title: Classification of tumor area using combined DCE and DSC MRI in patients with glioblastoma.
abstract: This study proposes an automatic method for identification and quantification of different tissue components: the non-enhanced infiltrative tumor, vasogenic edema and enhanced tumor areas, at the subject level, in patients with glioblastoma (GB) based on dynamic contrast enhancement (DCE) and dynamic susceptibility contrast (DSC) MRI. Nineteen MR data sets, obtained from 12 patients with GB, were included. Seven patients were scanned before and 8 weeks following bevacizumab initiation. Segmentation of the tumor area was performed based on the temporal data of DCE and DSC at the group-level using k-means algorithm, and further at the subject-level using support vector machines algorithm. The obtained components were associated to different tissues types based on their temporal characteristics, calculated perfusion and permeability values and MR-spectroscopy. The method enabled the segmentation of the tumor area into the enhancing permeable component; the non-enhancing hypoperfused component, associated with vasogenic edema; and the non-enhancing hyperperfused component, associated with infiltrative tumor. Good agreement was obtained between the group-level, unsupervised and subject-level, supervised classification results, with significant correlation (r = 0.93, p < 0.001) and average symmetric root-mean-square surface distance of 2.5 ± 5.1 mm. Longitudinal changes in the volumes of the three components were assessed alongside therapy. Tumor area segmentation using DCE and DSC can be used to differentiate between vasogenic edema and infiltrative tumors in patients with GB, which is of major clinical importance in therapy response assessment.
authors.affiliation: Functional Brain Center, The Wohl Institute for Advanced Imaging, Tel Aviv Sourasky Medical Center, 6 Weizman St, 64239, Tel Aviv, Israel.
authors.forename: Moran
authors.initials: M
authors.lastname: Artzi
authors.forename: Deborah T
authors.initials: DT
authors.lastname: Blumenthal
authors.forename: Felix
authors.initials: F
authors.lastname: Bokstein
authors.forename: Guy
authors.initials: G
authors.lastname: Nadav
authors.forename: Gilad
authors.initials: G
authors.lastname: Liberman
authors.forename: Orna
authors.initials: O
authors.lastname: Aizenstein
authors.forename: Dafna
authors.initials: D
authors.lastname: Ben Bashat
mesh_terms: D000465:Algorithms; D020533:Angiogenesis Inhibitors / Q000627:therapeutic use; D061067:Antibodies, Monoclonal, Humanized / Q000627:therapeutic use; D000068258:Bevacizumab; D001921:Brain / Q000187:drug effects / Q000473:pathology*; D001929:Brain Edema / Q000188:drug therapy / Q000473:pathology / Q000503:physiopathology; D001932:Brain Neoplasms / Q000188:drug therapy / Q000473:pathology* / Q000503:physiopathology; D005260:Female; D005909:Glioblastoma / Q000188:drug therapy / Q000473:pathology* / Q000503:physiopathology; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D008137:Longitudinal Studies; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D008875:Middle Aged; D060388:Support Vector Machine; D047368:Tumor Burden
chemical_list: D020533:Angiogenesis Inhibitors; D061067:Antibodies, Monoclonal, Humanized; D000068258:Bevacizumab