磁共振扩散加权成像纹理特征分析在乳腺良恶性肿瘤鉴别中的价值

doi:10.24920/003516

Abstract

Objective To investigate the difference in texture features on diffusion weighted imaging (DWI) images between breast benign and malignant tumors.

Methods Patients including 56 with mass-like breast cancer, 16 with breast fibroadenoma, and 4 with intraductal papilloma of breast treated in the Hainan Hospital of Chinese PLA General Hospital were retrospectively enrolled in this study, and allocated to the benign group (20 patients) and the malignant group (56 patients) according to the post-surgically pathological results. Texture analysis was performed on axial DWI images, and five characteristic parameters including Angular Second Moment (ASM), Contrast, Correlation, Inverse Difference Moment (IDM), and Entropy were calculated. Independent sample t-test and Mann-Whitney U test were performed for intergroup comparison. Regression model was established by using Binary Logistic regression analysis, and receiver operating characteristic curve (ROC) analysis was carried out to evaluate the diagnostic efficiency.

Results The texture features ASM, Contrast, Correlation and Entropy showed significant differences between the benign and malignant breast tumor groups (P_ASM=0.014, P_contrast=0.019, P_correlation=0.010, P_entropy=0.007). The area under the ROC curve was 0.685, 0.681, 0.754, and 0.683 respectively for the positive texture variables mentioned above, and that for the combined variables (ASM, Contrast, and Entropy) was 0.802 in the model of Logistic regression. Binary Logistic regression analysis demonstrated that ASM, Contrast and Entropy were considered as the specific imaging variables for the differential diagnosis of breast benign and malignant tumors.

Conclusion The texture analysis of DWI may be a simple and effective tool in the differential diagnosis between breast benign and malignant tumors.

Key words: breast tumor, texture analysis, magnetic resonance imaging, differential diagnosis

Wang Botao, Fan Wenping, Xu Huan, Li Lihui, Zhang Xiaohuan, Wang Kun, Liu Mengqi, You Junhao, Chen Zhiye. Value of Magnetic Resonance Imaging Texture Analysis in the Differential Diagnosis of Benign and Malignant Breast Tumors[J].Chinese Medical Sciences Journal, 2019, 34(1): 33-37.

Figures/Tables 4

Table 1

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Table 3

Figure 1.

References 14.

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Group	n	ASM [median (QR)]^a	Contrast^§	Correlation [median (QR)]^a	IDM [median (QR)]^a	Entropy^§
Malignant group	56	0.006 (0.010)	1070.423±511.094	0.0003 (0.0000)	0.034 (0.020)	5.421±0.912
Benign group	20	0.008 (0.020)	782.637±424.783	0.0004 (0.0000)	0.046 (0.050)	4.715±1.028
t/U value		352.500	2.405	275.0000	473.000	2.761
P value		0.014	0.019	0.001	0.305	0.007
95%CI		-0.002-0.017	54.132-577.751	0.0001-0.0004	-0.006-0.026	0.191-1.179

Parameter	AUC	Critical value	95%CI	Sensitivity	Specificity	Above critical value	Below critical value
ASM	0.685	0.0075	0.550-0.820	0.550	0.750	Benign	Malignant
Contrast	0.681	908.7935	0.543-0.820	0.700	0.625	Malignant	Benign
Correlation	0.754	0.0004	0.628-0.881	0.600	0.821	Benign	Malignant
Entropy	0.683	4.9345	0.547-0.818	0.550	0.732	Malignant	Benign

Independent variable	Regression coefficient	Standard error	Wald χ²	P value
ASM	58.453	31.984	3.340	0.068
Contrast	0.003	0.001	10.181	0.001
Entropy	1.980	0.664	8.882	0.003
Constant	-12.137	4.071	8.870	0.003

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