{"title":"A comprehensive predictive model study: ultrasound viscosity imaging in thyroid nodules.","authors":"Xiaohan Zou, Sijie Mo, Shuzhen Tang, Mengna Shao, Jing Chen, Fengling Chen, Lin Zhou, Zhibin Huang, Fajin Dong","doi":"10.1093/postmj/qgaf224","DOIUrl":null,"url":null,"abstract":"<p><strong>Purpose: </strong>This study aimed to develop a comprehensive predictive model that integrates clinical factors, conventional ultrasound (US) features, and ultrasound viscosity imaging metrics to improve diagnostic accuracy in thyroid nodules and to explore the role of viscosity metrics in differentiating benign and malignant thyroid nodules.</p><p><strong>Methods: </strong>A total of 215 patients with thyroid nodules were included in the study, with 70% in the training set and 30% in the testing set. Univariate and multivariate logistic regression were employed to identify key predictors, resulting in the development of three models. Three models were developed: ModA (clinical factors), ModB (clinical factors and conventional US features), and ModC (clinical factors, conventional US features, and US viscosity imaging). The diagnostic efficacy of the models was evaluated and compared using receiver operating characteristic curves.</p><p><strong>Results: </strong>Multivariate analysis revealed that factors, such as echogenicity, viscosity minimum (Vmin), and viscosity mean (Vmean), were significant risk factors for distinguishing between benign and malignant thyroid nodules. In the testing set, ModA demonstrated an Area Under the Curve (AUC) of 0.777, ModB exhibited an improvement to 0.922, and ModC demonstrated an AUC of 0.945. The findings indicate that ModC exhibits superior predictive accuracy for differentiating between benign and malignant thyroid nodules.</p><p><strong>Conclusion: </strong>The integration of US viscosity imaging with clinical factors and conventional US features can enhance the predictive performance of differentiation in thyroid nodules, offering a reliable clinical tool to support early and accurate diagnosis of thyroid nodules.</p><p><strong>Research questions: </strong></p>","PeriodicalId":20374,"journal":{"name":"Postgraduate Medical Journal","volume":" ","pages":"817-825"},"PeriodicalIF":3.6000,"publicationDate":"2026-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Postgraduate Medical Journal","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1093/postmj/qgaf224","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MEDICINE, GENERAL & INTERNAL","Score":null,"Total":0}
引用次数: 0
Abstract
Purpose: This study aimed to develop a comprehensive predictive model that integrates clinical factors, conventional ultrasound (US) features, and ultrasound viscosity imaging metrics to improve diagnostic accuracy in thyroid nodules and to explore the role of viscosity metrics in differentiating benign and malignant thyroid nodules.
Methods: A total of 215 patients with thyroid nodules were included in the study, with 70% in the training set and 30% in the testing set. Univariate and multivariate logistic regression were employed to identify key predictors, resulting in the development of three models. Three models were developed: ModA (clinical factors), ModB (clinical factors and conventional US features), and ModC (clinical factors, conventional US features, and US viscosity imaging). The diagnostic efficacy of the models was evaluated and compared using receiver operating characteristic curves.
Results: Multivariate analysis revealed that factors, such as echogenicity, viscosity minimum (Vmin), and viscosity mean (Vmean), were significant risk factors for distinguishing between benign and malignant thyroid nodules. In the testing set, ModA demonstrated an Area Under the Curve (AUC) of 0.777, ModB exhibited an improvement to 0.922, and ModC demonstrated an AUC of 0.945. The findings indicate that ModC exhibits superior predictive accuracy for differentiating between benign and malignant thyroid nodules.
Conclusion: The integration of US viscosity imaging with clinical factors and conventional US features can enhance the predictive performance of differentiation in thyroid nodules, offering a reliable clinical tool to support early and accurate diagnosis of thyroid nodules.
目的：本研究旨在建立一个综合临床因素、常规超声（US）特征和超声粘度成像指标的综合预测模型，以提高甲状腺结节的诊断准确性，并探讨粘度指标在甲状腺结节良恶性鉴别中的作用。方法：将215例甲状腺结节患者纳入研究，其中70%为训练组，30%为测试组。采用单变量和多变量逻辑回归来确定关键预测因子，从而开发了三个模型。建立了三种模型：ModA（临床因素）、ModB（临床因素和常规超声特征）和ModC（临床因素、常规超声特征和超声黏度成像）。采用受试者工作特征曲线对模型的诊断效果进行评价和比较。结果：多因素分析显示，回声性、最小黏度（Vmin）、黏度平均值（Vmean）是区分甲状腺结节良恶性的重要危险因素。在测试集中，ModA的曲线下面积（Area Under the Curve， AUC）为0.777，ModB的AUC为0.922，ModC的AUC为0.945。研究结果表明，ModC在区分良性和恶性甲状腺结节方面表现出优越的预测准确性。结论：超声黏度成像结合临床因素及常规超声特征可提高甲状腺结节鉴别的预测效能，为早期准确诊断甲状腺结节提供可靠的临床工具。研究问题:
期刊介绍：
Postgraduate Medical Journal is a peer reviewed journal published on behalf of the Fellowship of Postgraduate Medicine. The journal aims to support junior doctors and their teachers and contribute to the continuing professional development of all doctors by publishing papers on a wide range of topics relevant to the practicing clinician and teacher. Papers published in PMJ include those that focus on core competencies; that describe current practice and new developments in all branches of medicine; that describe relevance and impact of translational research on clinical practice; that provide background relevant to examinations; and papers on medical education and medical education research. PMJ supports CPD by providing the opportunity for doctors to publish many types of articles including original clinical research; reviews; quality improvement reports; editorials, and correspondence on clinical matters.
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