Drs. Kraus and Feng contributed equally to this work.
Trabecular morphometry by fractal signature analysis is a novel marker of osteoarthritis progression†
Article first published online: 30 NOV 2009
Copyright © 2009 by the American College of Rheumatology
Arthritis & Rheumatism
Volume 60, Issue 12, pages 3711–3722, December 2009
How to Cite
Kraus, V. B., Feng, S., Wang, S., White, S., Ainslie, M., Brett, A., Holmes, A. and Charles, H. C. (2009), Trabecular morphometry by fractal signature analysis is a novel marker of osteoarthritis progression. Arthritis & Rheumatism, 60: 3711–3722. doi: 10.1002/art.25012
The contents herein are solely the responsibility of the authors and do not necessarily represent the official view of the National Center for Research Resources, the National Institute of Arthritis and Musculoskeletal and Skin Diseases, or the NIH.
- Issue published online: 30 NOV 2009
- Article first published online: 30 NOV 2009
- Manuscript Accepted: 31 AUG 2009
- Manuscript Received: 8 APR 2009
- National Center for Research Resources. Grant Numbers: 1UL1-RR-024128-01, M01-RR-30
- National Institute of Arthritis and Musculoskeletal and Skin Diseases. Grant Number: R01-AR-48769
- Generous gift from David H. Murdock
To evaluate the effectiveness of using subchondral bone texture observed on a radiograph taken at baseline to predict progression of knee osteoarthritis (OA) over a 3-year period.
A total of 138 participants in the Prediction of Osteoarthritis Progression study were evaluated at baseline and after 3 years. Fractal signature analysis (FSA) of the medial subchondral tibial plateau was performed on fixed flexion radiographs of 248 nonreplaced knees, using a commercially available software tool. OA progression was defined as a change in joint space narrowing (JSN) or osteophyte formation of 1 grade according to a standardized knee atlas. Statistical analysis of fractal signatures was performed using a new model based on correlating the overall shape of a fractal dimension curve with radius.
Fractal signature of the medial tibial plateau at baseline was predictive of medial knee JSN progression (area under the curve [AUC] 0.75, of a receiver operating characteristic curve) but was not predictive of osteophyte formation or progression of JSN in the lateral compartment. Traditional covariates (age, sex, body mass index, knee pain), general bone mineral content, and joint space width at baseline were no more effective than random variables for predicting OA progression (AUC 0.52–0.58). The predictive model with maximum effectiveness combined fractal signature at baseline, knee alignment, traditional covariates, and bone mineral content (AUC 0.79).
We identified a prognostic marker of OA that is readily extracted from a plain radiograph using FSA. Although the method needs to be validated in a second cohort, our results indicate that the global shape approach to analyzing these data is a potentially efficient means of identifying individuals at risk of knee OA progression.