{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,22]],"date-time":"2026-08-22T06:26:02Z","timestamp":1787379962253,"version":"build-2736575974"},"reference-count":135,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2025,12,4]],"date-time":"2025-12-04T00:00:00Z","timestamp":1764806400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2025,12,4]],"date-time":"2025-12-04T00:00:00Z","timestamp":1764806400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100001826","name":"ZonMw","doi-asserted-by":"publisher","award":["852002128"],"award-info":[{"award-number":["852002128"]}],"id":[{"id":"10.13039\/501100001826","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["obgyn.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Ultrasound in Obstet &amp;amp; Gyne"],"published-print":{"date-parts":[[2026,5]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>Accurate preoperative classification of ovarian tumors is essential for guiding treatment. There is an increasing body of data evaluating ultrasound\u2010based models for this purpose in diverse clinical settings. The aim of this systematic review and meta\u2010analysis was to generate up\u2010to\u2010date evidence on the diagnostic accuracy of the most relevant ultrasound\u2010based models, including the Risk of Malignancy Index (RMI) versions 1, 2 and 3, Logistic Regression model 2 (LR2), Simple ultrasound\u2010based Rules (SR), the Assessment of Different NEoplasias in the adneXa (ADNEX) model and subjective assessment (SA), for the differentiation between benign and malignant ovarian tumors.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Ovid\/MEDLINE, EMBASE and the Cochrane Library were searched systematically from database inception until 19 June 2025. Eligible studies investigated the diagnostic accuracy of at least one of the preselected models, collected model parameters prospectively and provided sufficient data to construct 2\u2009\u00d7\u20092 tables. The risk of bias of all included studies was assessed using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS)\u20102 and QUADAS\u2010C extension tools. Pooled summary estimates of sensitivity and specificity for all included models were calculated and bivariate models were fitted into hierarchical summary receiver\u2010operating\u2010characteristics curves. Bivariate random\u2010effects meta\u2010regression analysis was conducted to determine significant differences in sensitivity and specificity between models. Subgroup analyses were conducted according to menopausal status and prevalence of ovarian malignancy.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      A total of 99 studies were included, describing 42\u2009496 ovarian tumors, of which 31\u2009371 (74%) were benign and 11\u2009125 (26%) were malignant. SA had both high sensitivity (90.2% (95%\u2009CI, 87.8\u201392.2%)) and high specificity (91.4% (95%\u2009CI, 89.3\u201393.2%)). SR followed by SA of inconclusive cases (SR\u2009+\u2009SA) showed similar performance to SA (sensitivity, 88.6% (95%\u2009CI, 85.7\u201391.0%);\n                      <jats:italic>P<\/jats:italic>\n                      \u2009=\u20090.397 and specificity, 91.0% (95%\u2009CI, 89.0\u201392.7%);\n                      <jats:italic>P<\/jats:italic>\n                      \u2009=\u20090.811), as did the ADNEX model with a cut\u2010off of 20% (sensitivity, 86.7% (95%\u2009CI, 80.6\u201391.0%);\n                      <jats:italic>P<\/jats:italic>\n                      \u2009=\u20090.095; specificity, 87.9% (95%\u2009CI, 80.1\u201392.9%),\n                      <jats:italic>P<\/jats:italic>\n                      \u2009=\u20090.119). The ADNEX model with a cut\u2010off of 10% had a similar sensitivity to SA (92.7% (95%\u2009CI, 90.8\u201394.2%);\n                      <jats:italic>P<\/jats:italic>\n                      \u2009=\u20090.130), but lower specificity (78.4% (95%\u2009CI, 71.7\u201383.8%);\n                      <jats:italic>P<\/jats:italic>\n                      \u2009&lt;\u20090.001). Higher cut\u2010offs of the ADNEX model led to a decrease in sensitivity, whereas lower cut\u2010offs resulted in reduced specificity. The LR2 model with a 10% cut\u2010off had a sensitivity of 89.5% (95%\u2009CI, 85.8\u201392.4%) and a specificity of 82.3% (95%\u2009CI, 75.0\u201387.8%). The RMI had the lowest diagnostic accuracy, with a sensitivity of 69.7% (95%\u2009CI, 67.0\u201372.2%) and a specificity of 90.5% (95%\u2009CI, 88.3\u201392.4%) for RMI version 1 with a cut\u2010off of 200. Subgroup analyses showed that both menopausal status and prevalence of malignancy significantly affected sensitivity (\n                      <jats:italic>P<\/jats:italic>\n                      \u2009&lt;\u20090.01) and specificity (\n                      <jats:italic>P<\/jats:italic>\n                      \u2009&lt;\u20090.01). Postmenopausal status and higher disease prevalence were associated with lower specificity, while sensitivity was lower in premenopausal women.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>\n                      All approaches, except for the RMI, performed well and could be used to differentiate between benign and malignant ovarian tumors. Although SA with or without SR had the highest diagnostic performance, it is dependent on operator expertise. If a strategy independent of operator expertise is preferred, the ADNEX model is recommended. Because of the high sensitivity of the ADNEX model, the likelihood of missing malignancies is low. In postmenopausal women, however, the reduced specificity may warrant a higher cut\u2010off, depending on how the impact of a false\u2010positive test result is evaluated. \u00a9 2025 The Author(s).\n                      <jats:italic>Ultrasound in Obstetrics &amp; Gynecology<\/jats:italic>\n                      published by John Wiley &amp; Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/uog.70135","type":"journal-article","created":{"date-parts":[[2025,12,5]],"date-time":"2025-12-05T06:27:01Z","timestamp":1764916021000},"page":"590-603","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Diagnostic accuracy of ultrasound models for assessment of ovarian tumors: systematic review and meta\u2010analysis"],"prefix":"10.1002","volume":"67","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4495-9622","authenticated-orcid":false,"given":"E.","family":"Lems","sequence":"first","affiliation":[{"name":"Department of Obstetrics and Gynaecology M\u00e1xima Medical Center  Veldhoven The Netherlands"},{"name":"Maastricht University Medical Center and Research School Grow  Maastricht The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"A. 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