title: Differentiation of Glioblastoma and Lymphoma Using Feature Extraction and Support Vector Machine.
abstract: Differentiation of glioblastoma multiformes (GBMs) and lymphomas using multi-sequence magnetic resonance imaging (MRI) is an important task that is valuable for treatment planning. However, this task is a challenge because GBMs and lymphomas may have a similar appearance in MRI images. This similarity may lead to misclassification and could affect the treatment results. In this paper, we propose a semi-automatic method based on multi-sequence MRI to differentiate these two types of brain tumors. Our method consists of three steps: 1) the key slice is selected from 3D MRIs and region of interests (ROIs) are drawn around the tumor region; 2) different features are extracted based on prior clinical knowledge and validated using a t-test; and 3) features that are helpful for classification are used to build an original feature vector and a support vector machine is applied to perform classification. In total, 58 GBM cases and 37 lymphoma cases are used to validate our method. A leave-one-out crossvalidation strategy is adopted in our experiments. The global accuracy of our method was determined as 96.84%, which indicates that our method is effective for the differentiation of GBM and lymphoma and can be applied in clinical diagnosis.
authors.affiliation: School of Technology, Nanjing Audit University, Nanjing, Jiangsu, 211815, China.
authors.forename: Zhangjing
authors.initials: Z
authors.lastname: Yang
authors.forename: Piaopiao
authors.initials: P
authors.lastname: Feng
authors.forename: Tian
authors.initials: T
authors.lastname: Wen
authors.forename: Minghua
authors.initials: M
authors.lastname: Wan
authors.affiliation: Department of Radiology, the First Affiliated Hospital of Nanjing Medical University, 300 Guangzhou Road, Nanjing, Jiangsu, 210029, China.
authors.forename: Xunning
authors.initials: X
authors.lastname: Hong
mesh_terms: D001921:Brain / Q000000981:diagnostic imaging / Q000473:pathology; D001932:Brain Neoplasms / Q000145:classification / Q000000981:diagnostic imaging* / Q000473:pathology; D003937:Diagnosis, Differential; D005909:Glioblastoma / Q000145:classification / Q000000981:diagnostic imaging* / Q000473:pathology; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods; D008223:Lymphoma / Q000145:classification / Q000000981:diagnostic imaging* / Q000473:pathology; D008279:Magnetic Resonance Imaging / Q000379:methods; D017711:Nonlinear Dynamics; D012189:Retrospective Studies; D012680:Sensitivity and Specificity; D060388:Support Vector Machine
keywords: Feature extraction; glioblastoma; lymphoma; support vector machine