摘要
目的:探讨超声造影(CEUS)参数成像在甲状腺微小乳头状癌(PTMC)鉴别诊断中的应用价值。方法:回顾性分析经手术切除的60个甲状腺结节(最大径≤1.0 cm)的超声造影资料,用QontraXt软件定量分析结节灌注峰值(Peak)、达峰时间(TP)、灌注率(Shapness)、曲线下面积(AUC);据手术病理结果分为良性组(n=28)和恶性组(n=32),分析CEUS参数在PTMC鉴别诊断中的价值。结果:超声造影定量分析结果显示,恶性组Peak、AUC显著小于良性组,两组比较差异有统计学意义(P<0.05);TP、Shapness良恶性组之间比较差异无统计学意义(P>0.05)。Peak、AUC诊断PTMC的ROC曲线下面积分别为0.819和0.738,最佳截断值(Cutoff值)分别为50.25和9.5;Peak诊断PTMC的敏感性、准确度高于AUC。结论:CEUS参数成像在PTMC的鉴别诊断中具有重要价值,其中Peak的价值最大,可作为鉴别PTMC的重要参考指标。
Objective:To investigate the value of parametric imaging of contrast-enhanced ultrasound(CEUS)in differential diagnosis of papillary thyroid microcarcinoma(PTMC).Methods:The CEUS data of 60 thyroid nodules(maximum diameter<1.0 cm)via surgical resection were retrospectively analyzed.The Peak,the time to peak(TP),the Shapness,the area under the curve(AUC)of nodules were quantitatiely analyzed by QontraXt software.According to the pathological results of surgery,the nodules were divided into benign group(n=28)and malignant group(n=32).Results:Peak and AUC were significantly lower in malignant group than those in benign group,and the difference was statistically significant(P<0.05).There was no statistically significant difference in TP and Shappness between benign group and malignant group(P>0.05).The area under the ROC curve of Peak and AUC in diagnosing PTMC were respectively 0.819 and 0.738,the best cutoff value were respectively 50.25 and 9.50.The sensitivity and accuracy of Peak in diagnosing PTMC were higher than AUC.Conclusion:The parametric imaging of CEUS has important value in the differential diagnosis of PTMC,among which Peak has the best value and can be used as an important reference index.
作者
王双龙
吕镔
李龙
马香玲
赵蕊
高小红
庞慧
WANG Shuanglong;LV Bin;LI Long(Department of Ultrasound,the First People's Hospital of Jining,Shandong Province,Jining 272011)
出处
《陕西医学杂志》
CAS
2020年第5期599-602,共4页
Shaanxi Medical Journal
基金
山东省济宁市科技助推新旧动能转换计划项目(2017SMNS011)。
关键词
甲状腺结节
甲状腺微小乳头状癌
诊断
超声造影
时间强度曲线
定量参数
Thyroid nodule
Papillary thyroid microcarcinoma
Diagnosis
Contrast-enhanced ultrasound
Time-activity curve
Quantitative parameters