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

Publication ,  Journal Article
Kraus, VB; Feng, S; Wang, S; White, S; Ainslie, M; Brett, A; Holmes, A; Charles, HC
Published in: Arthritis Rheum
December 2009

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.

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Published In

Arthritis Rheum

DOI

ISSN

0004-3591

Publication Date

December 2009

Volume

60

Issue

12

Start / End Page

3711 / 3722

Location

United States

Related Subject Headings

  • Tibia
  • Radiography
  • ROC Curve
  • Predictive Value of Tests
  • Osteophyte
  • Osteoarthritis, Knee
  • Male
  • Knee Joint
  • Image Processing, Computer-Assisted
  • Humans
 

Citation

APA
Chicago
ICMJE
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Kraus, V. B., Feng, S., Wang, S., White, S., Ainslie, M., Brett, A., … Charles, H. C. (2009). Trabecular morphometry by fractal signature analysis is a novel marker of osteoarthritis progression. Arthritis Rheum, 60(12), 3711–3722. https://doi.org/10.1002/art.25012
Kraus, Virginia Byers, Sheng Feng, ShengChu Wang, Scott White, Maureen Ainslie, Alan Brett, Anthony Holmes, and H Cecil Charles. “Trabecular morphometry by fractal signature analysis is a novel marker of osteoarthritis progression.Arthritis Rheum 60, no. 12 (December 2009): 3711–22. https://doi.org/10.1002/art.25012.
Kraus VB, Feng S, Wang S, White S, Ainslie M, Brett A, et al. Trabecular morphometry by fractal signature analysis is a novel marker of osteoarthritis progression. Arthritis Rheum. 2009 Dec;60(12):3711–22.
Kraus, Virginia Byers, et al. “Trabecular morphometry by fractal signature analysis is a novel marker of osteoarthritis progression.Arthritis Rheum, vol. 60, no. 12, Dec. 2009, pp. 3711–22. Pubmed, doi:10.1002/art.25012.
Kraus VB, Feng S, Wang S, White S, Ainslie M, Brett A, Holmes A, Charles HC. Trabecular morphometry by fractal signature analysis is a novel marker of osteoarthritis progression. Arthritis Rheum. 2009 Dec;60(12):3711–3722.
Journal cover image

Published In

Arthritis Rheum

DOI

ISSN

0004-3591

Publication Date

December 2009

Volume

60

Issue

12

Start / End Page

3711 / 3722

Location

United States

Related Subject Headings

  • Tibia
  • Radiography
  • ROC Curve
  • Predictive Value of Tests
  • Osteophyte
  • Osteoarthritis, Knee
  • Male
  • Knee Joint
  • Image Processing, Computer-Assisted
  • Humans