{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,27]],"date-time":"2026-05-27T12:57:25Z","timestamp":1779886645771,"version":"3.53.1"},"reference-count":14,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2022,10,25]],"date-time":"2022-10-25T00:00:00Z","timestamp":1666656000000},"content-version":"vor","delay-in-days":297,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["BioMed Research International"],"published-print":{"date-parts":[[2022,1]]},"abstract":"<jats:p><jats:italic>Objective<\/jats:italic>. To use the logistic regression model to evaluate the value of ultrasound characteristics in the Ovarian\u2010Adnexal Reporting and Data System ultrasound lexicon in determining ovarian solid component\u2010containing mass benignancy\/malignancy. <jats:italic>Methods<\/jats:italic>. We retrospectively analyzed the data of 172 patients with adnexal masses discovered by ultrasound, and diagnosis was confirmed by postoperative pathological tests from January 2019 to December 2021. Thirteen ovarian tumor\u2010related parameters in the benign and malignant ovarian tumor groups were selected for univariate analyses. Statistically significant parameters were included in multivariate logistic regression analyses to construct a logistic regression diagnosis model, and the diagnostic performance of the model in predicting ovarian malignancies was calculated. <jats:italic>Results<\/jats:italic>. Of the 172 adnexal tumors, 104 were benign, and 68 were malignant. There were differences in cancer antigen 125, maximum mass diameter, maximum solid component diameter, multilocular cyst with solid component, external contour, whether acoustic shadows were present in the solid component, number of papillae, vascularity, presence\/absence of ascites, and presence\/absence of peritoneal thickening or nodules between the benign ovarian tumor and malignancy groups (<jats:italic>p<\/jats:italic> &lt; 0.05). Logistic regression analyses showed that maximum solid component diameter, whether acoustic shadows were present in the solid component, number of papillae, and presence\/absence of ascites were included in the logistic regression model, and the area under the receiver operating characteristic curve for this regression model in predicting ovarian malignancy was 0.962 (95% confidence interval: 0.933~0.990; <jats:italic>p<\/jats:italic> &lt; 0.001). Logit\u2009(<jats:italic>p<\/jats:italic>) \u2265 \u22120.02 was used as the cutoff value, and the prediction accuracy, sensitivity, specificity, positive predictive value, and negative predictive values were 93.6%, 86.8%, 98.1%, 96.7%, and 91.9%, respectively. <jats:italic>Conclusion<\/jats:italic>. The logistic regression model containing the maximum solid component diameter, whether acoustic shadows were present in the solid component, number of papillae, and presence\/absence of ascites can help in determining the benignancy\/malignancy of solid component\u2010containing masses.<\/jats:p>","DOI":"10.1155\/2022\/7187334","type":"journal-article","created":{"date-parts":[[2022,10,25]],"date-time":"2022-10-25T18:50:06Z","timestamp":1666723806000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Application of O\u2010RADS Ultrasound Lexicon\u2010Based Logistic Regression Analysis Model in the Diagnosis of Solid Component\u2010Containing Ovarian Malignancies"],"prefix":"10.1155","volume":"2022","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9928-9718","authenticated-orcid":false,"given":"Hui","family":"Luo","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ziqing","family":"Lin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lijuan","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuying","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haojie","family":"Ning","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanping","family":"Feng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yulu","family":"Cheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoyi","family":"Wen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoyan","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2022,10,25]]},"reference":[{"key":"e_1_2_7_1_2","doi-asserted-by":"publisher","DOI":"10.3322\/caac.21551"},{"key":"e_1_2_7_2_2","doi-asserted-by":"publisher","DOI":"10.3322\/caac.21660"},{"key":"e_1_2_7_3_2","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2019191150"},{"key":"e_1_2_7_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jacr.2019.02.011"},{"key":"e_1_2_7_5_2","first-page":"641","article-title":"Research 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