{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,26]],"date-time":"2026-08-26T19:54:11Z","timestamp":1787774051780,"version":"build-2784847793"},"reference-count":46,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T00:00:00Z","timestamp":1772755200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T00:00:00Z","timestamp":1772755200000},"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>To develop machine\u2010learning models that incorporate clinical information and radiomics features extracted from ultrasound images to distinguish uterine sarcomas from leiomyomas.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This retrospective, multicenter, pilot case\u2013control study included 200 patients (100 with a uterine sarcoma and 100 with a usual\u2010type leiomyoma, i.e. including no benign leiomyoma variants) who underwent preoperative ultrasound examination between January 2010 and June 2022. The patient cohort was split (70:30) into training and validation sets, with the same proportion of leiomyomas and sarcomas in each subset. We extracted radiomics features belonging to different families: intensity\u2010based statistical features and textural features. The variables used in model building were patient age and the radiomics features that differed statistically significantly between sarcomas and leiomyomas and that were not redundant based on Spearman's correlation coefficient. Logistic regression, random forest, extreme gradient boosting (XGBoost) and support vector machine models were tested in the model development process. We evaluated the performance of the models in differentiating between sarcomas and leiomyomas using the area under the receiver\u2010operating\u2010characteristics curve (AUC), accuracy, sensitivity and specificity. We compared these results to those of subjective assessment by the original ultrasound examiner and to those of two independent expert ultrasound examiners who, blinded to clinical history, reviewed the same grayscale ultrasound images as those used for the radiomics analysis.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Sixty\u2010three radiomics features were extracted. Of these, eight differed statistically significantly between sarcomas and leiomyomas and were not correlated, so were selected for inclusion in model building. In the validation set, the model that performed best in differentiating between sarcomas and leiomyomas was an XGBoost model integrating patient age and radiomics features. In the validation set, this model had an AUC of 0.93, sensitivity of 0.93 and specificity of 0.83, at a risk\u2010of\u2010malignancy cut\u2010off of 47% (the cut\u2010off that yielded the highest number of correct classifications based on Youden's index in the training set). The corresponding results for the model integrating only the radiomics features were: AUC of 0.87, sensitivity of 0.87 and specificity of 0.83. Subjective assessment by the original ultrasound examiner had a sensitivity of 0.87 and specificity of 1 in the validation set, while retrospective review of grayscale ultrasound images by ultrasound experts had a sensitivity of 0.87 and specificity of 0.80 (same results for both reviewers).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>\n                      A model including eight radiomics features and patient age demonstrated reasonably good discriminative and classification performance for distinguishing uterine sarcomas from leiomyomas. Its classification ability was similar to that of subjective assessment by the original ultrasound examiner, being more sensitive but less specific. To confirm the role of radiomics for discriminating between uterine sarcomas and leiomyomas, large prospective studies including benign leiomyoma variants are needed. If good performance of radiomics models can be confirmed, integrating automated radiomics analysis into ultrasound machine software may help ultrasound examiners to discriminate between sarcomas and benign leiomyomas. \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.70187","type":"journal-article","created":{"date-parts":[[2026,3,8]],"date-time":"2026-03-08T22:15:49Z","timestamp":1773008149000},"page":"530-541","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Radiomics\u2010based\n                    <scp>ultrasOund<\/scp>\n                    Model for differentiating Uterine Sarcomas from leiomyomas (\n                    <scp>ROMUS<\/scp>\n                    ): a retrospective pilot Multicenter Italian Trials in Ovarian Cancer (\n                    <scp>MITO<\/scp>\n                    ) study"],"prefix":"10.1002","volume":"67","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2104-0602","authenticated-orcid":false,"given":"F.","family":"Ciccarone","sequence":"first","affiliation":[{"name":"Gynecologic Oncology Unit, Department of Woman and Child Health and Public Health Fondazione Policlinico Universitario \u2018A. Gemelli\u2019 IRCCS  Rome Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"A.","family":"Rizzi","sequence":"additional","affiliation":[{"name":"Gynecologic Oncology Unit, Department of Woman and Child Health and Public Health Fondazione Policlinico Universitario \u2018A. Gemelli\u2019 IRCCS  Rome Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"A.","family":"Biscione","sequence":"additional","affiliation":[{"name":"Ovarian Cancer Center Candiolo Cancer Institute, FPO\u2010IRCCS  Turin 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 \u2018A. Gemelli\u2019 IRCCS  Rome Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"E. H.","family":"Tran","sequence":"additional","affiliation":[{"name":"Radiomics G\u2010STeP Research Core Facility, Fondazione Policlinico Universitario \u2018A. Gemelli\u2019 IRCCS  Rome Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"T.","family":"Pasciuto","sequence":"additional","affiliation":[{"name":"Data Collection G\u2010STeP Research Core Facility, Fondazione Policlinico Universitario \u2018A. Gemelli\u2019 IRCCS  Rome Italy"},{"name":"Section of Hygiene, University Department of Life Sciences and Public Health, Universit\u00e0 Cattolica Del Sacro Cuore  Rome Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5070-7245","authenticated-orcid":false,"given":"F.","family":"Moro","sequence":"additional","affiliation":[{"name":"Gynecologic Oncology Unit, Department of Woman and Child Health and Public Health Fondazione Policlinico Universitario \u2018A. 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