Differentiation of glioblastomas and solitary metastatic brain tumors using texture analysis of conventional MRI / 中华放射学杂志

ABSTRACT

Objective To investigate the diagnostic value of the texture analysis derived from conventional MR imaging in differentiating glioblastomas from solitary brain metastases. Methods Thirty-four patients with pathological diagnoses of glioblastomas and 34 patients with pathological diagnoses of solitary brain metastases were enrolled in our study. All patients underwent conventional MR imaging including axial T1WI, T2WI, fluid attenuated inversion recovery (FLAIR) 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. These methods were used to identify the most significant texture features in discriminating glioblastomas from metastases. Then 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 glioblastomas from metastases. The results were shown by misclassification rate. Meanwhile, two senior radiologists (who had 5 and 9 years of experience in neuroimaging diagnosis, respectively) analysed the data of the 68 patients. Chi-square test was used to compare the differences in the results between the radiologists' analysis and the texture analysis. Results In the four kinds of sequences, the texture features for differentiating glioblastomas from solitary brain metastases were mainly from T2WI which had the lowest misclassification rate, 8.82% (6/68). The misclassification rates of the feature selection methods were similar in MI, Fisher's coefficient and POE + ACC (10.29%-27.94% for MI;11.76%-44.12% for Fisher's coefficientand 8.82%-38.24% for POE+ACC). However, the misclassification rate of the combination of the three methods (8.82%-33.83% for FPM) was lower than that of any other kind of method. In the statistical methods, NDA (8.82%-11.76% ) had lower misclassification rate than RDA (26.47%-39.71% ), PCA (27.94%-39.71%) and LDA (13.24%-44.12%). Misclassification rate of the radiologists' analysis 14.71%(10/68) was higher than that of the texture analysis, but there was no statistically difference between them (χ2= 10.993, P=0.287). Conclusion Texture analysis of conventional MR imaging can provide reliably objective basis for differentiating glioblastoma from solitary brain metastasis.