体素内不相干运动成像参数的纹理分析在胰腺神经内分泌肿瘤和胰腺癌鉴别诊断中的价值

doi:10.24920/003531

摘要/Abstract

摘要：

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，D_fast)和慢池扩散(slow component of diffusion，D_slow)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D_fast值和D_slow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D_slow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D_fast的角二阶矩和参数D_slow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

Abstract:

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 (D_fast) and true diffusion parameter slow component of diffusion (D_slow) 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 D_fast and D_slowshowed 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 D_fast and texture Correlation of D_slow 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 D_fast combined with Correlation of D_slow 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

Wang Yingwei, Zhang Xinghua, Wang Botao, Wang Ye, Liu Mengqi, Wang Haiyi, Ye Huiyi, Chen Zhiye. Value of Texture Analysis of Intravoxel Incoherent Motion Parameters in Differential Diagnosis of Pancreatic Neuroendocrine Tumor and Pancreatic Adenocarcinoma[J].Chinese Medical Sciences Journal, 2019, 34(1): 1-9.

图/表 5

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Parameters	PAC (n=32)	pNET (n=18)	U/t value	P value
f
ASM	0.398 (0.047)	0.468 (0.079)	-4.224	0.000
IDM	0.779±0.032	0.806±0.033	-2.810	0.007
Correlation (x10^-3)	0.065±0.007	0.076±0.009	-4.954	0.000
Contrast	4367.155 (1740.747)	3785.633 (1.70.172)	-2.021	0.043
Entropy	2.294±0.173	2.080±0.234	3.687	0.001
D_fast
ASM	0.350±0.049	0.450±0.623	-6.283	0.000
IDM	0.701 (0.069)	0.740 (0.045)	-3.456	0.001
Correlation (x10^-3)	0.083±0.007	0.102±0.016	-5.905	0.000
Contrast	2961.827±514.595	2469.961±618.127	3.016	0.004
Entropy	3.398 (0.587)	2.947 (0.534)	-4.143	0.000
D_slow
ASM	0.315±0.049	0.407±0.053	-6.169	0.000
IDM	0.589±0.048	0.659±0.044	-5.129	0.000
Correlation (x10^-3)	0.198±0.034	0.269±0.057	-5.562	0.000
Contrast	483.639±97.783	422.838±97.344	2.114	0.040
Entropy	4.570±0.441	3.833±0.421	5.768	0.000

Independent variables	Regression coefficient	Standard error	Wald χ²	P value
ASM of D_fast	35.251	13.929	6.405	0.011
Correlation of D_slow	33 247.973	14 163.943	5.510	0.019
Constant	-22.112	7.255	9.289	0.002

Parameters	AUC	95%CI	Cut-off value	Sensitivity	Specificity
f
Mean value	0.776	0.636-0.882	25.700^a	0.772	0.812
ASM	0.863	0.736-0.944	0.439^a	0.778	0.875
IDM	0.715	0.570-0.834	0.802^a	0.611	0.781
Correlation	0.849	0.719-0.934	0.069^a	0.778	0.812
Contrast	0.674	0.526-0.799	4675.279^b	1.000	0.406
Entropy	0.762	0.621-0.871	2.313^b	0.833	0.594
D_fast
Mean value	0.611	0.463-0.746	28.000^a	0.833	0.500
ASM	0.899	0.781-0.966	0.389^a	0.889	0.844
IDM	0.797	0.659-0.897	0.727^a	0.778	0.750
Correlation	0.887	0.766-0.959	0.094^a	0.833	0.937
Contrast	0.724	0.579-0.841	2224.521^b	0.444	0.937
Entropy	0.856	0.718-0.939	3.231^b	0.944	0.656
D_slow
Mean value	0.526	0.380-0.669	0.900	1.000	0.125
ASM	0.898	0.779-0.965	0.348^a	0.889	0.812
IDM	0.858	0.730-0.940	0.636^a	0.722	0.875
Correlation	0.856	0.728-0.939	0.245^a	0.778	0.937
Contrast	0.667	0.519-0.794	401.811^b	0.500	0.812
Entropy	0.880	0.757-0.955	4.218^b	0.889	0.750
Combined texture features	0.934	0.826-0.985	0.378^a	0.889	0.854

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