FRI0556 Automated scoring of knee osteoarthritis (OA) on routine radiographs identifies disease severity in oa. (12th June 2018)

• Record Type: Journal Article
• Authors: Law, A.; Assi, L.; Harrison, A.; Howe, F.A.; Sofat, N.
• Is Part Of: Annals of the rheumatic diseases. Volume 77(2018)Supplement 2
• Page Start: 803
• Page End: 803
• Publication Date: 2018-06-12
• Subjects: Rheumatism -- Periodicals; 616.723005
• DOI: 10.1136/annrheumdis-2018-eular.5304
• Languages: English
• ISSNs: 0003-4967

Abstract

Abstract : Background: Knee osteoarthritis (OA) causes pain and limited function of the knee, linked to cartilage loss, bone remodelling and inflammation 1 . Objectives: We aimed to find out whether joint space measures can stratify patients into mild and advanced OA using an automated scoring tool 2 . Methods: 86 weight-bearing radiographs (target joint: 41 left, 45 right knee) were analysed by using ImageBiopsy Lab software (JSx). Automated joint space (JS) measures were joint space width (JSW), minimum height (minH) and joint space area/region of interest (JSA/ROI, in mm 2 ). JS measures were compared against Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) of pain, stiffness and function, with each subscale score normalised to a total score of 100. Body-mass index (BMI) and age data were available as potential covariates. Image analysis was initiated with landmarks automatically placed on the end of tibial plateau for each compartment, with fine manual adjustment of landmarks and contour spline if necessary. Clinical scores were compared between mild OA (n=24) and advanced OA (n=62); patient groups were defined according to their clinical pathway of standard care: medical management for early OA and knee replacement for advanced OA. SPSS Statistics 25 was used for analysis. An independent-samples t-test was used to compare the automated radiological and clinical scores between the groups of different OA severity. Subsequently the General Linear Model (GLM) was used to evaluate group differences with appropriate co-variates and fixed factors. Results: Joint space measures were strongly linked to the two clinical groups of OA severity (mild and advanced OA). Differences were significant in the medial JSA/ROI (p<0.001), medial JSW (p=0.001), medial MinH (p<0.001), WOMAC pain (p=0.001), stiffness and function (both p=0.003). Age (p=0.014) and BMI (p=0.019) were also different between the two groups of OA severity in the t-test, and were then used as co-variates in the GLM analysis along with gender and ethnicity as fixed factors. Medial JSA/ROI (p=0.027) and medial MinH (p=0.041) were still significantly different between mild and advanced OA in the multivariate test. Receiving Operating Characteristic (ROC) curves showed that medial JSA/ROI was the better discriminator for severity of OA (AUC=0.738) when compared to WOMAC scores (AUC=0.719 for WOMAC pain). Conclusions: Medial joint space measures are possible markers for identifying the stage of disease if only radiographs were used. In particular, medial JSA/ROI may be utilised as an automated tool for characterising patient severity. References: [1] Kuttapitiya A, Assi L, Laing K, et al. Microarray analysis of bone marrow lesions in osteoarthritis demonstrates upregulation of genes implicated in osteochondral turnover, neurogenesis and inflammation. Annals of the Rheumatic Diseases2017;76(10):1764–1773. [2] Hunter D, Altman R, Cicuttini F, et al. OARSI Clinical Trials Recommendations: Knee imaging in clinical trials in osteoarthritis. Osteoarthritis and Cartilage2015;23(5):698–715. Acknowledgements: The software for analysis in this study was provided by ImageBiopsy Lab and all analyses were performed independently at St George's, University of London. Disclosure of Interest: None declared