Statistical shape modelling provides a responsive measure of morphological change in knee osteoarthritis over 12 months

Jennifer S. Gregory, Rebecca J. Barr, Kanako Yoshida, Salvatore Alesci, David M. Reid, Richard M. Aspden* (Corresponding Author)

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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Objectives: Responsive biomarkers are needed to assess the progression of osteoarthritis (OA) and their lack has hampered previous clinical trials. Statistical Shape Modelling (SSM) from radiographic images identifies those at greatest risk of fast-progression or joint replacement, but its sensitivity to change has not previously been measured. This study evaluates the responsiveness of SSM in knee OA in a 12-month observational study.

Methods: 109 people were recruited, who had knee radiographs in the previous 12 months, and grouped based on severity of radiographic OA (Kellgren Lawrence grading). An SSM was built from three dual-energy x-ray absorptiometry scans at 6-month intervals. Change-over-time and OA were assessed using generalised estimating equations, Standardized Response Means (SRM) and Reliable Change (RC) Indices

Results: Mode 1 showed typical features of radiographic OA and had a strong link with KLG but did not change significantly during the study. Mode 3 showed asymmetrical changes consistent with medial cartilage loss, osteophytes and joint malalignment and was responsive to change, with a 12-month SRM of 0.63. The greatest change was observed in the moderate radiographic OA group (SRM 0.92) compared with the controls (SRM 0.21) and the RC index identified 14% of this group whose progression was clinically significant.

Conclusions: Shape changes linked the progression of osteophytosis with increasing malalignment within the joint. Modelling of the whole joint enabled quantification of change beyond the point where bone-to-bone contact has been made. The knee SSM is, therefore, a responsive biomarker for radiographic change in knees over 12 months.
Original languageEnglish
Pages (from-to)2419-2426
Number of pages8
Issue number9
Early online date14 Jan 2020
Publication statusPublished - Sept 2020

Bibliographical note

Funding: This study was supported by an award (Ref: WHMSB_AU068/071) from the Translational Medicine Research Collaboration (TMRC) – a consortium made up of the Universities of Aberdeen, Dundee, Edinburgh and Glasgow, the four associated NHS Health Boards (Grampian, Tayside, Lothian and Greater Glasgow & Clyde), Scottish Enterprise and initially Wyeth, now Pfizer. The funder had no involvement in the collection, analysis and interpretation of data; in the writing of the manuscript; or in the decision to submit the manuscript for publication. Dr J.S. Gregory was the holder of an MRC New Investigator award (Ref: G0901242).


We are grateful to all the study participants. We thank Lana Gibson and Jennifer Scott for their expertise with the iDXA scanner as well as iDXA precision data and Dr Sandro Galea-Solar for assistance with KL grading.


  • knee osteoarthritis
  • statistical shape modelling
  • Kellgren-Lawrence grading
  • imaging biomarker
  • reliable change
  • Kellgren–Lawrence grading


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