{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,26]],"date-time":"2026-10-26T16:54:48Z","timestamp":1793033688861,"version":"4.5.0"},"reference-count":18,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2025,4,11]],"date-time":"2025-04-11T00:00:00Z","timestamp":1744329600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2025,4,11]],"date-time":"2025-04-11T00:00:00Z","timestamp":1744329600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["obgyn.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Intl J Gynecology &amp;amp; Obste"],"published-print":{"date-parts":[[2025,10]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>To evaluate the diagnostic value of shear wave elastography (SWE) in assessing endometriomas and its correlation with clinical symptoms. Furthermore, the study investigates the use of machine learning (ML) models to predict clinical outcomes based on SWE\u2010derived mean shear wave velocity (SWV).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This prospective study included 94 women aged 20\u201335\u2009years diagnosed with unilateral ovarian endometriomas. SWE was used to assess tissue stiffness in meters per second (m\/s). Clinical characteristics such as dysmenorrhea, dyspareunia, infertility, and non\u2010cyclic chronic pelvic pain were evaluated. Statistical analyses were performed using SPSS, and ML models, including Logistic Regression, Random Forest, Gradient Boosting, and Support Vector Machine (SVM), were employed to predict clinical outcomes based on mean SWV.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Shear wave elastography\u2010derived mean SWV was significantly higher in patients with dysmenorrhea (4.79\u2009\u00b1\u20091.08 vs. 3.22\u2009\u00b1\u20090.65\u2009m\/s;\n                      <jats:italic>P<\/jats:italic>\n                      \u2009=\u20090.024), dyspareunia (5.78\u2009\u00b1\u20091.9 vs. 3.01\u2009\u00b1\u20090.94\u2009m\/s;\n                      <jats:italic>P<\/jats:italic>\n                      \u2009=\u20090.016), and infertility (5.42\u2009\u00b1\u20091.44 vs. 3.93\u2009\u00b1\u20091.03\u2009m\/s;\n                      <jats:italic>P<\/jats:italic>\n                      \u2009=\u20090.014). ML models demonstrated high predictive accuracy for dysmenorrhea (receiver operating characteristic [ROC]\u2010area under the curve [AUC] 0.94) and dyspareunia (ROC\u2010AUC 0.98), with Logistic Regression and SVM outperforming other algorithms.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Shear wave elastography combined with ML offers a non\u2010invasive, cost\u2010effective approach to diagnosing and predicting clinical outcomes in endometriomas. The integration of imaging and computational tools paves the way for precision medicine in gynecology.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/ijgo.70159","type":"journal-article","created":{"date-parts":[[2025,4,11]],"date-time":"2025-04-11T11:52:02Z","timestamp":1744372322000},"page":"371-381","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Shear wave elastography values in endometrioma: Clinical findings and machine learning\u2010based prediction models"],"prefix":"10.1002","volume":"171","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8912-0812","authenticated-orcid":false,"given":"U\u011furcan","family":"Zorlu","sequence":"first","affiliation":[{"name":"Department of Perinatalogy Ankara Bilkent City Hospital  Ankara Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-7162-4668","authenticated-orcid":false,"given":"Sezer Nil","family":"Y\u0131lmazer\u2010Zorlu","sequence":"additional","affiliation":[{"name":"Department of Radiology Mamak State Hospital  Ankara Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3078-8420","authenticated-orcid":false,"given":"\u0130nci","family":"Halilzade","sequence":"additional","affiliation":[{"name":"Department of Obstetrics and Gynecology Ankara Bilkent City Hospital  Ankara Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9927-181X","authenticated-orcid":false,"given":"Batuhan","family":"Turgay","sequence":"additional","affiliation":[{"name":"Department of Obstetrics and Gynecology Ankara University Faculty of Medicine  Ankara Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9920-6804","authenticated-orcid":false,"given":"Mehmet","family":"\u00dcnsal","sequence":"additional","affiliation":[{"name":"Department of Gynecologic Oncology Sivas Numune Hospital  Sivas Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2025,4,11]]},"reference":[{"key":"e_1_2_13_2_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12905-015-0196-z"},{"key":"e_1_2_13_3_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10396-016-0701-5"},{"key":"e_1_2_13_4_1","doi-asserted-by":"publisher","DOI":"10.1055\/a-2073-8589"},{"key":"e_1_2_13_5_1","doi-asserted-by":"publisher","DOI":"10.37609\/akya.3336"},{"key":"e_1_2_13_6_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-97-3289-0_20"},{"key":"e_1_2_13_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejogrb.2024.06.020"},{"key":"e_1_2_13_8_1","doi-asserted-by":"publisher","DOI":"10.17816\/PAVLOVJ105496"},{"key":"e_1_2_13_9_1","doi-asserted-by":"publisher","DOI":"10.1007\/s43032-022-01083-x"},{"key":"e_1_2_13_10_1","doi-asserted-by":"publisher","DOI":"10.3390\/biomedicines9010054"},{"key":"e_1_2_13_11_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-50662-8_7"},{"key":"e_1_2_13_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.fertnstert.2011.06.064"},{"key":"e_1_2_13_13_1","doi-asserted-by":"publisher","DOI":"10.1196\/annals.1434.007"},{"key":"e_1_2_13_14_1","doi-asserted-by":"publisher","DOI":"10.23736\/S0026-4806.19.06298-0"},{"key":"e_1_2_13_15_1","first-page":"4765","article-title":"A unified approach to interpreting model predictions","volume":"30","author":"Lundberg SM","year":"2017","journal-title":"Adv Neural Inf Process Syst"},{"key":"e_1_2_13_16_1","doi-asserted-by":"publisher","DOI":"10.1002\/uog.29171"},{"key":"e_1_2_13_17_1","doi-asserted-by":"publisher","DOI":"10.3390\/jcm12010287"},{"key":"e_1_2_13_18_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-024-65238-3"},{"key":"e_1_2_13_19_1","doi-asserted-by":"publisher","DOI":"10.1186\/s13244-024-01646-3"}],"container-title":["International Journal of Gynecology &amp; 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