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强回声晕术前评估甲状腺结节良恶性的价值
引用本文:朱凌源,姜云雯,陈文艳,彭敏霞,吴昌财,颜树宏,钱立勇,赵臣银,郑笑娟.强回声晕术前评估甲状腺结节良恶性的价值[J].医学研究杂志,2017,46(7):66-70.
作者姓名:朱凌源  姜云雯  陈文艳  彭敏霞  吴昌财  颜树宏  钱立勇  赵臣银  郑笑娟
作者单位:316000 温州医科大学附属舟山医院,316000 温州医科大学附属舟山医院,316000 温州医科大学附属舟山医院,316000 温州医科大学附属舟山医院,316000 温州医科大学附属舟山医院,316000 温州医科大学附属舟山医院,316000 温州医科大学附属舟山医院,316000 温州医科大学附属舟山医院,316000 温州医科大学附属舟山医院
基金项目:浙江省科技厅公益性技术应用研究计划项目(2012C23059);浙江省医学会临床科研基金项目(2015ZYCA89);舟山市卫计局基金资助项目(2014T01);舟山市科技局基金资助项目(2009-043,2016C31042)
摘    要:目的 探讨强回声晕在甲状腺良恶性结节中的诊断价值及其病理成分。方法 回顾性分析2015年1月~2016年5月在舟山医院就诊的306例患者410个结节,所有结节均经手术切除并且病理证实。患者术前均进行常规超声检查,并由经验丰富的超声医生记录结节直径、位置、数目、纵横比、边缘、边界、回声水平、钙化、血流分级、声晕、强回声晕、与包膜关系、淋巴结转移情况。先进行单因素分析筛选出有统计学意义的指标,然后再进行多因素Logistic回归分析。以病理上结节周围出现纤维浸润带为金标准,构建ROC曲线,探讨强回声晕的病理成份。结果 二元Logistic回归分析筛选出强回声晕(Ps)进入回归模型。强回声晕(常规超声)的ROC曲线下面积为0.748,强回声晕(Ps)的ROC曲线下面积为0.761,强回声晕(常规超声)的阳性预测值为87%,阴性预测值为92.7%,强回声晕(Ps)的阳性预测值为83.3%,阴性预测值为89%。结论 强回声晕对甲状腺结节良恶性的诊断具有一定的价值。强回声晕的病理成分多为癌细胞浸润伴纤维组织及炎性细胞。

关 键 词:常规超声  强回声晕  Ps  纤维浸润带  Logistic回归模型
收稿时间:2016/10/30 0:00:00
修稿时间:2016/11/28 0:00:00

Value of Strong Echogenic Halo in the Preoperative Evaluation of Thyroid Nodule
Zhu Lingyuan,Jiang Yunwen,Chen Wenyan.Value of Strong Echogenic Halo in the Preoperative Evaluation of Thyroid Nodule[J].Journal of Medical Research,2017,46(7):66-70.
Authors:Zhu Lingyuan  Jiang Yunwen  Chen Wenyan
Institution:Department of Ultrasound Center, Affiliated Zhoushan Hospital of Wenzhou Medical University, Zhejiang 316000, China,Department of Ultrasound Center, Affiliated Zhoushan Hospital of Wenzhou Medical University, Zhejiang 316000, China,Department of Ultrasound Center, Affiliated Zhoushan Hospital of Wenzhou Medical University, Zhejiang 316000, China,Department of Ultrasound Center, Affiliated Zhoushan Hospital of Wenzhou Medical University, Zhejiang 316000, China,Department of Ultrasound Center, Affiliated Zhoushan Hospital of Wenzhou Medical University, Zhejiang 316000, China,Department of Ultrasound Center, Affiliated Zhoushan Hospital of Wenzhou Medical University, Zhejiang 316000, China,Department of Ultrasound Center, Affiliated Zhoushan Hospital of Wenzhou Medical University, Zhejiang 316000, China,Department of Ultrasound Center, Affiliated Zhoushan Hospital of Wenzhou Medical University, Zhejiang 316000, China and Department of Ultrasound Center, Affiliated Zhoushan Hospital of Wenzhou Medical University, Zhejiang 316000, China
Abstract:Objective To discuss the diagnostic value of echogenic halo in benign and malignant thyroid nodules and to find the Pathological components. Methods Retrospective analysis of 306 patients (410 nodules) from January 2015 to May 2016 in Zhoushan hospital was performed. All nodules were cut by surgery and confirmed by pathology. All patients were performed conventional ultrasound before operation. The size, location, number, aspect ratio, edge, boundary, echogenic level, calcification, acoustic halo, rear echo, strong echo halo, relation to the envelope were recorded by experienced ultrasonic docter. Firstly, univariate analysis was used to reveal statistically significant indicators, then multivariate logistic regression analysis was demonstrated. Appearing of fiber infiltration ribbon around the pathological nodules as the gold standard, the ROC curve was used to investigate the pathologic features of hyperechoic halo. Results The Logistic regression model selected strong echo halo (Ps) into the model (P<0.05). The area under the echogenic halo (conventional ultrasound) of the ROC curve was 0.748 and the area under the echogenic halo (Ps) of the ROC curve was 0.761. The positive predictive value of echogenic halo (conventional ultrasound) was 87% while the negative was 92.7%. The positive predictive value of echogenic halo (Ps) was 83.3% while the negative was 89%. Conclusion Strong echo halo for the diagnosis of benign and malignant thyroid nodules have a certain value. The pathological components of hyperechoic halo are mostly cancer cells infiltration with fibrous tissue and inflammatory cells.
Keywords:Conventional ultrasound  Echogenic  Ps  Fiber infiltration ribbon  Logistic regresstion model
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