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

骨关节炎 医学 接收机工作特性 射线照相术 分形分析 成像生物标志物 分形维数 内科学 分形 放射科 病理 数学 磁共振成像 数学分析 替代医学
作者
Virginia B. Kraus,Sheng Feng,Shengchu Wang,S. White,Maureen Ainslie,Alan Brett,Anthony A. Holmes,H. Cecil Charles
出处
期刊:Arthritis & Rheumatism [Wiley]
卷期号:60 (12): 3711-3722 被引量:107
标识
DOI:10.1002/art.25012
摘要

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.
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