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超声弹性成像联合乳腺影像报告和数据系统(BI-RADS)分类诊断非肿块型乳腺癌
引用本文:邢博缘,刘小慧,赵云,平杰,张玲,刘捷,刘冬婷. 超声弹性成像联合乳腺影像报告和数据系统(BI-RADS)分类诊断非肿块型乳腺癌[J]. 中国医学影像技术, 2021, 37(8): 1154-1157
作者姓名:邢博缘  刘小慧  赵云  平杰  张玲  刘捷  刘冬婷
作者单位:三峡大学医学院, 湖北 宜昌 443000;三峡大学人民医院超声科, 湖北 宜昌 443000
摘    要:目的 探讨超声弹性成像(UE)技术联合乳腺影像报告和数据系统(BI-RADS)分类对非肿块型乳腺癌的诊断价值。方法 回顾性分析48例经二维超声及UE诊断、并经病理学证实的局灶性非肿块型乳腺病变患者(共53个病灶),以手术或穿刺活检病理结果作为金标准,评价超声弹性技术、BI-RADS分类及二者联合诊断非肿块型乳腺癌的价值。结果 53个病灶中,良性21个,恶性32个。UE诊断敏感度、特异度、准确率分别为68.75%、71.43%和69.81%;BI-RADS分类诊断敏感度、特异度和准确率分别为62.50%、66.67%和64.15%(P均>0.05);UE联合BI-RADS分类的诊断敏感度、特异度、准确率分别为81.25%、80.95%和81.13%,均高于单一UE或BI-RADS分类(P均<0.05)。结论 UE联合BI-RADS分类能提高诊断非肿块型乳腺癌的敏感度、特异度和准确率。

关 键 词:乳腺肿瘤  弹性成像技术  乳腺影像报告和数据系统
收稿时间:2020-02-05
修稿时间:2021-06-01

Ultrasonic elastography combined with breast imaging reporting and date system (BI-RADS) classification in diagnosis of nonpalpable breast cancer
XING Boyuan,LIU Xiaohui,ZHAO Yun,PING Jie,ZHANG Ling,LIU Jie,LIU Dongting. Ultrasonic elastography combined with breast imaging reporting and date system (BI-RADS) classification in diagnosis of nonpalpable breast cancer[J]. Chinese Journal of Medical Imaging Technology, 2021, 37(8): 1154-1157
Authors:XING Boyuan  LIU Xiaohui  ZHAO Yun  PING Jie  ZHANG Ling  LIU Jie  LIU Dongting
Affiliation:Medical College of China Three Gorges University, Yichang 443000, China;Department of Ultrasound, the People''s Hospital of China Three Gorges University, Yichang 443000, China
Abstract:Objective To explore the value of ultrasonic elastography (UE) combined with breast imaging reporting and date system (BI-RADS) classification in diagnosis of nonpalpable breast cancer. Methods Data of 48 patients with 53 focal non-lump breast lesions diagnosed with two-dimensional ultrasound and UE and proved pathologically were retrospectively analyzed. Taken surgical or biopsy pathologic results as the golden standards, the value of UE, BI-RADS classification and the combination of these 2 methods for diagnosing nonpalpable breast cancer were evaluated. Results Among 53 lesions, 21 were benign and 32 were malignant. The sensitivity, specificity and accuracy of UE was 68.75%, 71.43% and 69.81%, of BI-RADS classification was 62.50%, 66.67% and 64.15%, respectively (all P>0.05). The sensitivity, specificity and accuracy of UE combined with BI-RADS classification was 81.25%, 80.95% and 81.13%, all higher than those of UE or BI-RADS classification alone (all P<0.05). Conclusion UE combined with BI-RADS classification could improve the sensitivity, specificity and accuracy of diagnosis of non-lump breast cancer.
Keywords:breast neoplasms  elasticity imaging techniques  breast imaging reporting and data system
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