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中华消化病与影像杂志(电子版) ›› 2019, Vol. 09 ›› Issue (06) : 267 -271. doi: 10.3877/cma.j.issn.2095-2015.2019.06.007

所属专题: 文献

临床研究

扩散加权成像序列图像纹理分析鉴别诊断乳腺良恶性肿瘤的价值
孙中茹1, 田为中,1, 俞骥2   
  1. 1. 225300 江苏省,泰州市人民医院影像科
    2. 225300 江苏省,泰州市中医院影像科
  • 收稿日期:2019-10-07 出版日期:2019-12-01
  • 通信作者: 田为中

Value of image texture analysis of diffusion-weighted imaging sequence in the differential diagnosis of benign and malignant breast tumors

Zhongru Sun1, Weizhong Tian,1, Ji Yu2   

  1. 1. Department of Imaging, Taizhou People′s Hospital of Jiangsu Province, Taizhou 225300, China
    2. Department of Imaging, Taizhou Hospital of Traditional Chinese Medicine of Jiangsu Province, Taizhou 225300, China
  • Received:2019-10-07 Published:2019-12-01
  • Corresponding author: Weizhong Tian
  • About author:
    Corresponding author: Tian Weizhong, Email:
引用本文:

孙中茹, 田为中, 俞骥. 扩散加权成像序列图像纹理分析鉴别诊断乳腺良恶性肿瘤的价值[J/OL]. 中华消化病与影像杂志(电子版), 2019, 09(06): 267-271.

Zhongru Sun, Weizhong Tian, Ji Yu. Value of image texture analysis of diffusion-weighted imaging sequence in the differential diagnosis of benign and malignant breast tumors[J/OL]. Chinese Journal of Digestion and Medical Imageology(Electronic Edition), 2019, 09(06): 267-271.

目的

探讨扩散加权成像序列(DWI)图像纹理分析鉴别诊断乳腺良恶性肿瘤的价值。

方法

回顾性分析泰州市人民医院手术病理证实的28例乳腺良性肿瘤与28例恶性肿瘤的DWI图像的影像特征及纹理特征。采用MaZda软件提取所有患者DWI图像中肿瘤病灶的直方图与灰度游程矩阵参数,包括均值(mean)、方差(variance)、偏度(skewness)、峰度(kurtosis)和第1、10、50、90、99百分位数(Pere.1%、Pere.10%、Pere.50%、Pere.90%、Pere.99%)及短游程因子(SRE)、长游程因子(LRE)、灰度不均匀度(GLNU)、游程长不均匀度(RLNU)、游程中的图像分数(fraction)(包括水平、垂直、45dgr、135dgr 4个方向),采用独立样本t检验(正态分布数据)或非参数检验Mann-Whitney U检验(偏态分布数据)分析良恶性肿瘤病灶DWI图像纹理参数的差异,提取差异有统计学意义的纹理特征参数,使用ROC曲线分析有统计学意义的纹理参数鉴别良恶性肿瘤的诊断效能,运用多变量Logistic回归分析对差异有统计学意义的纹理参数进行建模并绘制ROC曲线评价模型效能。

结果

直方图参数中的方差(variance)与灰度游程矩阵参数中的游程长不均匀度(RLNU)(包括水平、垂直、45dgr、135dgr 4个方向)在两组间的差异有统计学意义(P<0.05),其中游程长不均匀度水平方向(HRLNU)以447.5517为阈值时诊断效能最佳,对应的AUC、灵敏度和特异度分别为0.874、85.71%、78.58%;通过对差异有统计学意义的纹理特征参数建立多参数Logistic回归诊断模型,对应的AUC、灵敏度及特异度为0.940、96.40%、82.10%。

结论

DWI图像纹理分析鉴别诊断乳腺良恶性肿瘤具有良好的应用价值。

Objective

To explore the value of texture analysis of diffusion-weighted imaging(DWI)sequence in differential diagnosis of benign and malignant breast tumors.

Methods

The image and texture features of DWI images of 28 cases of benign breast tumors and 28 cases of malignant breast tumors confirmed by surgery and pathology in Taizhou People′s Hospital of Jiangsu Province were retrospectively analyzed.MaZda software was used to extract histogram and gray-scale run-length matrix parameters of tumors in DWI images of all patients, including mean, variance, skewness, kurtosis, Pere.1%, Pere.10%, Pere.50%, Pere.90%, Pere.99% and short run-length emphasis(SRE), long run-distance factor(LRE), grey-level non-uniformity(GLNU), run-length non-uniformity(RLNU), image fraction(including horizontal, vertical, 45dgr and 135dgr directions)in run-length were tested by independent sample t test(normal distribution data)or non-parametric Mann-Whitney U test(skewed distribution data). The difference of texture parameters in DWI images of benign and malignant tumors was analyzed, and the texture characteristic parameters with statistical significance were extracted.The diagnostic efficiency of texture parameters was identified by using receiver operating characteristic(ROC)curve analysis.The texture parameters with statistical significance were modeled by using multivariate logistic regression analysis and ROC curve was drawn to evaluate the effectiveness of the model.

Results

The variance of histogram parameters and RLNU(including horizontal, vertical, 45dgr and 135dgr)of gray run-length matrix parameters had significant differences between the two groups(P<0.05). The diagnostic efficiency of horizontal run-length non-uniformity(HRLNU)was the best when the threshold value was 447.5517, and the corresponding area under the curve(AUC), sensitivity and specificity scores were 0.874, 85.71%, 78.58%.The diagnostic model of multi-parameter logistic regression was established through texture feature parameters with statistical significance, and the corresponding AUC, sensitivity and specificity were 0.940, 96.40% and 82.10%.

Conclusion

DWI image texture analysis has good application value in differential diagnosis of breast benign and malignant tumors.

表1 纹理特征参数的含义[7]
图1 MaZda软件勾画的肿瘤病灶感兴趣区ROI示意图
表2 良恶性肿瘤间差异有统计学意义的纹理特征参数比较(±s)
表3 良恶性肿瘤间差异有统计学意义的纹理特征参数的诊断效能
图2 ROC曲线显示差异有统计学意义纹理特征参数鉴别良性肿瘤与恶性肿瘤的效能。5个纹理特征参数(1个直方图参数Variance和4个灰度游程矩阵参数HRLNU、VRLNU、45dgr RLNU、135dgr RLNU)的ROC曲线
图3 基于组间差异有统计学意义的纹理特征参数(Variance、HRLNU、VRLNU、45dgr RLNU、135dgr RLNU)的logistic回归诊断模型的ROC曲线
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