Osteoarthritis (OA) is the most common knee joint disease and the leading cause of knee pain and functional disability in adults (1). On a structural level, knee OA is characterized by loss of articular cartilage, meniscal tears and maceration, osteophytes, and microstructural changes in subchondral bone (2–5). Previous studies have suggested that bone changes occur before cartilage defects (6). The 2-dimensional (2-D) trabecular bone texture visible on plain radiography contains information directly related to 3-dimensional bone structure (7–10). Because of these findings, there is a growing interest in developing a low-cost and noninvasive trabecular bone texture–based system for predicting the progression of early and late knee OA, i.e., a method to examine the size, shape, and orientation of trabecular bone in an attempt to foresee the risk of structural progression of OA.
In a previous study, structural features on radiographs of the whole knee joint predicted OA progression, which was defined as an increase in the Kellgren/Lawrence (K/L) grade (11) from 0 at baseline to 2 at followup 20 years later (12). For the prediction, a weighted neighbor distance using compound hierarchy of algorithms representing morphology (WND-CHARM) classification system was used, and a classification accuracy of 62% was obtained. In another study, the trabecular bone texture was used to predict OA progression defined as an increase in the medial joint space narrowing (JSN) grade over a 3-year period (13). This study was conducted using a system based on a regression model and fractal signature analysis. The system achieved a prediction accuracy value (defined as the area under the curve [AUC] of the receiver operating characteristic [ROC] curve) for OA progression of 0.75. Although the results obtained using these 2 systems are promising, the interpretation of bone texture changes is not easy. This is because the image features extracted in the WND-CHARM system and the polynomial coefficients used in the regression model and fractal signature analysis–based system have little or no physical meaning. Also, the WND-CHARM system is sensitive to imaging conditions, such as magnification and rotation, while the box-counting technique used in fractal signature analysis is highly dependent on trabecular marrow pore size and signal-to-noise ratio (14).
In the present study, we used a well-defined cohort of subjects with prior meniscectomy in whom 2 sets of weight-bearing knee radiographs were obtained 4 years apart. We developed trabecular bone structure parameters based on a signature dissimilarity measure method (15) that quantifies roughness, degree of anisotropy, and direction of anisotropy of trabecular bone textures. These parameters are invariant to a range of image magnification, exposure, noise, and blur. Unlike previous studies (12, 13), we evaluated the progression of both early and late medial compartment knee OA. This allowed for a thorough assessment of the influence of changes in trabecular bone texture in the different stages of knee OA.
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In this study, we confirmed that medial tibial trabecular bone texture is predictive of loss of medial joint space in knees with OA (13). Importantly, we extended these findings to provide a detailed description of changes in trabecular bone roughness and anisotropy due to knee OA, to show for the first time that medial tibial trabecular bone texture is also predictive of medial joint space loss in knees with K/L grade equivalents of ≤1 at baseline, and that lateral tibial trabecular bone texture is predictive of medial joint space loss.
We have several possible explanations for the prediction of loss of medial compartment joint space by trabecular bone texture, and they are not mutually exclusive. First, there may be an interaction between subchondral bone remodeling and the modulation of cartilage catabolism (28). Previous studies suggest that an increase in the vascularization and remodeling rate of OA subchondral bone promotes diffusion of cytokines, eicosanoids, and growth factors into articular cartilage, thus affecting chondrocytes and inducing cartilage degradation (29–32). This could lead to the onset of secondary ossification and a decrease in cartilage thickness (33).
Second, genetic factors may play a role in the abnormal metabolism of subchondral bone, leading to morphologic alterations and subsequently to radiographic OA changes. For example, Wnt signaling antagonists are involved in the proliferation, differentiation, and mineralization of osteoblasts (34–37), and polymorphisms of their encoding genes were identified in patients with knee OA (38) and patients with hip OA (39). Also, abnormal production of insulin-like growth factor 1 in OA subchondral bone osteoblasts could increase the bone remodeling rate and stiffness, leading to cartilage matrix degradation (28).
Third, trabecular bone texture changes may indicate abnormal trabecular bone structure due to unfavorable biomechanical loading, which may also adversely affect the overlying joint cartilage. Previous studies showed that high systemic bone mineral density (BMD) increases the risk of incident knee OA and JSN (40). Meniscal damage is associated with increased BMD in the ipsilateral compartment (41) and with an increased risk of the development of subchondral bone marrow lesions (42).
For medial ROI and knees without preexisting radiographic OA, the DegA*DirA term was positively associated with an increase in the medial JSN grade (Table 3). This indicates an increase in the degree of anisotropy of trabecular bone texture and a shift in the alignment of trabeculae toward the horizontal direction. This finding is consistent with the results of previous studies in which the retention of horizontal trabeculae within the medial subchondral region was observed (3, 43). The bone architecture may be reorganized in knees with early-stage OA as a result of trabeculae being less well aligned to the main loading direction (44).
For knees with preexisting OA, the R1 and R2 terms (medial ROI) were negatively and positively associated with an increase in the medial JSN grade, respectively (Table 3). This suggests a nonlinear change in the overall roughness. The negative association of the R1 term could indicate a decrease in the overall roughness of trabecular bone, which can be associated with shorter and thicker trabeculae. Previous studies found that during OA bone remodeling, the trabecular bone thickness increased, especially in the main loading direction (3–5), and the medial tibial plateau bone area expanded (45). The expansion of the bone area has been associated with the thickening of subchondral trabeculae in knees with early and moderate OA (46). The positive association of the R2 term could indicate an increase in the number of thinner trabeculae, which can be attributed to fenestration and thinning. It was suggested that the high roughness of trabecular bone texture in late knee OA is caused by osteoporosis (43). In the present study, the DegA and DirA parameters were not found to be significantly associated with an increase in medial JSN grade, while female sex and high BMI were associated with an increased risk of progression.
We also found that the lateral compartment tibial trabecular bone texture is predictive of medial joint space loss. A possible explanation is that medial compartment OA is often associated with relative unloading of the lateral compartment, inducing bone resorption (47). Further, medial cartilage thickness correlates with lateral apparent trabecular number, thickness, and separation (47, 48).
Our study has several important limitations that we would like to point out. First, 2 different radiographic protocols were used, although both of them were with weight-bearing and with the knee in about the same degree of flexion. A pilot sample using both protocols prior to examination B did not suggest any systematic effects on the semiquantitative scoring of JSN and osteophytes. Second, all of the subjects had prior partial meniscectomy in at least 1 knee, and it is unclear whether prediction would be different for a cohort without meniscal surgery. Third, the sample size available for lateral compartment OA was too small and therefore was not analyzed. Fourth, for the prediction of OA we used discrete medial JSN and osteophyte grades, whereas the nature of knee OA is continuous. This could affect our results since the grading of OA features is subject to the reader's interpretation and the grades may not be linear with respect to the actual progression of knee OA (49). A further limitation is that we did not analyze radiographs at examination A for subjects who were lost to followup (32% of subjects). Hence, there are no estimates of whether the texture parameters differed in subjects lost to followup from those in subjects who completed examination B. Finally, the texture parameters do not provide information about bone texture changes at individual scales and directions as fractal signatures do. However, they are able to quantify texture changes at each pixel location over all scales. Further studies with large databases of knee images are needed to evaluate the full potential of different approaches in the prediction of OA.
In conclusion, the system for the automated analysis of trabecular bone texture showed promising results in the prediction of loss of tibiofemoral joint space. In particular, we showed that the texture parameters markedly improve the model for prediction of joint space based on age, sex, BMI, and JSN grade, and that a good prediction of medial joint space loss in knees with early OA progression (i.e., K/L grade equivalents of ≤1 at baseline) can be obtained. The prediction accuracy of this system needs to be further validated using large databases of knee images from other populations.
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- PATIENTS AND METHODS
- AUTHOR CONTRIBUTIONS
All authors were involved in drafting the article or revising it critically for important intellectual content, and all authors approved the final version to be published. Mr. Woloszynski had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.
Study conception and design. Woloszynski, Podsiadlo, Lohmander, Englund.
Acquisition of data. Lohmander, Englund.
Analysis and interpretation of data. Woloszynski, Podsiadlo, Stachowiak, Kurzynski, Lohmander, Englund.