
Trabecular morphometry by fractal signature analysis is a novel marker of osteoarthritis progression
Author(s) -
Kraus Virginia Byers,
Feng Sheng,
Wang ShengChu,
White Scott,
Ainslie Maureen,
Brett Alan,
Holmes Anthony,
Charles H. Cecil
Publication year - 2009
Publication title -
arthritis & rheumatism
Language(s) - English
Resource type - Journals
eISSN - 1529-0131
pISSN - 0004-3591
DOI - 10.1002/art.25012
Subject(s) - osteoarthritis , medicine , receiver operating characteristic , radiography , fractal analysis , imaging biomarker , fractal dimension , fractal , radiology , pathology , mathematics , magnetic resonance imaging , mathematical analysis , alternative medicine
Objective 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. Methods 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. Results 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). Conclusion 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.