Differentiation of benign and malignant breast lesions using texture analysis of conventional MRI:a preliminary study
10.3760/cma.j.issn.1005-1201.2017.08.006
- VernacularTitle:常规MRI纹理分析鉴别乳腺良、恶性病变的价值初探
- Author:
Zhuwei ZHANG
;
Ting HUA
;
Tingting XU
;
Jiping YAO
;
Jian GONG
;
Qing GUAN
;
Jianping RUAN
;
Guangyu TANG
- Keywords:
Breast diseases;
Magnetic resonance imaging;
Texture analysis
- From:
Chinese Journal of Radiology
2017;51(8):588-591
- CountryChina
- Language:Chinese
-
Abstract:
Objective To investigate the diagnostic value of texture analysis derived from conventional MR imaging in differentiating benign and malignant breast lesions. Methods Thirty-six patients with malignant breast lesion and 33 patients with benign breast lesion were retrospectively analyzed in our study. All patients underwent conventional MR imaging including axial T1WI, T2WI, and contrast-enhanced T1WI before surgery. Texture features were calculated from manually drawn ROIs by using MaZda software. The feature selection methods included mutual information (MI), Fishers coefficient, classification error probability combined with average correlation coefficients (POE + ACC) and the combination of the above three methods(FPM). These methods were used to identify the most significant texture features in discriminating benign breast lesion from malignant breast lesion. The statistical methods including raw data analysis (RDA), principal component analysis (PCA), linear discriminant analysis (LDA) and nonlinear discriminant analysis (NDA) were used to distinguish malignant breast lesion from benign breast lesion. The results were shown by misclassification rate. Results In the three kinds of sequences, the texture features for differentiating malignant breast lesion and benign breast lesion were mainly from T2WI which had the lowest misclassification rate 4.35%(3/69). The misclassification rates of the feature selection methods were similar in MI, Fisher coefficient and POE+ACC (15.94%to 56.52%for MI;17.39%to 56.52%for Fisher coefficient and 17.39%to 56.52%for POE+ACC). However, the misclassification rate of the combination of the three methods (4.35%to 53.62%for FPM) was lower than that of any other kind of method. In the statistical methods, NDA (4.35% to 27.54%) had lower misclassification rate than RDA (33.33% to 56.52%), PCA (33.33% to 53.62%) and LDA (15.94% to 44.93%). Conclusion Texture analysis of conventional MR imaging can provide reliably objective basis for differentiating benign from malignant breast lesions.
