Guo Chen, Yanfang Wang, Guojuan Wang, Rong Yang, Ran Zheng, Yuanqing Zhang, Xiaorong Lv, Fang Nie
{"title":"Diagnostic analysis of contrast-enhanced ultrasound features in malignant partially cystic thyroid nodules.","authors":"Guo Chen, Yanfang Wang, Guojuan Wang, Rong Yang, Ran Zheng, Yuanqing Zhang, Xiaorong Lv, Fang Nie","doi":"10.1177/13860291261430999","DOIUrl":null,"url":null,"abstract":"<p><p>ObjectivePartially cystic thyroid nodules (PCTNs) are frequently underestimated in clinical practice and managed conservatively, potentially increasing the risk of cervical lymph node metastasis. This study aims to explore the contrast-enhanced ultrasound features associated with malignancy in PCTNs.MethodsThis retrospective study included 94 PCTNs from 87 patients in the Second Hospital of Lanzhou University between January 2019 and December 2024. Conventional ultrasound, contrast-enhanced ultrasound (CEUS) features, and clinical data of the nodules were analyzed. Using pathological diagnosis as the gold standard, univariate and multivariate logistic regression analyses were conducted to identify independent predictors of malignancy in PCTNs. A predictive model was subsequently developed, and its diagnostic performance was assessed using receiver operating characteristic (ROC) curve analysis.ResultsCentrifugal perfusion, an ill-defined enhancement boundary, faster washout, and the absence of a peripheral high-enhancement ring are independent predictors of malignant PCTNs. The resulting predictive model exhibited excellent diagnostic performance for PCTNs, with a sensitivity of 92.7%, a specificity of 92.5%, a Youden index of 0.852, and an AUC of 0.968.ConclusionsCEUS features are valuable in differentiating malignant nodules from benign PCTNs, improving physicians' diagnostic confidence in distinguishing benign and malignant PCTNs, and assisting clinicians in clinical decision-making.</p>","PeriodicalId":93943,"journal":{"name":"Clinical hemorheology and microcirculation","volume":" ","pages":"13860291261430999"},"PeriodicalIF":0.0000,"publicationDate":"2026-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Clinical hemorheology and microcirculation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1177/13860291261430999","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
ObjectivePartially cystic thyroid nodules (PCTNs) are frequently underestimated in clinical practice and managed conservatively, potentially increasing the risk of cervical lymph node metastasis. This study aims to explore the contrast-enhanced ultrasound features associated with malignancy in PCTNs.MethodsThis retrospective study included 94 PCTNs from 87 patients in the Second Hospital of Lanzhou University between January 2019 and December 2024. Conventional ultrasound, contrast-enhanced ultrasound (CEUS) features, and clinical data of the nodules were analyzed. Using pathological diagnosis as the gold standard, univariate and multivariate logistic regression analyses were conducted to identify independent predictors of malignancy in PCTNs. A predictive model was subsequently developed, and its diagnostic performance was assessed using receiver operating characteristic (ROC) curve analysis.ResultsCentrifugal perfusion, an ill-defined enhancement boundary, faster washout, and the absence of a peripheral high-enhancement ring are independent predictors of malignant PCTNs. The resulting predictive model exhibited excellent diagnostic performance for PCTNs, with a sensitivity of 92.7%, a specificity of 92.5%, a Youden index of 0.852, and an AUC of 0.968.ConclusionsCEUS features are valuable in differentiating malignant nodules from benign PCTNs, improving physicians' diagnostic confidence in distinguishing benign and malignant PCTNs, and assisting clinicians in clinical decision-making.