Article(id=1320381058676281898, tenantId=1146029695717560320, journalId=1319373289681944729, issueId=1320381045866873144, articleNumber=null, orderNo=null, doi=10.11876/mimt202601011, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=null, receivedDateStr=null, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1792732975881, onlineDateStr=2026-10-23, pubDate=1770652800000, pubDateStr=2026-02-10, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1792732975881, onlineIssueDateStr=2026-10-23, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1792732975881, creator=13701087609, updateTime=1792732975881, updator=13701087609, issue=Issue{id=1320381045866873144, tenantId=1146029695717560320, journalId=1319373289681944729, year='2026', volume='32', issue='1', pageStart='1', pageEnd='106', issueExtLink='null', onlineDate='null', pubDate='1770652800000', pubDateStr='2026-02-10', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1792732972828, creator='13701087609', updateTime=1792746352003, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1320437162382840024, tenantId=1146029695717560320, journalId=1319373289681944729, issueId=1320381045866873144, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1320437162382840025, tenantId=1146029695717560320, journalId=1319373289681944729, issueId=1320381045866873144, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=74, endPage=81, ext={EN=ArticleExt(id=1320381058877608491, articleId=1320381058676281898, tenantId=1146029695717560320, journalId=1319373289681944729, language=EN, title=Application of multi-parameter MRI in differential diagnosis of benign and malignant thyroid nodules, columnId=null, journalTitle=Modern Instruments & Medical Treatment, columnName=null, runingTitle=null, highlight=null, articleAbstract=
Objective

To explore the application value of multiparametric MRI techniques, including conventional MRI, contrast-enhanced MRI (CE-MRI), and dynamic contrast-enhanced MRI (DCE-MRI), in differentiating benign from malignant thyroid nodules.

Methods

A retrospective analysis was performed on 74 patients with 89 thyroid lesions who were confirmed by surgical pathology with definite pathological results and preoperative magnetic resonance imaging. Observe the nodule boundary and whether the nodule has become cystic. Nodule size, apparent diffusion coefficient (ADC), and various quantitative parameters including volume transfer constant (Ktrans), extracellular space volume fraction outside blood vessels (Ve), vascular return constant (Kep), initial area under the gadolinium-concentration–time curve (iAUC) from DCE-MRI were measured, and recorded. Independent sample t-tests and Mann-Whitney U tests were used to compare parameters between benign and malignant thyroid nodules. Multifactorial logistic regression was employed to select independent predictive factors and establish a combined diagnostic model, with ROC curves analyzed to assess the performance of individual parameters and the combined model.

Results

Differences in lesion margin, ADC, Ktrans, and iAUC between benign and malignant thyroid nodules were all statistically significant (P<0.05). Multivariate logistic regression with stepwise selection identified iAUC, ADC, and margin status as the optimal predictor set; the corresponding odds ratios were 0.890, 0.984, and 64.759, respectively. The area under the ROC curve for this model was 0.986, with a sensitivity of 94.1%, and a specificity of 89.4%. Internal validation demonstrated excellent generalizability, with an AUC of 0.979 in the validation cohort.

Conclusion

This study shows that multiparametric MRI can improve the ability to distinguish between benign and malignant thyroid nodules. A combined diagnostic model incorporating independent risk factors such as iAUC value, ADC value, and boundary clarity has a high diagnostic value.

, authors=Jin Feng1, Jinlin Miao1, Jun Zhu1, Kai Hu1, Jianguo Xia2, *, authorsList=Jin Feng, Jinlin Miao, Jun Zhu, Kai Hu, Jianguo Xia, authorCompany=null, correspAuthors=Jianguo Xia, authorNote=null, correspAuthorsNote=null, copyrightStatement=All copyrights in 2026 belong to the sponsor of the journal.Not content published by the journal may be reproduced or abridged without authorization., copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1320381059187986989, articleId=1320381058676281898, tenantId=1146029695717560320, journalId=1319373289681944729, language=CN, title=MRI多参数成像在甲状腺良恶性结节鉴别中的应用研究, columnId=1320381058969883180, journalTitle=现代仪器与医疗, columnName=临床研究, runingTitle=null, highlight=null, articleAbstract=
目的

探讨MRI平扫、常规增强及动态对比增强扫描的单一参数以及多参数成像技术在甲状腺良恶性结节鉴别中的应用价值。

方法

回顾性分析2022年4月至2023年12月泰州市第四人民医院经手术病理证实并行术前甲状腺MRI检查的74例甲状腺病变患者的共89枚结节,观察结节边界、结节是否伴有囊变,测量、记录结节大小、表观扩散系数(apparent diffusion coefficient,ADC)值及MRI动态对比增强各定量参数,包括转运常数(Ktrans)值、血管外细胞外间隙容积分数(Ve)值、血管回流常数(Kep)值、受试者工作特征初始曲线,下面积(iAUC)值。采用独立样本t检验及Mann-Whitney U检验比较甲状腺良恶性结节各项参数。采用多因素Logistic回归筛选独立预测因素并建立联合诊断模型,绘制受试者工作特征(receiver operating characteristic,ROC)曲线,分析各参数及联合模型性能。

结果

良恶性甲状腺结节的边界、ADC值、Ktrans值、iAUC值的差异具有统计学意义(P<0.05)。运用多因素Logistic回归分析对所有的参数进行筛选,最终结果显示iAUC值(OR=0.890)、ADC值(OR=0.984)、边界(OR=64.759)是鉴别甲状腺结节良恶性的独立预测因子,其ROC曲线下面积(area under curve,AUC)分别为0.807、0.917、0.836,三者联合诊断模型的AUC为0.986,敏感度为94.1%,特异度为89.4%。内部验证显示该模型具有良好的泛化能力(验证集AUC=0.979)。

结论

甲状腺MRI多参数成像可以提高甲状腺良恶性结节鉴别的能力,联合独立预测因子iAUC值、ADC值及边界的诊断模型具有较高的诊断性能。

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冯进,男,硕士研究生,主治医师,研究方向:磁共振成像

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夏建国,男,硕士研究生,主任医师,研究方向:磁共振成像
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MRI多参数成像在甲状腺良恶性结节鉴别中的应用研究
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冯进 1 , 缪锦林 1 , 朱骏 1 , 胡凯 1 , 夏建国 2, *
现代仪器与医疗 | 临床研究 2026,32(1): 74-81
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现代仪器与医疗 |临床研究 2026 , 32 (1) : 74 -81
MRI多参数成像在甲状腺良恶性结节鉴别中的应用研究
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冯进1, 缪锦林1, 朱骏1, 胡凯1, 夏建国2, *
作者信息
  • 1.江苏省泰州市第四人民医院放射科,泰州 225300
  • 2.南京医科大学附属泰州市人民医院放射科,泰州 225300
通讯作者:
夏建国,男,硕士研究生,主任医师,研究方向:磁共振成像
作者简介:

冯进,男,硕士研究生,主治医师,研究方向:磁共振成像

Application of multi-parameter MRI in differential diagnosis of benign and malignant thyroid nodules
Jin Feng1, Jinlin Miao1, Jun Zhu1, Kai Hu1, Jianguo Xia2, *
Affiliations
  • 1.Department of Radiology, Taizhou Fourth People's Hospital, Taizhou 225300, China
  • 2.Department of Radiology, the Affiliated Taizhou People's Hospital of Nanjing Medical University, Taizhou 225300, China
出版时间: 2026-02-10 doi: 10.11876/mimt202601011
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目的

探讨MRI平扫、常规增强及动态对比增强扫描的单一参数以及多参数成像技术在甲状腺良恶性结节鉴别中的应用价值。

方法

回顾性分析2022年4月至2023年12月泰州市第四人民医院经手术病理证实并行术前甲状腺MRI检查的74例甲状腺病变患者的共89枚结节,观察结节边界、结节是否伴有囊变,测量、记录结节大小、表观扩散系数(apparent diffusion coefficient,ADC)值及MRI动态对比增强各定量参数,包括转运常数(Ktrans)值、血管外细胞外间隙容积分数(Ve)值、血管回流常数(Kep)值、受试者工作特征初始曲线,下面积(iAUC)值。采用独立样本t检验及Mann-Whitney U检验比较甲状腺良恶性结节各项参数。采用多因素Logistic回归筛选独立预测因素并建立联合诊断模型,绘制受试者工作特征(receiver operating characteristic,ROC)曲线,分析各参数及联合模型性能。

结果

良恶性甲状腺结节的边界、ADC值、Ktrans值、iAUC值的差异具有统计学意义(P<0.05)。运用多因素Logistic回归分析对所有的参数进行筛选,最终结果显示iAUC值(OR=0.890)、ADC值(OR=0.984)、边界(OR=64.759)是鉴别甲状腺结节良恶性的独立预测因子,其ROC曲线下面积(area under curve,AUC)分别为0.807、0.917、0.836,三者联合诊断模型的AUC为0.986,敏感度为94.1%,特异度为89.4%。内部验证显示该模型具有良好的泛化能力(验证集AUC=0.979)。

结论

甲状腺MRI多参数成像可以提高甲状腺良恶性结节鉴别的能力,联合独立预测因子iAUC值、ADC值及边界的诊断模型具有较高的诊断性能。

甲状腺结节  /  良恶性鉴别  /  多参数  /  磁共振成像  /  动态对比增强
Objective

To explore the application value of multiparametric MRI techniques, including conventional MRI, contrast-enhanced MRI (CE-MRI), and dynamic contrast-enhanced MRI (DCE-MRI), in differentiating benign from malignant thyroid nodules.

Methods

A retrospective analysis was performed on 74 patients with 89 thyroid lesions who were confirmed by surgical pathology with definite pathological results and preoperative magnetic resonance imaging. Observe the nodule boundary and whether the nodule has become cystic. Nodule size, apparent diffusion coefficient (ADC), and various quantitative parameters including volume transfer constant (Ktrans), extracellular space volume fraction outside blood vessels (Ve), vascular return constant (Kep), initial area under the gadolinium-concentration–time curve (iAUC) from DCE-MRI were measured, and recorded. Independent sample t-tests and Mann-Whitney U tests were used to compare parameters between benign and malignant thyroid nodules. Multifactorial logistic regression was employed to select independent predictive factors and establish a combined diagnostic model, with ROC curves analyzed to assess the performance of individual parameters and the combined model.

Results

Differences in lesion margin, ADC, Ktrans, and iAUC between benign and malignant thyroid nodules were all statistically significant (P<0.05). Multivariate logistic regression with stepwise selection identified iAUC, ADC, and margin status as the optimal predictor set; the corresponding odds ratios were 0.890, 0.984, and 64.759, respectively. The area under the ROC curve for this model was 0.986, with a sensitivity of 94.1%, and a specificity of 89.4%. Internal validation demonstrated excellent generalizability, with an AUC of 0.979 in the validation cohort.

Conclusion

This study shows that multiparametric MRI can improve the ability to distinguish between benign and malignant thyroid nodules. A combined diagnostic model incorporating independent risk factors such as iAUC value, ADC value, and boundary clarity has a high diagnostic value.

Thyroid nodules  /  Benign and malignant discrimination  /  Multiparametric  /  Magnetic resonance imaging  /  Dynamic contrast-enhanced
冯进, 缪锦林, 朱骏, 胡凯, 夏建国. MRI多参数成像在甲状腺良恶性结节鉴别中的应用研究. 现代仪器与医疗, 2026 , 32 (1) : 74 -81 . DOI: 10.11876/mimt202601011
Jin Feng, Jinlin Miao, Jun Zhu, Kai Hu, Jianguo Xia. Application of multi-parameter MRI in differential diagnosis of benign and malignant thyroid nodules[J]. Modern Instruments & Medical Treatment, 2026 , 32 (1) : 74 -81 . DOI: 10.11876/mimt202601011

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doi: 10.11876/mimt202601011
  • 首发时间:2026-10-23
  • 出版时间:2026-02-10
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    1.江苏省泰州市第四人民医院放射科,泰州 225300
    2.南京医科大学附属泰州市人民医院放射科,泰州 225300

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夏建国,男,硕士研究生,主任医师,研究方向:磁共振成像
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