title: Improving Multiple Sclerosis Plaque Detection Using a Semiautomated Assistive Approach.
abstract: OBJECTIVE
  Treating MS with disease-modifying drugs relies on accurate MR imaging follow-up to determine the treatment effect. We aimed to develop and validate a semiautomated software platform to facilitate detection of new lesions and improved lesions.
  
  
  METHODS
  We developed VisTarsier to assist manual comparison of volumetric FLAIR sequences by using interstudy registration, resectioning, and color-map overlays that highlight new lesions and improved lesions. Using the software, 2 neuroradiologists retrospectively assessed MR imaging MS comparison study pairs acquired between 2009 and 2011 (161 comparison study pairs met the study inclusion criteria). Lesion detection and reading times were recorded. We tested inter- and intraobserver agreement and comparison with original clinical reports. Feedback was obtained from referring neurologists to assess the potential clinical impact.
  
  
  RESULTS
  More comparison study pairs with new lesions (reader 1, n = 60; reader 2, n = 62) and improved lesions (reader 1, n = 28; reader 2, n = 39) were recorded by using the software compared with original radiology reports (new lesions, n = 20; improved lesions, n = 5); the difference reached statistical significance (P < .001). Interobserver lesion number agreement was substantial (≥1 new lesion: κ = 0.87; 95% CI, 0.79-0.95; ≥1 improved lesion: κ = 0.72; 95% CI, 0.59-0.85), and overall interobserver lesion number correlation was good (Spearman ρ: new lesion = 0.910, improved lesion = 0.774). Intraobserver agreement was very good (new lesion: κ = 1.0, improved lesion: κ = 0.94; 95% CI, 0.82-1.00). Mean reporting times were <3 minutes. Neurologists indicated retrospective management alterations in 79% of comparative study pairs with newly detected lesion changes.
  
  
  CONCLUSIONS
  Using software that highlights changes between study pairs can improve lesion detection. Neurologist feedback indicated a likely impact on management.
authors.affiliation: From the Department of Radiology (J.v.H., P.M.D., F.G.), The Royal Melbourne Hospital and University of Melbourne, Parkville, Victoria, Australia jolandivh@hotmail.com.
authors.forename: J
authors.initials: J
authors.lastname: van Heerden
authors.affiliation: Department of Electrical and Electronic Engineering (D.R., A.M.Z.), School of Engineering, University of Melbourne, Parkville, Victoria, Australia.
authors.forename: D
authors.initials: D
authors.lastname: Rawlinson
authors.affiliation: Department of Electrical and Electronic Engineering (D.R., A.M.Z.), School of Engineering, University of Melbourne, Parkville, Victoria, Australia.
authors.forename: A M
authors.initials: AM
authors.lastname: Zhang
authors.affiliation: IBM Research (R.C.), Melbourne, Victoria, Australia.
authors.forename: R
authors.identifier: http://orcid.org/0000-0001-7629-5894
authors.initials: R
authors.lastname: Chakravorty
authors.affiliation: Melbourne EpiCentre (M.A.T.), The Royal Melbourne Hospital and Department of Medicine, University of Melbourne, Parkville, Victoria, Australia.
authors.forename: M A
authors.initials: MA
authors.lastname: Tacey
authors.affiliation: From the Department of Radiology (J.v.H., P.M.D., F.G.), The Royal Melbourne Hospital and University of Melbourne, Parkville, Victoria, Australia.
authors.forename: P M
authors.initials: PM
authors.lastname: Desmond
authors.affiliation: From the Department of Radiology (J.v.H., P.M.D., F.G.), The Royal Melbourne Hospital and University of Melbourne, Parkville, Victoria, Australia.
authors.forename: F
authors.initials: F
authors.lastname: Gaillard
mesh_terms: D000328:Adult; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D009103:Multiple Sclerosis / Q000473:pathology*; D059906:Neuroimaging / Q000379:methods*; D015588:Observer Variation; D012189:Retrospective Studies; D012984:Software