Chinese Medical Sciences Journal ›› 2019, Vol. 34 ›› Issue (1): 1-9.doi: 10.24920/003531

• 论著 •    下一篇



  1. 1 解放军总医院海南医院放射诊断科,海南,三亚 572013
    2 放军总医院放射诊断科,北京 100853
  • 收稿日期:2018-10-18 修回日期:2019-02-22 出版日期:2019-03-30 发布日期:2019-04-08
  • 通讯作者: 叶慧义;

Value of Texture Analysis of Intravoxel Incoherent Motion Parameters in Differential Diagnosis of Pancreatic Neuroendocrine Tumor and Pancreatic Adenocarcinoma

Wang Yingwei1,2,Zhang Xinghua2,Wang Botao1,Wang Ye2,Liu Mengqi1,2,Wang Haiyi1,Ye Huiyi2,*(),Chen Zhiye1,2,*()   

  1. 1 Department of Radiology, Hainan Hospital of Chinese PLA General Hospital, Sanya, Hainan 572013, China
    2 Department of Radiology, Chinese PLA General Hospital, Beijing 100853, China
  • Received:2018-10-18 Revised:2019-02-22 Published:2019-03-30 Online:2019-04-08
  • Contact: Ye Huiyi,Chen Zhiye;


目的 评价体素内不相干运动成像(intravoxel incoherent motion,IVIM)参数的纹理特征在胰腺神经内分泌肿瘤和胰腺癌鉴别诊断上的价值。

方法 18例胰腺神经内分泌肿瘤患者和32例胰腺癌患者纳入此项回归性研究。所有患者术前均接受了10个b值(0-800 s/mm2)的扩散加权成像(diffusion-weighted imaging,DWI)检查,所获得图像数据应用IVIM模型进行分析,获得灰度编码的灌注分数(perfusion fraction,f)、快池扩散(fast component of diffusion,Dfast)和慢池扩散(slow component of diffusion,Dslow)参数图。在各组参数图上测量肿瘤最大截面的参数均值和纹理特征(包括角二阶矩,逆差距、自相关、对比度和熵)。采用独立样本t检验及Mann-Whitney U检验比较两组肿瘤间IVIM参数均值和纹理特征的差异,同时进行二元Logistic回归分析法建立回归模型,并进行受试者工作特征曲线分析评价诊断效能。

结果 胰腺神经内分泌肿瘤组的参数f值显著高于胰腺癌组(27.0% 比19.0%, P = 0.001),而参数Dfast值和Dslow值在两组肿瘤间的差异不具有统计学意义。各IVIM参数的所有纹理特征在两组肿瘤间的差异具有统计学意义(P = 0.000~0.043)。二元Logisic回归分析提示参数Dfast的纹理特征角二阶矩和参数Dslow的纹理特征自相关可以作为鉴别两者肿瘤的独立变量。受试者工作特征曲线分析显示IVIM参数的多个纹理特征鉴别胰腺神经内分泌肿瘤和胰腺癌的诊断效能要优于参数的均值(曲线下面积0.849~0.899比0.526~0.776)。进入Logistic回归模型的纹理特征组合(参数Dfast的角二阶矩和参数Dslow的自相关)鉴别胰腺神经内分泌中和胰腺癌的诊断效能最高(曲线下面积0.934,切值0.378,敏感性0.889,特异性0.854)。

结论 IVIM参数的纹理特征分析可以作为一种有效的胰腺神经内分泌肿瘤与胰腺癌的鉴别诊断工具。

关键词: 神经内分泌肿瘤, 胰腺癌, 纹理分析, 体素内不相干运动, 鉴别诊断


Objective To evaluate the value of texture features derived from intravoxel incoherent motion (IVIM) parameters for differentiating pancreatic neuroendocrine tumor (pNET) from pancreatic adenocarcinoma (PAC).

Methods Eighteen patients with pNET and 32 patients with PAC were retrospectively enrolled in this study. All patients underwent diffusion-weighted imaging with 10 b values used (from 0 to 800 s/mm 2). Based on IVIM model, perfusion-related parameters including perfusion fraction (f), fast component of diffusion (Dfast) and true diffusion parameter slow component of diffusion (Dslow) were calculated on a voxel-by-voxel basis and reorganized into gray-encoded parametric maps. The mean value of each IVIM parameter and texture features [Angular Second Moment (ASM), Inverse Difference Moment (IDM), Correlation, Contrast and Entropy] values of IVIM parameters were measured. Independent sample t-test or Mann-Whitney U test were performed for the between-group comparison of quantitative data. Regression model was established by using binary logistic regression analysis, and receiver operating characteristic (ROC) curve was plotted to evaluate the diagnostic efficiency.

Results The mean f value of the pNET group were significantly higher than that of the PAC group (27.0% vs. 19.0%, P = 0.001), while the mean values of Dfast and Dslow showed no significant differences between the two groups. All texture features (ASM, IDM, Correlation, Contrast and Entropy) of each IVIM parameter showed significant differences between the pNET and PAC groups (P=0.000-0.043). Binary logistic regression analysis showed that texture ASM of Dfast and texture Correlation of Dslow were considered as the specific imaging variables for the differential diagnosis of pNET and PAC. ROC analysis revealed that multiple texture features presented better diagnostic performance than IVIM parameters (AUC 0.849-0.899 vs. 0.526-0.776), and texture ASM of Dfast combined with Correlation of Dslow in the model of logistic regression had largest area under ROC curve for distinguishing pNET from PAC (AUC 0.934, cutoff 0.378, sensitivity 0.889, specificity 0.854).

Conclusion Texture analysis of IVIM parameters could be an effective and noninvasive tool to differentiate pNET from PAC.

Key words: neuroendocrine tumor, pancreatic adenocarcinoma, texture analysis, intravoxel incoherent motion, differential diagnosis

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