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

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This study was designed to investigate the capacity of the texture features derived from intravoxel incoherent motion parameters to differentiate pancreatic neuroendocrine tumor from pancreatic adenocarcinoma. And the Figure shown here revealed texture feature Angular Second Moment (ASM) of fast component of diffusion (Dfast) combined with Correlation of true diffusion parameter slow component of diffusion (Dslow) presented the excellent differential diagnostic performance between pancreatic neuroendocrine tumor and pancreatic adenocarcinoma (AUC 0.934, cutoff 0.378, sensitivity 0.889, specificity 0.854).

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.

Figures/Tables 5

Figure 1.

Table 1

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Figure 2.

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

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