title: Diagnostic Value of Fractal Analysis for the Differentiation of Brain Tumors Using 3-Tesla Magnetic Resonance Susceptibility-Weighted Imaging.
abstract: Susceptibility-weighted imaging (SWI) of brain tumors provides information about neoplastic vasculature and intratumoral micro- and macrobleedings. Low- and high-grade gliomas can be distinguished by SWI due to their different vascular characteristics. Fractal analysis allows for quantification of these radiological differences by a computer-based morphological assessment of SWI patterns.
  
  
  
  To show the feasibility of SWI analysis on 3-T magnetic resonance imaging to distinguish different kinds of brain tumors.
  
  
  
  Seventy-eight patients affected by brain tumors of different histopathology (low- and high-grade gliomas, metastases, meningiomas, lymphomas) were included. All patients underwent preoperative 3-T magnetic resonance imaging including SWI, on which the lesions were contoured. The images underwent automated computation, extracting 2 quantitative parameters: the volume fraction of SWI signals within the tumors (signal ratio) and the morphological self-similar features (fractal dimension [FD]). The results were then correlated with each histopathological type of tumor.
  
  
  
  Signal ratio and FD were able to differentiate low-grade gliomas from grade III and IV gliomas, metastases, and meningiomas (P < .05). FD was statistically different between lymphomas and high-grade gliomas (P < .05). A receiver-operating characteristic analysis showed that the optimal cutoff value for differentiating low- from high-grade gliomas was 1.75 for FD (sensitivity, 81%; specificity, 89%) and 0.03 for signal ratio (sensitivity, 80%; specificity, 86%).
  
  
  
  FD of SWI on 3-T magnetic resonance imaging is a novel image biomarker for glioma grading and brain tumor characterization. Computational models offer promising results that may improve diagnosis and open perspectives in the radiological assessment of brain tumors.
  
  
  
  FD, fractal dimensionSR, signal ratioSWI, susceptibility-weighted imaging.
authors.affiliation: ‡Australian School of Advanced Medicine, Department of Neurosurgery, Macquarie University Hospital, Sydney, New South Wales, Australia; §Garvan Institute of Medical Research, Sydney, New South Wales, Australia; ¶Department of Neurosurgery, University Hospital Pontchaillou, Rennes, France; ‖Department of Neuroradiology, University Hospital Pontchaillou, Rennes, France; #Division of Neurosurgery, St. Michael's Hospital, University of Toronto, Toronto, Ontario, Canada.
authors.forename: Antonio
authors.initials: A
authors.lastname: Di Ieva
authors.forename: Pierre-Jean
authors.initials: PJ
authors.lastname: Le Reste
authors.forename: Béatrice
authors.initials: B
authors.lastname: Carsin-Nicol
authors.forename: Jean-Christophe
authors.initials: JC
authors.lastname: Ferre
authors.forename: Michael D
authors.initials: MD
authors.lastname: Cusimano
mesh_terms: D000328:Adult; D000368:Aged; D001932:Brain Neoplasms / Q000000981:diagnostic imaging* / Q000473:pathology; D003937:Diagnosis, Differential; D005240:Feasibility Studies; D005260:Female; D017709:Fractals; D005910:Glioma / Q000000981:diagnostic imaging* / Q000473:pathology; D006801:Humans; D008223:Lymphoma / Q000000981:diagnostic imaging* / Q000473:pathology; D008279:Magnetic Resonance Imaging; D008297:Male; D008579:Meningioma / Q000000981:diagnostic imaging* / Q000473:pathology; D008875:Middle Aged; D060787:Neoplasm Grading; D012189:Retrospective Studies; D012680:Sensitivity and Specificity