title: Automated voxel-based analysis of volumetric dynamic contrast-enhanced CT data improves measurement of serial changes in tumor vascular biomarkers.
abstract: OBJECTIVE
  Development of perfusion imaging as a biomarker requires more robust methodologies for quantification of tumor physiology that allow assessment of volumetric tumor heterogeneity over time. This study proposes a parametric method for automatically analyzing perfused tissue from volumetric dynamic contrast-enhanced (DCE) computed tomography (CT) scans and assesses whether this 4-dimensional (4D) DCE approach is more robust and accurate than conventional, region-of-interest (ROI)-based CT methods in quantifying tumor perfusion with preliminary evaluation in metastatic brain cancer.
  
  
  METHODS
  Functional parameter reproducibility and analysis of sensitivity to imaging resolution and arterial input function were evaluated in image sets acquired from a 320-slice CT with a controlled flow phantom and patients with brain metastases, whose treatments were planned for stereotactic radiation surgery and who consented to a research ethics board-approved prospective imaging biomarker study. A voxel-based temporal dynamic analysis (TDA) methodology was used at baseline, at day 7, and at day 20 after treatment. The ability to detect changes in kinetic parameter maps in clinical data sets was investigated for both 4D TDA and conventional 2D ROI-based analysis methods.
  
  
  RESULTS
  A total of 7 brain metastases in 3 patients were evaluated over the 3 time points. The 4D TDA method showed improved spatial efficacy and accuracy of perfusion parameters compared to ROI-based DCE analysis (P<.005), with a reproducibility error of less than 2% when tested with DCE phantom data. Clinically, changes in transfer constant from the blood plasma into the extracellular extravascular space (Ktrans) were seen when using TDA, with substantially smaller errors than the 2D method on both day 7 post radiation surgery (±13%; P<.05) and by day 20 (±12%; P<.04). Standard methods showed a decrease in Ktrans but with large uncertainty (111.6 ± 150.5) %.
  
  
  CONCLUSIONS
  Parametric voxel-based analysis of 4D DCE CT data resulted in greater accuracy and reliability in measuring changes in perfusion CT-based kinetic metrics, which have the potential to be used as biomarkers in patients with metastatic brain cancer.
authors.affiliation: Radiation Medicine Program, Princess Margaret Cancer Center and University Health Network, Toronto, Ontario, Canada; Department of Radiation Oncology, University of Toronto, Toronto, Ontario, Canada; Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, Ontario, Canada. Electronic address: catherine.coolens@rmp.uhn.on.ca.
authors.forename: Catherine
authors.initials: C
authors.lastname: Coolens
authors.affiliation: Radiation Medicine Program, Princess Margaret Cancer Center and University Health Network, Toronto, Ontario, Canada.
authors.forename: Brandon
authors.initials: B
authors.lastname: Driscoll
authors.affiliation: Radiation Medicine Program, Princess Margaret Cancer Center and University Health Network, Toronto, Ontario, Canada; Department of Radiation Oncology, University of Toronto, Toronto, Ontario, Canada.
authors.forename: Caroline
authors.initials: C
authors.lastname: Chung
authors.affiliation: Radiation Medicine Program, Princess Margaret Cancer Center and University Health Network, Toronto, Ontario, Canada.
authors.forename: Tina
authors.initials: T
authors.lastname: Shek
authors.affiliation: Radiation Medicine Program, Princess Margaret Cancer Center and University Health Network, Toronto, Ontario, Canada.
authors.forename: Alborz
authors.initials: A
authors.lastname: Gorjizadeh
authors.affiliation: Radiation Medicine Program, Princess Margaret Cancer Center and University Health Network, Toronto, Ontario, Canada; Department of Radiation Oncology, University of Toronto, Toronto, Ontario, Canada.
authors.forename: Cynthia
authors.initials: C
authors.lastname: Ménard
authors.affiliation: Radiation Medicine Program, Princess Margaret Cancer Center and University Health Network, Toronto, Ontario, Canada; Department of Radiation Oncology, University of Toronto, Toronto, Ontario, Canada; Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, Ontario, Canada.
authors.forename: David
authors.initials: D
authors.lastname: Jaffray
mesh_terms: D000465:Algorithms; D001932:Brain Neoplasms / Q000098:blood supply* / Q000000981:diagnostic imaging / Q000556:secondary / Q000601:surgery; D003287:Contrast Media; D056973:Four-Dimensional Computed Tomography / Q000379:methods*; D006801:Humans; D002538:Intracranial Arteriovenous Malformations / Q000000981:diagnostic imaging; D055420:Perfusion Imaging / Q000379:methods*; D019047:Phantoms, Imaging; D016634:Radiosurgery; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014057:Tomography, X-Ray Computed / Q000379:methods
chemical_list: D003287:Contrast Media