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声脉冲辐射力弹性成像对乳腺实性病灶良恶性的诊断价值 被引量:2

Diagnostic Value of Acoustic Radiation Force Impulse Elastography in Benign or Malignant Solid Breast Masses
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摘要 目的评估声脉冲辐射力(acoustic radiation force impulse,ARFI)弹性成像技术对乳腺实性病变良恶性鉴别诊断价值。方法对62例患者的62个乳腺实性病灶行ARFI声触诊组织成像(virtual touch tissue imaging,VTI)及声触诊组织定量(virtual touch tissue quantitfication,VTQ)两种技术检查。以VTI弹性分级≥IV级判断为恶性病灶,VTQ中计算病灶剪切波速度(shear wave velocity,SWV)平均值。结果根据受试者工作特征(receiver operating characteristic,ROC)曲线得出乳腺良恶性病灶SWV平均值的最佳诊断截断值为4.12 m/s,曲线下面积(area under curve,AUC)分别为0.793,其灵敏度、特异度、准确率分别为76.92%、88.89%、83.87%;以VTI弹性分级≥IV级判断乳腺病灶为恶性,其灵敏度、特异度、准确率分别为76.92%、86.11%、82.26%,联合VTI及VTQ诊断的灵敏度、特异度及准确率分别92.30%,94.44%,93.55%。结论 ARFI技术定性及定量评价乳腺病灶的良恶性,对良恶性鉴别有较高诊断价值,两者结合应用对乳腺实性病灶的鉴别有较大帮助。 Objective To evaluate the values of acoustic radiation force impulse (ARFI) technology in differentia- ting between benign and malignant solid breast masses. Methods Sixty-two patients with 62 solid breast masses were accepted virtual touch tissue imaging (VTI) and virtual touch tissue quantification (VTQ) examination of ARFI. The elastic classification of VTI ≥IV was considered as malignant lesions, and the average shear wave velocity (SWV) of lesion tissues was calculated. Results According to the receiver operating characteristic (ROC) curve, the best diagnostic cut-off values of SWV mean was 4. 12 m/s, area under curve (AUC) was 0. 793, sensitivity was 76. 92%, specificity was 88. 89%, and accuracy was 83.87%. The elasticity of VTI≥IV was considered to be malignant,the sensitivity, specificity and accuracy were 76. 92%, 86. 11%, 82. 26%, respectively. Conbined VTI with VTQ, the sensitivity, specificity and accuracy were 92. 30%, 94. 44%, 93. 55 %, respectively. Conclusion ARFI quantitative assessment has high diagnostic value on identification of benign and malignant breast lesions. It's helpful on identifying breast solid lesions by combining VTI with VTQ.
出处 《华南国防医学杂志》 CAS 2017年第4期244-246,共3页 Military Medical Journal of South China
关键词 乳腺 实性病灶 声触诊组织定量 声触诊组织成像 Breast Solid lesions Virtual touch tissue imaging Virtual touch tissue quantification
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