title: Impact of Software Modeling on the Accuracy of Perfusion MRI in Glioma.
abstract: OBJECTIVE
  Relative cerebral blood volume, as measured by T2*-weighted dynamic susceptibility-weighted contrast-enhanced MRI, represents the most robust and widely used perfusion MR imaging metric in neuro-oncology. Our aim was to determine whether differences in modeling implementation will impact the correction of leakage effects (from blood-brain barrier disruption) and the accuracy of relative CBV calculations as measured on T2*-weighted dynamic susceptibility-weighted contrast-enhanced MR imaging at 3T field strength.
  
  
  METHODS
  This study included 52 patients with glioma undergoing DSC MR imaging. Thirty-six patients underwent both non-preload dose- and preload dose-corrected DSC acquisitions, with 16 patients undergoing preload dose-corrected acquisitions only. For each acquisition, we generated 2 sets of relative CBV metrics by using 2 separate, widely published, FDA-approved commercial software packages: IB Neuro and nordicICE. We calculated 4 relative CBV metrics within tumor volumes: mean relative CBV, mode relative CBV, percentage of voxels with relative CBV > 1.75, and percentage of voxels with relative CBV > 1.0 (fractional tumor burden). We determined Pearson (r) and Spearman (ρ) correlations between non-preload dose- and preload dose-corrected metrics. In a subset of patients with recurrent glioblastoma (n = 25), we determined receiver operating characteristic area under the curve for fractional tumor burden accuracy to predict the tissue diagnosis of tumor recurrence versus posttreatment effect. We also determined correlations between rCBV and microvessel area from stereotactic biopsies (n = 29) in 12 patients.
  
  
  RESULTS
  With IB Neuro, relative CBV metrics correlated highly between non-preload dose- and preload dose-corrected conditions for fractional tumor burden (r = 0.96, ρ = 0.94), percentage > 1.75 (r = 0.93, ρ = 0.91), mean (r = 0.87, ρ = 0.86), and mode (r = 0.78, ρ = 0.76). These correlations dropped substantially with nordicICE. With fractional tumor burden, IB Neuro was more accurate than nordicICE in diagnosing tumor versus posttreatment effect (area under the curve = 0.85 versus 0.67) (P < .01). The highest relative CBV-microvessel area correlations required preload dose and IB Neuro (r = 0.64, ρ = 0.58, P = .001).
  
  
  CONCLUSIONS
  Different implementations of perfusion MR imaging software modeling can impact the accuracy of leakage correction, relative CBV calculation, and correlations with histologic benchmarks.
authors.affiliation: From the Department of Radiology (L.S.H.) Keller Center for Imaging Innovation (L.S.H., C.E., J.P.K., L.C.B.) Hu.Leland@Mayo.Edu.
authors.forename: L S
authors.initials: LS
authors.lastname: Hu
authors.affiliation: the Department of Radiology (Z.K., P.K., T.J.K., B.J.E.), Mayo Clinic, Rochester, Minnesota.
authors.forename: Z
authors.initials: Z
authors.lastname: Kelm
authors.affiliation: the Department of Radiology (Z.K., P.K., T.J.K., B.J.E.), Mayo Clinic, Rochester, Minnesota.
authors.forename: P
authors.initials: P
authors.lastname: Korfiatis
authors.affiliation: Biostatistics (A.C.D.), Mayo Clinic, Phoenix/Scottsdale, Arizona.
authors.forename: A C
authors.initials: AC
authors.lastname: Dueck
authors.affiliation: Keller Center for Imaging Innovation (L.S.H., C.E., J.P.K., L.C.B.).
authors.forename: C
authors.initials: C
authors.lastname: Elrod
authors.affiliation: the Department of Radiological Sciences (B.M.E.), David Geffen School of Medicine, University of California, Los Angeles, California.
authors.forename: B M
authors.initials: BM
authors.lastname: Ellingson
authors.affiliation: the Department of Radiology (Z.K., P.K., T.J.K., B.J.E.), Mayo Clinic, Rochester, Minnesota.
authors.forename: T J
authors.initials: TJ
authors.lastname: Kaufmann
authors.affiliation: Departments of Neuropathology (J.M.E.).
authors.forename: J M
authors.initials: JM
authors.lastname: Eschbacher
authors.affiliation: Keller Center for Imaging Innovation (L.S.H., C.E., J.P.K., L.C.B.) Neuroradiology (J.P.K.).
authors.forename: J P
authors.identifier: http://orcid.org/0000-0003-4181-4185
authors.initials: JP
authors.lastname: Karis
authors.affiliation: Neurosurgery (K.S., P.N.), Barrow Neurological Institute, Phoenix, Arizona.
authors.forename: K
authors.initials: K
authors.lastname: Smith
authors.affiliation: Neurosurgery (K.S., P.N.), Barrow Neurological Institute, Phoenix, Arizona.
authors.forename: P
authors.initials: P
authors.lastname: Nakaji
authors.affiliation: the Department of Radiation Oncology (D.B., D.P.), Mayo Clinic, Rochester, Minnesota.
authors.forename: D
authors.initials: D
authors.lastname: Brinkman
authors.affiliation: the Department of Radiation Oncology (D.B., D.P.), Mayo Clinic, Rochester, Minnesota.
authors.forename: D
authors.initials: D
authors.lastname: Pafundi
authors.affiliation: Keller Center for Imaging Innovation (L.S.H., C.E., J.P.K., L.C.B.).
authors.forename: L C
authors.initials: LC
authors.lastname: Baxter
authors.affiliation: the Department of Radiology (Z.K., P.K., T.J.K., B.J.E.), Mayo Clinic, Rochester, Minnesota.
authors.forename: B J
authors.initials: BJ
authors.lastname: Erickson
mesh_terms: D000328:Adult; D000368:Aged; D001932:Brain Neoplasms / Q000098:blood supply* / Q000473:pathology; D002560:Cerebrovascular Circulation / Q000502:physiology; D005260:Female; D005910:Glioma / Q000098:blood supply* / Q000473:pathology; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D008875:Middle Aged; D008959:Models, Neurological; D010477:Perfusion; D012984:Software