title: Quantitative radiographic features of early knee osteoarthritis: development over 5 years and relationship with symptoms in the CHECK cohort.
abstract: OBJECTIVE
  To evaluate whether computer-assisted, interactive digital analysis of knee radiographs enables identification of different quantitative features of joint damage, and to evaluate the relationship of such features with each other and with clinical characteristics during 5-year followup in early osteoarthritis (OA).
  
  
  METHODS
  Knee radiographs from the Cohort Hip and Cohort Knee (CHECK) study, including 1002 individuals with early OA complaints, were evaluated for different measures with knee images digital analysis (KIDA). To aid definition of different radiographic features of OA, principal component analysis of KIDA was used. Features were correlated (Pearson) to each other, evaluated for changes over time, and related to clinical outcome (Western Ontario and McMaster Universities Osteoarthritis Index for pain and function) using baseline, 2-year, and 5-year followup data.
  
  
  RESULTS
  The identified radiographic features were joint space width (JSW: minimum, medial, lateral), varus angle, osteophyte area, eminence height, and bone density. The features progressed in severity at different times during followup: early (medial JSW, osteophyte area), late (minimum and lateral JSW, eminence height), and both early and late (varus angle, bone density). Correlations between different radiographic features varied between timepoints. The JSW features were most strongly related to each other (largest r = 0.82), but also, e.g., osteophytes and bone density were correlated (largest r = 0.33). The relationships with clinical outcome varied over time, but were most commonly found for osteophyte area and JSW.
  
  
  CONCLUSIONS
  In this early OA cohort, different radiographic features were identified that progressed at different rates between timepoints. The relations between radiographic features and with clinical outcome varied over time. This implies that longitudinal evaluation of different features can improve insight into progression of OA.
authors.affiliation: Rheumatology and Clinical Immunology, the Image Sciences Institute, University Medical Center Utrecht, Utrecht, The Netherlands.
authors.forename: Margot B
authors.initials: MB
authors.lastname: Kinds
authors.forename: Anne C A
authors.initials: AC
authors.lastname: Marijnissen
authors.forename: Johannes W J
authors.initials: JW
authors.lastname: Bijlsma
authors.forename: Maarten
authors.initials: M
authors.lastname: Boers
authors.forename: Floris P J G
authors.initials: FP
authors.lastname: Lafeber
authors.forename: Paco M J
authors.initials: PM
authors.lastname: Welsing
mesh_terms: D015519:Bone Density; D018450:Disease Progression; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted; D007719:Knee Joint / Q000000981:diagnostic imaging*; D008297:Male; D008875:Middle Aged; D020370:Osteoarthritis, Knee / Q000000981:diagnostic imaging*; D011857:Radiographic Image Interpretation, Computer-Assisted; D012720:Severity of Illness Index