title: A new metric for detecting change in slowly evolving brain tumors: validation in meningioma patients.
abstract: BACKGROUND
  Change detection is a critical component in the diagnosis and monitoring of many slowly evolving pathologies.
  
  
  OBJECTIVE
  This article describes a semiautomatic monitoring approach using longitudinal medical images. We test the method on brain scans of patients with meningioma, which experts have found difficult to monitor because the tumor evolution is very slow and may be obscured by artifacts related to image acquisition.
  
  
  METHODS
  We describe a semiautomatic procedure targeted toward identifying difficult-to-detect changes in brain tumor imaging. The tool combines input from a medical expert with state-of-the-art technology. The software is easy to calibrate and, in less than 5 minutes, returns the total volume of tumor change in mm. We test the method on postgadolinium, T1-weighted magnetic resonance images of 10 patients with meningioma and compare our results with experts' findings. We also perform benchmark testing with synthetic data.
  
  
  RESULTS
  Our experiments indicated that experts' visual inspections are not sensitive enough to detect subtle growth. Measurements based on experts' manual segmentations were highly accurate but also labor intensive. The accuracy of our approach was comparable to the experts' results. However, our approach required far less user input and generated more consistent measurements.
  
  
  CONCLUSIONS
  The sensitivity of experts' visual inspection is often too low to detect subtle growth of meningiomas from longitudinal scans. Measurements based on experts' segmentation are highly accurate but generally too labor intensive for standard clinical settings. We described an alternative metric that provides accurate and robust measurements of subtle tumor changes while requiring a minimal amount of user input.
authors.affiliation: Section of Biomedical Image Analysis, Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA. pohl@csail.mit.edu
authors.forename: Kilian M
authors.initials: KM
authors.lastname: Pohl
authors.forename: Ender
authors.initials: E
authors.lastname: Konukoglu
authors.forename: Sebastian
authors.initials: S
authors.lastname: Novellas
authors.forename: Nicholas
authors.initials: N
authors.lastname: Ayache
authors.forename: Andriy
authors.initials: A
authors.lastname: Fedorov
authors.forename: Ion-Florin
authors.initials: IF
authors.lastname: Talos
authors.forename: Alexandra
authors.initials: A
authors.lastname: Golby
authors.forename: William M
authors.initials: WM
authors.lastname: Wells
authors.forename: Ron
authors.initials: R
authors.lastname: Kikinis
authors.forename: Peter M
authors.initials: PM
authors.lastname: Black
mesh_terms: D001932:Brain Neoplasms / Q000175:diagnosis*; D003936:Diagnosis, Computer-Assisted / Q000379:methods; D003952:Diagnostic Imaging / Q000379:methods; D018450:Disease Progression; D005260:Female; D006801:Humans; D008137:Longitudinal Studies; D008297:Male; D008577:Meningeal Neoplasms / Q000175:diagnosis*; D008579:Meningioma / Q000175:diagnosis*; D015203:Reproducibility of Results; D012984:Software; D013997:Time Factors