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乳腺病灶与腺体的VTQ比值校正乳腺肿块BI-RADS分类的诊断价值 被引量:5

Diagnostic value of breast lesions' VTQ/breast tissue's VTQ ratio in correction of BI-RADS
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摘要 目的探讨乳腺病灶与腺体的VTQ比值在乳腺肿块BI-RADS分类校正中的应用价值。方法对155例患者共160个病灶进行常规超声检查,对病灶进行BI-RADS分类,分别进行病灶VTQ值测量和腺体VTQ值测量。以病理结果为诊断金标准,构建受试者工作特征(ROC)曲线,评价乳腺病灶与腺体的VTQ比值的诊断价值,并对BI-RADS分类进行校正。结果乳腺病灶与腺体的VTQ比值的ROC曲线下面积(AUC)为0. 92,95%CI:0. 87~0. 97,诊断界值为2. 68。BI-RADS校正前、后的敏感度为100%、97. 5%,特异度为51. 9%、77. 2%,准确性为76. 3%、87. 5%,阳性预测值为68. 1%、81. 4%,阴性预测值为100%、96. 8%。结论乳腺病灶与腺体的VTQ比值可以明显提高BI-RADS的诊断效能,减少不必要的穿刺活检。 Objective To assess the applied value of breast lesions’VTQ/breast tissue’s VTQ ratio in correcting breast imaging reporting and data system(BI-RADS).Methods A total of 155 patients with 160 breast lesions were examined by B-mode ultrasound and categorized with BI-RADS.The measurement of breast lesions’VTQ and breast tissue’s VTQ were performed for each lesion,respectively.With the pathological results as the gold standard,the receiver operating characteristic cure(ROC)was drawn to evaluate the diagnostic value of breast lesions’VTQ/breast tissue’s VTQ ratio.Results The AUC of breast lesions’VTQ/breast tissue’s VTQ ratio was 0.92,95%CI:0.87~0.97,the cut-off value was 2.68.Before correction,the sensitivity,specificity,accuracy,positive predictive value and negative predictive value of BI-RADS were 100%,51.9%,76.3%,68.1%,100%.After correction,the sensitivity,specificity accuracy,positive predictive value and negative predictive value of BI-RADS were 97.5%,77.2%,87.5%,81.4%,96.8%.Conclusion Breast lesions’VTQ/breast tissue’s VTQ ratio can significantly improve diagnostic performance of BI-RADS,which can reduce the unnecessary biopsies.
作者 裴蓓 隋秀芳 张杰 张行 叶显俊 章生龙 王磊 Pei Bei;Sui Xiufang;Zhang Jie;ZHANG Xing;YE Xianjun;ZHANG Shenglong;WANG Lei(Dept of Ultrasonography,The Affiliated Provincial Hospital of Anhui Medical University,Hefei 230001)
出处 《安徽医科大学学报》 CAS 北大核心 2018年第12期1924-1927,共4页 Acta Universitatis Medicinalis Anhui
基金 安徽省科技厅基金资助项目(编号:1401045018)
关键词 乳腺肿块 弹性成像 声触诊组织组织量化技术 BI-RADS breast lesions elastography virtual touch tissue quantification BI-RADS
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