Value of image texture analysis of diffusion-weighted imaging sequence in the differential diagnosis of benign and malignant breast tumors

Abstract

Objective To explore the value of texture analysis of diffusion-weighted imaging(DWI)sequence in differential diagnosis of benign and malignant breast tumors. Methods The image and texture features of DWI images of 28 cases of benign breast tumors and 28 cases of malignant breast tumors confirmed by surgery and pathology in Taizhou People′s Hospital of Jiangsu Province were retrospectively analyzed.MaZda software was used to extract histogram and gray-scale run-length matrix parameters of tumors in DWI images of all patients, including mean, variance, skewness, kurtosis, Pere.1%, Pere.10%, Pere.50%, Pere.90%, Pere.99% and short run-length emphasis(SRE), long run-distance factor(LRE), grey-level non-uniformity(GLNU), run-length non-uniformity(RLNU), image fraction(including horizontal, vertical, 45dgr and 135dgr directions)in run-length were tested by independent sample t test(normal distribution data)or non-parametric Mann-Whitney U test(skewed distribution data). The difference of texture parameters in DWI images of benign and malignant tumors was analyzed, and the texture characteristic parameters with statistical significance were extracted.The diagnostic efficiency of texture parameters was identified by using receiver operating characteristic(ROC)curve analysis.The texture parameters with statistical significance were modeled by using multivariate logistic regression analysis and ROC curve was drawn to evaluate the effectiveness of the model. Results The variance of histogram parameters and RLNU(including horizontal, vertical, 45dgr and 135dgr)of gray run-length matrix parameters had significant differences between the two groups(P<0.05). The diagnostic efficiency of horizontal run-length non-uniformity(HRLNU)was the best when the threshold value was 447.5517, and the corresponding area under the curve(AUC), sensitivity and specificity scores were 0.874, 85.71%, 78.58%.The diagnostic model of multi-parameter logistic regression was established through texture feature parameters with statistical significance, and the corresponding AUC, sensitivity and specificity were 0.940, 96.40% and 82.10%. Conclusion DWI image texture analysis has good application value in differential diagnosis of breast benign and malignant tumors. Key words: Breast neoplasms; Diffusion-weighted imaging; Texture analysis; Histogram; Gray-scale run-length matrix