symptoms such as dysmenorrhea, dyspareunia, non-cyclic chronic pelvic pain, and menorrhagia were evaluated. Statistical analysis was conducted using SPSS software, with performance metrics such as accuracy, F1 score, and ROC-AUC used to assess model effectiveness. RESULTS: Significant associations were found between SWE velocity (SWV) values and symptoms like dysmenorrhea, dyspareunia, and non-cyclic chronic pelvic pain. K-nearest neighbors (KNN) exhibited the best performance in predicting dyspareunia and non-cyclic chronic pelvic pain, while random forest performed best for dysmenorrhea. Menorrhagia did not show significant differences in SWE values. Cutoff values for clinical symptoms, such as 4.69 m/s for dysmenorrhea, were identified, providing actionable thresholds for clinical use. CONCLUSION: SWE combined with ML offers a promising approach to predict clinical symptoms of adenomyosis, aiding in personalized treatment strategies. This study highlights the potential of integrating advanced imaging techniques and computational models to enhance clinical decision-making and improve patient outcomes.
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Abstract

Objective Adenomyosis is a gynecological condition characterized by the invasion of endometrial tissue into the myometrium, causing symptoms such as dysmenorrhea, menorrhagia, and chronic pelvic pain. Its diagnosis remains challenging due to overlapping features with other uterine disorders, and the variability in symptom presentation makes management complex. This study aims to evaluate the utility of shear wave elastography (SWE) in predicting clinical symptoms of adenomyosis and to explore the potential of machine learning (ML) models in enhancing diagnostic precision and predicting patient outcomes.

Methods

A total of 63 women diagnosed with focal adenomyosis were included in this prospective observational study. SWE was performed to assess uterine tissue stiffness, with ML algorithms (logistic regression, random forest, support vector machine, K-nearest neighbors) applied to predict symptoms based on SWE measurements and clinical features. Clinical symptoms such as dysmenorrhea, dyspareunia, non-cyclic chronic pelvic pain, and menorrhagia were evaluated. Statistical analysis was conducted using SPSS software, with performance metrics such as accuracy, F1 score, and ROC-AUC used to assess model effectiveness.

Results

Significant associations were found between SWE velocity (SWV) values and symptoms like dysmenorrhea, dyspareunia, and non-cyclic chronic pelvic pain. K-nearest neighbors (KNN) exhibited the best performance in predicting dyspareunia and non-cyclic chronic pelvic pain, while random forest performed best for dysmenorrhea. Menorrhagia did not show significant differences in SWE values. Cutoff values for clinical symptoms, such as 4.69 m/s for dysmenorrhea, were identified, providing actionable thresholds for clinical use.

Conclusion

SWE combined with ML offers a promising approach to predict clinical symptoms of adenomyosis, aiding in personalized treatment strategies. This study highlights the potential of integrating advanced imaging techniques and computational models to enhance clinical decision-making and improve patient outcomes. CONFLICT OF INTEREST STATEMENT There are no conflicts of interest among the authors. DATA AVAILABILITY STATEMENT The datasets generated and/or analyzed during the current study are not publicly available due to ethical and legal restrictions related to patient confidentiality and data protection regulations. The data contain sensitive personal health information of participants diagnosed with adenomyosis. Public sharing would violate institutional review board (IRB) policies and national privacy laws, including the Turkish Personal Data Protection Law No. 6698. In accordance with the approved ethics protocol (Ethics Committee Approval No: TABED-2-24-668, Ankara Bilkent City Hospital, Ankara, Turkey), access to the raw data is restricted to the research team. Any requests for access will be reviewed on a case-by-case basis by the hospital's ethics committee and data custodian, and must comply with all applicable legal and ethical standards.

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Condition tags

dysmenorrheachronic_pelvic_painadenomyosisdyspareunia

MeSH descriptors

Adenomyosis Adenomyosis Adenomyosis Adenomyosis Adenomyosis Adenomyosis Adenomyosis Adenomyosis Adenomyosis Adenomyosis Adenomyosis Adenomyosis Adenomyosis Adenomyosis Adenomyosis Adenomyosis Adenomyosis Adenomyosis Adenomyosis Adenomyosis

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