切换至 "中华医学电子期刊资源库"
高级检索
中华医学超声杂志（电子版）

中华医学超声杂志（电子版） ›› 2019, Vol. 16 ›› Issue (08) : 581 -585. doi: 10.3877/cma.j.issn.1672-6448.2019.08.006

所属专题： 乳腺超声； 文献；

浅表器官超声影像学

超声联合纹理分析对乳腺结节良恶性的诊断价值
种美玲1, 时白雪1, 张禧2, 钱林学1, 胡向东1,†()   
  1. 1. 100050　首都医科大学附属北京友谊医院超声科
    2. 100084　北京，清华大学生物医学工程系
  • 收稿日期:2019-07-01 出版日期:2019-08-01
  • 通信作者: 胡向东

Diagnostic value of ultrasound combined with texture analysis in differentiating benign and malignant breast nodules

Meiling Chong1, Baixue Shi1, Xi Zhang2, Linxue Qian1, Xiangdong Hu1,†()   

  1. 1. Department of Ultrasound, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China
    2. Department of Biomedical Engineering, Tsinghua University, Beijing 100084, China
  • Received:2019-07-01 Published:2019-08-01
  • Corresponding author: Xiangdong Hu
  • About author:
    Corresponding author: Hu Xiangdong, Email:
全文 ( ) 下载PDF ( ) 导出引用
引用本文：

种美玲, 时白雪, 张禧, 钱林学, 胡向东. 超声联合纹理分析对乳腺结节良恶性的诊断价值[J]. 中华医学超声杂志（电子版）, 2019, 16(08): 581-585.

Meiling Chong, Baixue Shi, Xi Zhang, Linxue Qian, Xiangdong Hu. Diagnostic value of ultrasound combined with texture analysis in differentiating benign and malignant breast nodules[J]. Chinese Journal of Medical Ultrasound (Electronic Edition), 2019, 16(08): 581-585.

目的

以乳腺结节的灰阶超声及剪切波弹性超声影像为基础，分析超声图像的纹理特征，探索常规超声联合纹理分析对乳腺结节良恶性的诊断价值。

方法

前瞻性收集2018年8月至2018年12月于首都医科大学附属北京友谊医院常规超声检查发现乳腺结节并获得病理诊断的患者113例，共113个结节。所有患者均行常规超声及剪切波弹性成像检查，并对113个乳腺结节依据乳腺影像报告和数据系统（BI-RADS）进行分类；对超声图像进行纹理分析，获得纹理特征参数并建立诊断模型。以病理结果为"金标准"，分析纹理特征诊断模型、常规超声联合纹理特征诊断模型对乳腺结节良恶性的诊断价值。

结果

以乳腺结节穿刺病理结果为"金标准"，纹理分析诊断乳腺结节良恶性的敏感度、特异度、阳性预测值、阴性预测值及准确性分别为0.64、0.91、0.75、0.86、0.83，ROC曲线下面积为0.77；常规超声与纹理分析联合方法诊断乳腺结节良恶性的敏感度、特异度、阳性预测值、阴性预测值及准确性分别为0.88、0.85、0.71、0.94、0.86，ROC曲线下面积为0.86。联合诊断的ROC曲线下面积高于纹理分析，差异有统计学意义（Z=2.133，P=0.03）。采用常规超声联合纹理分析方法，90.0%（72/80）的BI-RADS 4a类乳腺结节可以降级为BI-RADS 3类，病理结果显示，降级为BI-RADS 3类的乳腺结节中94.4%（68/72）为良性结节。

结论

常规超声联合纹理分析对乳腺结节良恶性有较好的诊断效能，可减少不必要的有创性检查，具有良好的应用前景。

关键词: 乳腺疾病, 超声检查, 弹性成像技术, 纹理分析
Objective

To explore the diagnostic value of routine ultrasonography combined with texture analysis in breast nodules based on their gray-scale ultrasound and shear-wave elastography images.

Methods

From August 2018 to December 2018, 113 patients with 113 breast nodules were prospectively included, and the breast nodules were found by routine ultrasonography and diagnosed by pathology. All patients underwent conventional ultrasound and shear wave elastography, and 113 breast nodules were classified according to Breast Imaging Reporting and Data System (BI-RADS). Texture feature parameters were obtained through texture analysis of ultrasound images and a diagnostic model was established. Using pathological results as the "golden standard", the diagnostic value of the texture feature diagnostic model and routine ultrasonography combined with the texture feature diagnostic model for benign and malignant breast nodules was analyzed.

Results

Using the pathological results of puncture specimens of breast nodules as the "golden standard", the sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of texture analysis in the diagnosis of benign and malignant breast nodules were 0.64, 0.91, 0.75, 0.86, and 0.83, respectively, and the area under the ROC curve (AUC) was 0.77. The sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of routine ultrasonography combined with texture analysis in the diagnosis of benign and malignant breast nodules were 0.88, 0.85, 0.71, 0.94, and 0.86, respectively, and the AUC was 0.86. The AUC of the combined diagnosis was significantly higher than that of texture analysis (Z=2.133, P=0.03). Based on the combined diagnosis, 90.0% (72/80) of BI-RADS 4a breast nodules could be degraded to BI-RADS 3. Pathological results showed that 94.4% (68/72) of the breast nodules degraded to BI-RADS 3 were benign.

Conclusion

Conventional ultrasound combined with texture analysis has better diagnostic efficacy for benign and malignant breast nodules. The combined diagnosis can reduce needless puncture and has good application prospects.

Key words: Breast diseases, Ultrasonography, Elasticity imaging technique, Texture analysis
图1 纹理分析、灰阶超声联合纹理分析诊断乳腺结节良恶性的ROC曲线。纹理分析的ROC曲线下面积为0.77；联合诊断的ROC曲线下面积为0.86
表1 纹理分析、联合方法对113个乳腺结节良恶性的诊断结果与病理学诊断结果比较（个）
病理学 纹理分析 合计 灰阶超声联合纹理分析 合计
良性 恶性 良性（BI-RADS 3类） 恶性（BI-RADS 4类及以上）
良性 73 7 80 68 12 80
恶性 12 21 33 4 29 33
合计 85 28 113 72 41 113
表2 纹理分析与联合方法对113个乳腺结节良恶性的诊断效能分析（%）
方法 结节数（个） 敏感度 特异度 阳性预测值 阴性预测值 准确性
纹理分析 113 0.64 0.91 0.75 0.86 0.83
灰阶超声联合纹理分析 113 0.88 0.85 0.71 0.94 0.86
图2 患者，女性，46岁，右侧乳房发现一低回声结节，乳腺结节的超声检查、剪切波弹性检查及病理检查图像。图a为常规超声显示乳腺结节形态欠规则（箭头所示），因此诊断为BI-RADS 4a类；图b为剪切波弹性图像，基于灰阶超声及剪切波弹性图像的纹理分析判断该结节为良性，因此将其降级为BI-RADS 3类；图c为穿刺病理结果提示该结节为导管内乳头状瘤（HE ×10）；BI-RADS为乳腺影像报告和数据系统
1
Chen W, Zheng R, Baade PD, et al. Cancer statistics in China, 2015 [J]. CA Cancer J Clin, 2016, 66(2): 115-132.
2
Torre LA, Bray F, SIiegel RL, et al. Global cancer statistics, 2012 [J]. CA Cancer J Clin, 2015, 65(2): 87-108.
3
Lee CH. Screening mammography: proven benefit, continued controversy [J]. Radiol Clin N Am, 2002, 40(3): 395-407.
4
Yoshida H, Casalino DD, Keserci B, et al. Wavelet-packet-based texture analysis for differentiation between benign and malignant liver tumours in ultrasound images [J]. Phys Med Biol, 2003, 48(22): 3735-3753.
5
Gómez-Flores W, Ruiz-Ortega BA. New Fully Automated Method for Segmentation of Breast Lesions on Ultrasound Based on Texture Analysis [J]. Ultrasound Med Biol, 2016, 42(7): 1637-1650.
6
D′Orsi CJ, Sickles EA, Mendelson EB, et al. ACR BI-RADS Atlas, breast imaging reporing and data system [M]. Reston, Va: American College of Radiology, 2013.
7
Haralick RM, Shanmuga K, Dinstein I. Textural features for image classification [J]. IEEE Trans Syst Man Cybern, 1973, 3(6): 610-621.
8
Valckx FM, Thijssen JM. Characterization of echographic image texture by cooccurrence matrix parame- ters [J]. Ultrasound Med Biol, 1997; 23(4): 559-571.
9
Friedman JH. Greedy function approximation: A gradient boosting machine [J]. Ann Stat, 2000, 29(5): 1189-1232.
10
Bhatia KS, Lee YY, Yuen EH, et al. Ultrasound elastography in the head and neck. Part II. Accuracy for malignancy [J]. Cancer Imaging, 2013, 13(2): 260-276.
11
Evans A, Whelehan P, Thomson K, et al. Quantitative shear wave ultrasound elastography: Initial experience in solid breast masses [J]. Breast Cancer Res, 2010, 12(6): R104.
12
Tomita F, Tsuji S. Computer Analysis of Visual Textures [M]. Boston: Springer, 1990: 13-36.
13
Huang C, He Q, Huang M, et al. Non-Invasive Identification of Vulnerable Atherosclerotic Plaques Using Texture Analysis in Ultrasound Carotid Elastography: An In Vivo Feasibility Study Validated by Magnetic Resonance Imaging [J]. Ultrasound Med Biol, 2017, 43(4): 817-830.
14
Tan M, Mariapun S, Yip CH, et al. A novel method of determining breast cancer risk using parenchymal textural analysis of mammography images on an Asian cohort [J]. Phys Med Biol, 2019, 64(3): 035016.
15
Bhatia KS, Lam AC, Pang SW, et al. Feasibility Study of Texture Analysis Using Ultrasound Shear Wave Elastography to Predict Malignancy in Thyroid Nodules [J]. Ultrasound Med Biol, 2016, 42(7): 1671-1680.
16
Soltaninejad M, Yang G, Lambrou T, et al. Supervised Learning based Multimodal MRI Brain Tumour Segmentation using Texture Features from Supervoxels [J]. Comput Meth Prog Bio, 2018, 157(4): 69-84.
[1] 魏淑婕, 惠品晶, 丁亚芳, 张白, 颜燕红, 周鹏, 黄亚波. 单侧颈内动脉闭塞患者行颞浅动脉-大脑中动脉搭桥术的脑血流动力学评估[J]. 中华医学超声杂志（电子版）, 2023, 20(10): 1046-1055.
[2] 张璇, 马宇童, 苗玉倩, 张云, 吴士文, 党晓楚, 陈颖颖, 钟兆明, 王雪娟, 胡淼, 孙岩峰, 马秀珠, 吕发勤, 寇海燕. 超声对Duchenne肌营养不良儿童膈肌功能的评价[J]. 中华医学超声杂志（电子版）, 2023, 20(10): 1068-1073.
[3] 朱连华, 费翔, 韩鹏, 姜波, 李楠, 罗渝昆. 高帧频超声造影在胆囊息肉样病变中的鉴别诊断价值[J]. 中华医学超声杂志（电子版）, 2023, 20(09): 904-910.
[4] 张梅芳, 谭莹, 朱巧珍, 温昕, 袁鹰, 秦越, 郭洪波, 侯伶秀, 黄文兰, 彭桂艳, 李胜利. 早孕期胎儿头臀长正中矢状切面超声图像的人工智能质控研究[J]. 中华医学超声杂志（电子版）, 2023, 20(09): 945-950.
[5] 陈舜, 薛恩生, 叶琴. PDCA在持续改进超声危急值管理制度中的价值[J]. 中华医学超声杂志（电子版）, 2023, 20(09): 974-978.
[6] 周钰菡, 肖欢, 唐毅, 杨春江, 周娟, 朱丽容, 徐娟, 牟芳婷. 超声对儿童髋关节暂时性滑膜炎的诊断价值[J]. 中华医学超声杂志（电子版）, 2023, 20(08): 795-800.
[7] 刘欢颜, 华扬, 贾凌云, 赵新宇, 刘蓓蓓. 颈内动脉闭塞病变管腔结构和血流动力学特征分析[J]. 中华医学超声杂志（电子版）, 2023, 20(08): 809-815.
[8] 郏亚平, 曾书娥. 含鳞状细胞癌成分的乳腺化生性癌的超声与病理特征分析[J]. 中华医学超声杂志（电子版）, 2023, 20(08): 844-848.
[9] 张丽丽, 陈莉, 余美琴, 聂小艳, 王婧玲, 刘婷. PDCA循环法在超声浅表器官亚专科建设中的应用[J]. 中华医学超声杂志（电子版）, 2023, 20(07): 717-721.
[10] 罗刚, 泮思林, 陈涛涛, 许茜, 纪志娴, 王思宝, 孙玲玉. 超声心动图在胎儿心脏介入治疗室间隔完整的肺动脉闭锁中的应用[J]. 中华医学超声杂志（电子版）, 2023, 20(06): 605-609.
[11] 黄佳, 石华, 张玉国, 胡佳琪, 陈茜. 胎儿左头臂静脉正常与异常超声图像特征及其临床意义[J]. 中华医学超声杂志（电子版）, 2023, 20(06): 610-617.
[12] 袁泽, 庄丽. 超声检测胎儿脐动脉和大脑中动脉血流对胎儿宫内窘迫的诊断价值[J]. 中华医学超声杂志（电子版）, 2023, 20(06): 618-621.
[13] 孔博, 张璟, 吕珂. 超声技术在复杂腹壁疝诊治中的作用[J]. 中华疝和腹壁外科杂志(电子版), 2023, 17(06): 670-673.
[14] 廖梅, 张红君, 金洁玚, 吕艳, 任杰. 床旁超声造影对肝移植术后早期肝动脉血栓的诊断价值[J]. 中华肝脏外科手术学电子杂志, 2023, 12(06): 630-634.
[15] 杨天池, 韩威, 邱枫, 祁佳慧. 术中胰腺超声弹性成像在胰腺质地评估中的应用[J]. 中华肝脏外科手术学电子杂志, 2023, 12(06): 646-650.
阅读次数
全文


摘要

版权所有 中华医学会 中华医学电子音像出版社有限责任公司  京ICP备14006079号-1  网络出版服务许可证：(署)网出证(京)字第075号