{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,19]],"date-time":"2026-10-19T17:51:13Z","timestamp":1792432273901,"version":"4.5.0"},"reference-count":41,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2026,3,30]],"date-time":"2026-03-30T00:00:00Z","timestamp":1774828800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,3,30]],"date-time":"2026-03-30T00:00:00Z","timestamp":1774828800000},"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":["Ultrasound in Obstet &amp; Gyne"],"published-print":{"date-parts":[[2026,4]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>The primary aim of this study was to develop and internally validate ultrasound\u2010based radiomics models to discriminate between all types of benign and malignant adnexal masses. The secondary aim was to compare the performance of the radiomics models with that of the Assessment of Different NEoplasias in the adneXa (ADNEX) model.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      This was a retrospective, observational, single\u2010center study, for which all patients with an adnexal mass that were included in the ongoing International Ovarian Tumor Analysis phase\u20105 and phase\u20107 studies and were examined using ultrasound between January 2012 and December 2023 at Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy, were eligible for inclusion. Inclusion criteria were: adnexal mass detected by ultrasound; surgical removal of the adnexal mass within 180\u2009days after the ultrasound examination; histological confirmation of an adnexal mass; and absence of a synchronous malignant tumor. Patients without digital ultrasound images saved in DICOM format were excluded. The patient cohort was split randomly into training and validation sets using a stratified split with a ratio of 70:30, to preserve the proportion of benign and malignant cases in the two sets. Two machine\u2010learning models for discriminating between benign and malignant adnexal\u00a0masses were built using one image per tumor, with 5\u2010fold cross\u2010validation for hyperparameter tuning, and were tested on the validation set. The variables used in model building were patient age, serum CA\u2009125 level and the radiomics features that differed significantly between benign and malignant tumors (determined using the Mann\u2013Whitney\n                      <jats:italic>U<\/jats:italic>\n                      \u2010test with Benjamini\u2013Hochberg correction) and were not redundant based on Pearson correlation analysis. Histology was the reference standard. We assessed the discriminative performance of the radiomics models using the area under the receiver\u2010operating\u2010characteristics curve (AUC) and classification performance using sensitivity and specificity at the optimal cut\u2010off of each model to classify the mass as malignant, as determined by Youden's index. The diagnostic performance of the developed radiomics models was compared with that of the ADNEX model (AUC, sensitivity and specificity at the 10% risk\u2010of\u2010malignancy cut\u2010off, which is the recommended threshold for clinical use of the ADNEX model).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      In total, 4501 patients met the inclusion criteria. Among these, 2428 patients were excluded owing to an absence of ultrasound images or images unsuitable for radiomics analysis. Overall, a total of 2073 patients were included in the analysis, of whom 803 (38.7%) had a histologically confirmed malignant tumor. In the validation set (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u2009622, including 254 malignancies), the clinical\u2013radiomics model trained using the eXtreme Gradient Boosting algorithm, including age, serum CA\u2009125 level and 14 selected radiomics features,\u00a0achieved the highest performance, with an AUC of 0.89 (95%\u2009CI, 0.86\u20130.92), sensitivity of 0.83 (95%\u2009CI, 0.79\u20130.88) and specificity of 0.81 (95%\u2009CI, 0.77\u20130.85) at the optimal cut\u2010off (31% risk of malignancy, based on Youden's index). At a 10% risk\u2010of\u2010malignancy cut\u2010off, it had a sensitivity of 0.94 (95%\u2009CI, 0.91\u20130.97) and specificity of 0.48 (95%\u2009CI, 0.42\u20130.53). The ADNEX model had an AUC of 0.95 (95%\u2009CI, 0.93\u20130.97), sensitivity of 0.97 (95%\u2009CI, 0.95\u20130.99) and specificity of 0.72 (95%\u2009CI, 0.68\u20130.77) at the 10% risk\u2010of\u2010malignancy cut\u2010off in the validation set.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>\n                      Our results support further exploration of radiomics analysis for distinguishing between benign and malignant adnexal masses in larger study populations. Future studies should consider using multiple images per tumor and testing alternative model\u2010building methods, and should perform external validation to assess the generalizability of the radiomics models. \u00a9 2026 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.70203","type":"journal-article","created":{"date-parts":[[2026,3,30]],"date-time":"2026-03-30T09:29:44Z","timestamp":1774862984000},"page":"519-529","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Developing and validating ultrasound\u2010based machine\u2010learning models incorporating radiomics features to predict malignancy in adnexal masses"],"prefix":"10.1002","volume":"67","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5070-7245","authenticated-orcid":false,"given":"F.","family":"Moro","sequence":"first","affiliation":[{"name":"UniCamillus\u2010International Medical University of Rome  Rome Italy"},{"name":"Dipartimento Scienze della Salute della Donna e del Bambino Fondazione Policlinico Universitario Agostino Gemelli IRCCS  Rome Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-9443-1834","authenticated-orcid":false,"given":"M.","family":"Ciancia","sequence":"additional","affiliation":[{"name":"Dipartimento Scienze della Salute della Donna e del Bambino Fondazione Policlinico Universitario Agostino Gemelli IRCCS  Rome Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"S. di","family":"Berardino","sequence":"additional","affiliation":[{"name":"Dipartimento Scienze della Salute della Donna e del Bambino Fondazione Policlinico Universitario Agostino Gemelli IRCCS  Rome Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8172-1093","authenticated-orcid":false,"given":"G.","family":"Baldassari","sequence":"additional","affiliation":[{"name":"Radiomics G\u2010STeP Research Core Facility, Fondazione Policlinico Universitario Agostino Gemelli IRCCS  Rome Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0736-3114","authenticated-orcid":false,"given":"F.","family":"Mascilini","sequence":"additional","affiliation":[{"name":"Dipartimento Scienze della Salute della Donna e del Bambino Fondazione Policlinico Universitario Agostino Gemelli IRCCS  Rome Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2104-0602","authenticated-orcid":false,"given":"F.","family":"Ciccarone","sequence":"additional","affiliation":[{"name":"Dipartimento Scienze della Salute della Donna e del Bambino Fondazione Policlinico Universitario Agostino Gemelli IRCCS  Rome Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4442-4046","authenticated-orcid":false,"given":"C.","family":"Nero","sequence":"additional","affiliation":[{"name":"Dipartimento Scienze della Salute della Donna e del Bambino Fondazione Policlinico Universitario Agostino Gemelli IRCCS  Rome Italy"},{"name":"Dipartimento Universitario Scienze della Vita e Sanit\u00e0 Pubblica, Universit\u00e0 Cattolica del Sacro Cuore  Rome Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7176-9090","authenticated-orcid":false,"given":"H. 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