Trabecular morphometry by fractal signature analysis is a novel marker of osteoarthritis progression

Abstract

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.

Figures

Figure 1. Optasia KneeAnalyzer computer assisted identification of regions of interest

A) Joint segmentation was based on six manually selected initialization points (marked by x) at the lateral femur, medial femur, lateral tibia, medial tibia, lateral tibial spine, and medial tibial spine. B) Once the initialization points were selected, the software determined the joint space boundary profiles for both the lateral and medial compartments (medial compartment on right) and identified the region for fractal signature analysis in the medial subchondral bone (blue rectangular box).

Figure 2. FSA curves of progressors (red) and non-progressors (blue)

A) Knee radiographs were analyzed with the Optasia KneeAnalyzer, which generated a complex family of curves, each curve representing one individual’s fractal signature (fractal dimensions over a series of radii in mm). B) Curve fitting with quadratic and linear components showing lower mean fractal dimensions in knee OA progressors in the tension (horizontal on left) component, and higher mean fractal dimensions in knee OA progressors in the compression (vertical on right) component.

Figure 3. Representative Receiver Operating Characteristic (ROC) curves depicting the strength of the predictive models for medial OA joint space narrowing

The black diagonal line represents the result of using random variables to predict medial knee OA joint space narrowing progression. Model 1 (thick red line) uses covariates: age, gender, BMI, knee pain and bone mineral content (BMC); Model 2 (thick pink line) uses FSA alone; Model 3 (thin dark blue line) uses baseline medial joint space narrowing alone; Model 4 (thin light blue line) uses knee alignment alone; Model 8 (thin green line) with highest overall area under the curve uses covariates (age, gender, BMI, knee pain, and BMC), knee alignment, and FSA; Model 11 (thin dark line) uses knee alignment and FSA (FSA=bone texture by fractal signature analysis).