Computeraided Grading of Gliomas Based on Local and Global MRI Features

Abstract

Background and objectives\r\nA computer-aided diagnosis (CAD) system based on quantitative magnetic resonance imaging (MRI) features was developed to evaluate the malignancy of diffuse gliomas, which are central nervous system tumors.\r\n\r\nMethods\r\nThe acquired image database for the CAD performance evaluation was composed of 34 glioblastomas and 73 diffuse lower-grade gliomas. In each case, tissues enclosed in a delineated tumor area were analyzed according to their gray-scale intensities on MRI scans. Four histogram moment features describing the global gray-scale distributions of gliomas tissues and 14 textural features were used to interpret local correlations between adjacent pixel values. With a logistic regression model, the individual feature set and a combination of both feature sets were used to establish the malignancy prediction model.\r\n\r\nResults\r\nPerformances of the CAD system using global, local, and the combination of both image feature sets achieved accuracies of 76%, 83%, and 88%, respectively. Compared to global features, the combined features had significantly better accuracy (p = 0.0213). With respect to the pathology results, the CAD classification obtained substantial agreement κ = 0.698, p < 0.001.\r\n\r\nConclusions\r\nNumerous proposed image features were significant in distinguishing glioblastomas from lower-grade gliomas. Combining them further into a malignancy prediction model would be promising in providing diagnostic suggestions for clinical use.