苏蕾, 周牧野, 李阳, 郭婕, 侯迎迎, 马小开, 孙医学. 超声影像组学对乳腺BI-RADS 3类及以上结节良恶性鉴别诊断的应用价值[J]. 蚌埠医学院学报, 2023, 48(8): 1101-1104. DOI: 10.13898/j.cnki.issn.1000-2200.2023.08.019
    引用本文: 苏蕾, 周牧野, 李阳, 郭婕, 侯迎迎, 马小开, 孙医学. 超声影像组学对乳腺BI-RADS 3类及以上结节良恶性鉴别诊断的应用价值[J]. 蚌埠医学院学报, 2023, 48(8): 1101-1104. DOI: 10.13898/j.cnki.issn.1000-2200.2023.08.019
    SU Lei, ZHOU Mu-ye, LI Yang, GUO Jie, HOU Ying-ying, MA Xiao-kai, SUN Yi-xue. Application value of ultrasound radiomics in differential diagnosis of benign and malignant breast BI-RADS category 3 and above nodules[J]. Journal of Bengbu Medical College, 2023, 48(8): 1101-1104. DOI: 10.13898/j.cnki.issn.1000-2200.2023.08.019
    Citation: SU Lei, ZHOU Mu-ye, LI Yang, GUO Jie, HOU Ying-ying, MA Xiao-kai, SUN Yi-xue. Application value of ultrasound radiomics in differential diagnosis of benign and malignant breast BI-RADS category 3 and above nodules[J]. Journal of Bengbu Medical College, 2023, 48(8): 1101-1104. DOI: 10.13898/j.cnki.issn.1000-2200.2023.08.019

    超声影像组学对乳腺BI-RADS 3类及以上结节良恶性鉴别诊断的应用价值

    Application value of ultrasound radiomics in differential diagnosis of benign and malignant breast BI-RADS category 3 and above nodules

    • 摘要:
      目的探讨超声影像组学对乳腺BI-RADS 3类及以上结节良恶性的鉴别诊断。
      方法回顾性分析经超声诊断为BI-RADS 3类及以上结节164个, 并获得其病理结果, 将病灶划分为训练集(n=123)、测试集(n=41)。基于超声影像组学中的特征提取工程提取图像中感兴趣区的高维特征参数并利用特征降维筛选可靠特征建立模型进行分类研究, 训练集用于建立模型, 测试集用于验证模型准确性。
      结果超声组学所建立的模型鉴别乳腺结节良恶性的敏感性、特异性、准确性及ROC曲线下面积分别为87.51%、84.62%、85.00%、0.854(0.707~0.946)。
      结论超声影像组学在鉴别乳腺BI-RADS 3类及以上结节的良恶性上具有良好的诊断价值。

       

      Abstract:
      ObjectiveTo evaluate the differential diagnosis of benign and malignant breast BI-RADS category 3 and above nodules by ultrasound radiomics.
      MethodsA total of 164 breast nodules of BI-RADS category 3 and above diagnosed by ultrasound were retrospectively analyzed.The lesions were divided into training set (n=123) and test set (n=41).Base on feature extraction engineering of ultrasound radiomics, the high latitude feature parameters of the region of interest in the image were extracted, and the feature dimension reduction was used to screen the reliable features which established the model for classification research.The training set was used to establish the model, and the test set was used to verify the accuracy of the model.
      ResultsThe sensitivity, specificity, accuracy and area under ROC curve of the model established by ultrasound radiomics were 87.51%, 84.62%, 85.00% and 0.854(0.707-0.946), respectively.
      ConclusionsUltrasound radiomics has certain diagnostic value in differentiating benign and malignant breast BI-RADS category 3 and above nodules.

       

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