RESUMO
Objective:To investigate the value of ADC derived from DWI combined with texture analysis derived from T 2WI fat suppressed images in distinguishing benign and malignant soft tissue tumors. Methods:The MRI and DWI images of 94 patients with soft tissue tumors (44 cases with malignant and 50 cases with benign) confirmed by pathology were analyzed retrospectively in the First Affiliated Hospital of USTC West District. ADC values of solid components were measured at GE ADW4.6 workstation. The texture features were extracted by manually drawing the ROI on the maximum level of the T 2WI fat suppressed images; the ADC values and texture parameters between the two groups were statistically analyzed by SPSS17.0, and the multivariate logistic regression model were conducted to analyze and calculate the diagnostic performance. Results:ADC value of benign and malignant soft tissue tumors was (1.6±0.3)×10 -3 mm 2/s, (1.2±0.5)×10 -3 mm 2/s, respectively, and the difference was statistically significant( t=-5.382, P<0.05). Taking 1.28×10 -3 mm 2/s as the critical value, the area under curve (AUC) for the diagnosis of benign and malignant soft tissue tumors was 0.783, the sensitivity was 92.00%, and the specificity was 65.91%. Among the texture features, the AUC of frequency size, skewness, Inertia All Direction_offset7, Inverse Difference Moment angle0_offset1, Inverse Difference Moment angle0_offset7 and Haralick Correlation All Direction_offset4_SD distinguishing benign and malignant soft tissue tumors were 0.825, 0.739, 0.826, 0.816, 0.820 and 0.783, respectively. The AUC, sensitivity and specificity of the best predictive model distinguishing benign and malignant soft tissue tumors were 0.930, 88.00% and 86.36% respectively using multivariate logistic regression analysis. Conclusion:ADC combined with texture analysis is of great value in preoperative differentiation of benign and malignant soft tissue tumors.