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S-DetectTM分类技术诊断BI-RADS 4类乳腺肿块
引用本文:邢博缘,赵云,平杰,刘捷,谢茜,李金林,张世忠.S-DetectTM分类技术诊断BI-RADS 4类乳腺肿块[J].中国医学影像技术,2020,36(9):1319-1323.
作者姓名:邢博缘  赵云  平杰  刘捷  谢茜  李金林  张世忠
作者单位:三峡大学人民医院 宜昌市第一人民医院, 湖北 宜昌 443000;三峡大学医学院, 湖北 宜昌 443002
摘    要:目的 观察S-DetectTM分类技术鉴别诊断BI-RADS 4类乳腺良恶性肿块的价值。方法 对94例经二维超声诊断为BI-RADS 4类乳腺肿块患者(共104个肿块)行S-DetectTM分类技术检查,以手术或穿刺活检病理结果作为金标准,评价S-DetectTM分类技术、BI-RADS分类及二者联合应用诊断乳腺BI-RADS 4类良恶性肿块的价值。结果 104个乳腺肿块,经病理确诊为良性41个、恶性63个。S-DetectTM分类技术诊断乳腺BI-RADS 4a类乳腺肿块的敏感度(SE)66.67%,特异度(SP)89.29%、阳性预测值(PPV)57.14%、阴性预测值(NPV)92.59%;对乳腺BI-RADS 4b类肿块分别为90.91%、60.00%、88.24%及66.67%;对乳腺BI-RADS 4c类肿块分别为95.83%、66.67%、95.83%及66.67%。S-DetectTM分类技术联合BI-RADS分类诊断乳腺肿块的SE、SP、准确率明显均高于单独运用(P均<0.05)。结论 S-DetectTM分类技术判断乳腺BI-RADS 4a类良性肿块、BI-RADS 4b类及BI-RADS 4c类恶性肿块均有较高价值。S-DetectTM分类技术联合BI-RADS分类可明显提高鉴别BI-RADS 4类乳腺良恶性肿块的效能。

关 键 词:乳腺肿瘤  超声检查  乳腺影像报告和数据系统  S-DetectTM分类
收稿时间:2019/8/18 0:00:00
修稿时间:2020/12/2 0:00:00

S-DetectTM classification technique in diagnosis of BI-RADS 4 breast masses
XING Boyuan,ZHAO Yun,PING Jie,LIU Jie,XIE Qian,LI Jinlin,ZHANG Shizhong.S-DetectTM classification technique in diagnosis of BI-RADS 4 breast masses[J].Chinese Journal of Medical Imaging Technology,2020,36(9):1319-1323.
Authors:XING Boyuan  ZHAO Yun  PING Jie  LIU Jie  XIE Qian  LI Jinlin  ZHANG Shizhong
Institution:People''s Hospital of China Three Gorges University, the First People''s Hospital of Yichang, Yichang 443000, China;Medical College of China Three Gorges University, Yichang 443002, China
Abstract:Objective To explore the diagnostic value of S-DetectTM classification technique for benign and malignant breast imaging reporting and data system (BI-RADS) 4 breast masses. Methods Totally 94 patients with 104 two-dimensional ultrasound diagnosed BI-RADS 4 breast masses were examined using S-DetectTM classification technique. Taken pathological results as the gold standards, the diagnostic values of S-DetectTM classification technology, BI-RADS classification alone and the combination of them of benign and malignant breast BI-RADS 4 masses were observed. Results There were 41 benign and 63 malignant ones among all 104 BI-RADS 4 breast masses. The sensitivity (SE) of S-DetectTM classification technique for diagnosing breast BI-RADS 4a mass was 66.67%, specificity (SP) was 89.29%, positive predictive value (PPV) was 57.14%, negative predictive value (NPV) was 92.59%, of BI-RADS 4b masses was 90.91%, 60.00%, 88.24% and 66.67%, of breast BI-RADS 4c mass was 95.83%, 66.67%, 95.83% and 66.67%, respectively. SE, SP and accuracy of combination of S-DetectTM classification and BI-RADS classification for diagnosing breast masses were significantly higher than those of BI-RADS classification and S-DetectTM classification technique alone (all P<0.05). Conclusion S-DetectTM classification technique was valuable for judging BI-RADS 4a benign masses as well as BI-RADS 4b and BI-RADS 4c malignant masses. S-DetectTM classification technology combined with BI-RADS classification could significantly improve the diagnostic value of identifying benign and malignant BI-RADS 4 breast masses.
Keywords:breast neoplasms  ultrasonography  breast imaging reporting and data system  S-DetectTM classification
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