Chinese Medical Sciences Journal ›› 2019, Vol. 34 ›› Issue (1): 33-37.doi: 10.24920/003516

• Original Articles • Previous Articles     Next Articles

Value of Magnetic Resonance Imaging Texture Analysis in the Differential Diagnosis of Benign and Malignant Breast Tumors

Wang Botao1, Fan Wenping1, Xu Huan1, Li Lihui1, Zhang Xiaohuan1, Wang Kun1, Liu Mengqi1, 3, You Junhao2, *(), Chen Zhiye1, 3, *()   

  1. 1 Department of Radiology,Department of Oncology, Hainan Hospital of Chinese PLA General Hospital, Sanya, Hainan 572013, China
    2 Department of Oncology, Hainan Hospital of Chinese PLA General Hospital, Sanya, Hainan 572013, China
    3 Department of Radiology, Chinese PLA General Hospital, Beijing 100853, China
  • Received:2018-09-18 Revised:2019-02-22 Online:2019-03-30 Published:2019-04-08
  • Contact: You Junhao,Chen Zhiye E-mail:24103936@qq.com;yyqf@hotmail.com

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 (PASM=0.014, Pcontrast=0.019, Pcorrelation=0.010, Pentropy=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

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