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Abstract

  1. Top of page
  2. Abstract
  3. PATIENTS AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. AUTHOR CONTRIBUTIONS
  7. REFERENCES

Objective

To develop a system for predicting the progression of radiographic knee osteoarthritis (OA) using tibial trabecular bone texture.

Methods

We studied 203 knees with (n = 68) or without (n = 135) radiographic tibiofemoral OA in 105 subjects (90 men and 15 women with a mean age of 54 years) in whom 2 sets of knee radiographs were obtained 4 years apart. We determined medial and lateral compartment tibial trabecular bone texture using an automated region selection method. Three texture parameters were calculated: roughness, degree of anisotropy, and direction of anisotropy based on a signature dissimilarity measure method. We evaluated tibiofemoral OA progression using a radiographic semiquantitative outcome: an increase in the medial joint space narrowing (JSN) grade. We examined the predictive ability of trabecular bone texture in knees with and those without preexisting radiographic OA, with adjustment for age, sex, and body mass index, using logistic regression (generalized estimating equations) and receiver operating characteristic curves.

Results

The prediction of increased medial JSN in knees with or without preexisting radiographic OA was the most accurate for medial trabecular bone texture; the area under the curve (AUC) was 0.77 and 0.75, respectively. For lateral trabecular bone texture, the AUC was 0.71 in knees with preexisting OA and 0.72 in knees without preexisting OA.

Conclusion

We have developed a system, based on analyzing tibial trabecular bone texture, which yields good prediction of loss of tibiofemoral joint space. The predictive ability of the system needs to be further validated.

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.

PATIENTS AND METHODS

  1. Top of page
  2. Abstract
  3. PATIENTS AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. AUTHOR CONTRIBUTIONS
  7. REFERENCES

Subjects.

The study was approved by the ethics committee of the Faculty of Medicine at Lund University, and informed consent was obtained from all participants. Subjects were retrospectively identified via surgical records to have undergone isolated medial or lateral meniscectomy at Lund University Hospital in 1983, 1984, or 1985 (16, 17). Exclusion criteria included cruciate ligament injury, previous knee surgery (i.e., knee surgery before the index meniscectomy), meniscectomy in both knee compartments, osteochondritis dissecans, fracture in or adjacent to the knee, septic arthritis, osteonecrosis, and radiographic signs of knee OA at the time of meniscectomy (17).

Of the 519 subjects identified, 254 who did not meet any of the exclusion criteria were invited to participate in the first radiographic followup examination (examination A) in 2000. Knee radiographs were obtained in 155 subjects who were then invited to a second radiographic examination (examination B) in 2004. Longitudinal knee radiographs were obtained in 106 subjects (Table 1). We excluded 1 subject due to the presence of bilateral radiographic end-stage OA at examination A (i.e., medial JSN grade of 3, osteotomy, or arthroplasty), and 6 other knees (in 6 different subjects) were excluded for the same reason. One knee was also excluded due to artifacts present on the radiograph that prevented image analysis, leaving 203 knees in 105 subjects for analysis.

Table 1. Characteristics of the 105 study subjects at each examination*
 Examination AExamination B
  • *

    A total of 203 knees were evaluated. Except where indicated otherwise, values are the mean ± SD. OA = osteoarthritis; JSN = joint space narrowing.

  • Approximating a Kellgren/Lawrence grade of 2 or worse.

Age, years53.6 ± 10.557.6 ± 10.5
Men, no. (%)90 (86)90 (86)
Body mass index, kg/m226.1 ± 3.426.8 ± 3.7
Time since examination A, months48.8 ± 1.0
Knees with radiographic tibiofemoral OA, no. (%)68 (33)95 (47)
JSN grade in the medial compartment, no. (%)  
 0107 (53)85 (42)
 178 (38)79 (39)
 218 (9)28 (14)
 30 (0)11 (5)

Acquisition and grading of knee radiographs.

At examination A, standing anteroposterior digital radiographs of the tibiofemoral joint at ∼15° of flexion were obtained using a fluoroscopically positioned x-ray beam (17). At examination B, posteroanterior digital radiographs of the tibiofemoral joint were obtained using the fixed flexion (SynaFlexer) protocol (18, 19). In a pilot series conduced prior to examination B, we acquired knee radiographs in 10 subjects (20 knees) on the same day using both protocols. We graded the radiographs (using side-by-side comparison) and detected some discrepancies but no statistical or systematic differences between the pairs of knees with respect to semiquantitative JSN and osteophyte scoring according to the 1995 OA Research Society International (OARSI) atlas (20). Since bone textures from radiographs obtained at examination B were not used, following 2 different protocols did not affect subsequent image analyses.

Two readers who were aware of the time sequence but were blinded with regard to clinical data and the other rater's readings graded the paired knee radiographs from examinations A and B. They evaluated the radiographs for JSN and osteophytes in the tibiofemoral joint using a 4-point scale of 0–3, where 0 indicates no evidence of JSN or osteophytes, according to the 1995 OARSI atlas (20). Interobserver agreement determined by weighted kappa was 0.84 for JSN and 0.72 for osteophytes. The radiographs with discrepancies in the JSN or osteophyte grades assigned by the 2 investigators were then adjudicated, i.e., a consensus reading was determined. At examination B, we classified 1 subject who underwent proximal tibial valgus osteotomy in the left knee between examinations as having a JSN grade of 3 in the medial compartment.

Definitions.

Radiographic knee OA.

We classified a knee as having radiographic tibiofemoral OA if one or more of the following criteria were fulfilled in either the medial compartment or the lateral compartment: JSN grade ≥2, sum of marginal osteophyte grades in the same tibiofemoral compartment ≥2, or JSN grade 1 and marginal osteophyte grade 1 in the same tibiofemoral compartment. These criteria approximate grade 2 or worse on the K/L scale.

Progression of medial compartment radiographic knee OA.

Because lateral compartment OA is rare, we focused on medial compartment OA only. We defined progression of medial compartment radiographic knee OA as an increase in the medial compartment JSN grade.

First, we analyzed all knees as one group irrespective of radiographic status at examination A. Second, we stratified the analysis according to the absence or presence of pre-existing radiographic tibiofemoral OA, as defined above. Hence, early radiographic OA progression and the progression in the medial compartment were evaluated separately.

Trabecular bone image analysis.

For image analysis, we used digital radiographs obtained at examination A (Phasix 60 generator; CGR). The radiographs were converted from DICOM to uncompressed TIFF format and stored as 8-bit gray-scale level images with a resolution of 146 μm per pixel. Previous studies showed that 8-bit images contain sufficient details for the evaluation of OA changes (13, 21). Image analysis was performed in a blinded manner.

Region selection.

An automated region selection method was used to determine the trabecular bone region of interest (ROI) on the digital radiographs (22). This method selects the ROI on the subchondral bone immediately under the cortical plate of the medial and lateral tibial compartments, respectively, in a series of steps (Figure 1). The steps include delineation of cortical bone plates using active shape model and fine ROI adjustment for fibular head, periarticular osteopenia, and subchondral bone sclerosis. The landmarks used are tibial borders, tibial spine, fibular head, and cortical plates. Epiphyseal bone and physis are not considered in the selection of ROIs. The size of each trabecular bone texture image selected was 112 × 112 pixels, which covered an area of 16.4 × 16.4 mm.

thumbnail image

Figure 1. Trabecular bone regions of interest (ROIs) selected in the analysis using an automated method. Boxed areas show the ROIs.

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Bone texture parameters.

We calculated 3 trabecular bone texture parameters, i.e., roughness, degree of anisotropy, and direction of anisotropy, using the signature dissimilarity measure method. In this method, a scale-space representation of a bone texture image is generated as a set of images in which the fine-scale features are successively smoothed. This is achieved by the convolution of the bone image with Gaussian kernels of increasing width parameter (called scale). Twenty-five scales ranging from 1 to 9 pixels in steps of 1.096n (n = 0–24) were used. After the image representation was obtained, for each pixel the gradient (edge detector) and Laplacian (smoothness detector) operators were calculated across all scales, and the extremum values of the operators were found. The difference between the extremum values and an angle associated with the extremum gradient value define roughness and orientation measures at each pixel location. A normalized histogram of the roughness (orientation) measure, called a roughness (orientation) signature, was then generated. The shape and position of the roughness signature with respect to 0 describe the roughness of the bone texture image. If the signature has its maximum value at ∼0, this indicates that, on average, the neighborhood of each pixel does not resemble either smooth regions or edge patches. For the signature skewed toward negative values, the neighborhood of each pixel has, on average, more blobs (smooth regions) than edges. For positive values, this is the opposite. The procedure described above was repeated for all bone texture images.

Roughness (R1, R2).

For measurements of roughness (i.e., complexity of bone texture), earth mover's distances (23) between all possible pairs of roughness signatures were calculated. The earth mover's distances represent an image distance space. We defined 2 independent roughness parameters (R1 and R2) as a projection of the distances calculated between the roughness signatures of trabecular bone texture images on a 2-D space. For the distance projection, Sammon's nonlinear mapping (24) was used. The space dimension was chosen as a tradeoff between avoiding the “curse of dimensionality” and being able to capture possible nonlinear relations in the distance space. The parameters provide a measure of the overall texture roughness. Higher roughness indicates that there are more sharp-edged texture features (i.e., more thin and long trabeculae and more narrow spaces in between them). The parameters were normalized in such a way that the smoothest and roughest trabecular bone texture in this study corresponded to the values (0,0) and (1, 1), respectively, for (R1, R2). Characterization of trabecular bone texture roughness provides valuable information about trabecular bone changes in OA (3, 25).

Degree of anisotropy.

The degree of anisotropy (DegA) was defined as the sum of squared bin weights in the orientation signature, i.e.,

  • equation image

where S(θ) is the weight of the angle θ in the orientation signature. The DegA parameter is a measure of the overall anisotropy of trabecular bone texture; the higher the value of the parameter, the higher the degree of anisotropy (i.e., there are more sharp-edged texture features aligned in the same direction). The parameter was normalized in such a way that the values 0 and 1 for DegA represent the least and most anisotropic trabecular bone texture, respectively. Anisotropy of trabecular bone texture changes with OA (21, 25).

Direction of anisotropy.

The direction of anisotropy (DirA) was defined as the average value of normalized bin centers in the orientation signature, i.e.,

  • equation image

This parameter measures the weighted average direction of trabeculae alignments. Each weight S(θ) is proportional to the “sharpness” of the trabeculae aligned along the angle θ, i.e., longer and thinner trabeculae have higher weights, and shorter and thicker trabeculae have lower weights. This allows for quantifying the overall direction of bone texture with a single number that depends on the sharpness and alignment of trabeculae. DirA is equal to 0° for bone texture that has all trabeculae aligned to the vertical direction of the image. OA changes in trabecular bone texture at different directions are significant (21, 26).

Statistical analysis.

To evaluate the predictive abilities of the texture parameters, we used 2 binary logistic regression models (model 1 and model 2). For both models, the covariates used were the texture parameters and their quadratic and first-order interaction terms. Neither forward/backward parameter selection nor a subset of the parameters with significant associations was used, i.e., all linear, quadratic, and interaction terms of the 3 texture parameters were always included in the models. In model 2, we further adjusted for age, sex, and body mass index (BMI). Hosmer-Lemeshow tests were used to assess goodness of fit. To account for correlation between the knees of the same subject, we estimated regression coefficients using type III generalized estimating equations with an exchangeable working correlation matrix. A prediction score for the progression of early and late medial compartment radiographic OA was calculated for each knee. The score was an average value of all covariates weighted by the regression coefficients.

We constructed the ROC curves based on the scores using a 10-fold cross-validation method (27). The cross-validation was repeated 300 times, and the averaged ROC curves were calculated. The AUC was used as a measure of the overall performance of the model. For the null model, the AUC was equal to 0.5. The 2 models were not optimized for AUC, since our aim was to develop models that can provide an accurate prediction without being optimized for a particular performance measure. Since this is an exploratory study, we focused on identifying the models and terms that are predictive of loss of tibiofemoral joint space, and hence, we did not correct significant associations in the models for multiple testing. The statistical analysis was performed using SPSS software, release 16.0.

RESULTS

  1. Top of page
  2. Abstract
  3. PATIENTS AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. AUTHOR CONTRIBUTIONS
  7. REFERENCES

Radiographic characteristics.

At examination A, 68 knees (33%) in 51 subjects (49%) were classified as having radiographic tibiofemoral OA (Table 1). Fifty-four knees (27%) in 41 subjects (39%) had an increase in the medial compartment JSN grade from examination A to examination B.

Associations of bone texture parameters with subject characteristics.

Associations of the texture parameters of medial ROI with age, sex, BMI, and medial JSN grade at examination A were calculated using analysis of covariance. The R1 and R2 parameters were both associated with age (P < 0.01) and sex (P < 0.01). The DegA and DirA parameters were both associated with BMI (P < 0.01 for both) and medial JSN grade (P = 0.02 for both). There were no significant associations for lateral ROI.

Prediction of OA progression.

All of the knees in the entire sample (n = 203) were included in this analysis. The highest prediction accuracy (AUC 0.77) was obtained using the medial ROI and model 2 (adjusted for age, sex, and BMI) (Table 2). The quadratic terms of the texture parameters R2, DegA, and DirA, and the interaction terms R1*R2 and DegA*DirA were significantly associated with an increase in medial JSN grade (Table 3). In this multivariable model with the highest prediction accuracy, the covariates age (P = 0.76), sex (P = 0.57), and BMI (P = 0.06) were not statistically significant. The ROC curves obtained for model 2 are shown in Figure 2.

Table 2. Prediction accuracies calculated as the AUC for progression of medial tibiofemoral compartment OA defined as an increase in medial joint space narrowing grade*
 Medial ROILateral ROI
Model 1Model 2Model 1Model 2
  • *

    Values are the mean area under the curve (AUC) (95% confidence interval). OA = osteoarthritis; ROI = region of interest.

  • Based on texture parameters only.

  • Based on texture parameters with adjustment for age, sex, and body mass index.

  • §

    Knees with Kellgren/Lawrence equivalents of ≤1 (see Patients and Methods).

  • Knees with Kellgren/Lawrence equivalents of ≥2 (see Patients and Methods).

Entire sample (n = 203)0.74 (0.67, 0.82)0.77 (0.70, 0.84)0.68 (0.62, 0.75)0.71 (0.64, 0.78)
Early OA progression (n = 135)§0.74 (0.67, 0.82)0.75 (0.69, 0.83)0.72 (0.64, 0.80)0.72 (0.65, 0.80)
Late OA progression (n = 68)0.76 (0.68, 0.84)0.77 (0.68, 0.86)0.68 (0.60, 0.77)0.71 (0.63, 0.79)
Table 3. Significant associations of texture parameters and covariates with an increase in medial JSN grade using model 2 and medial region of interest*
Texture parameter/covariateβ (95% CI)P
  • *

    Model 2 was based on texture parameters with adjustment for age, sex, and body mass index. JSN = joint space narrowing; 95% CI = 95% confidence interval; R1 and R2 = roughness parameters; DegA = degree of anisotropy; DirA = direction of anisotropy; OA = osteoarthritis.

  • Knees with Kellgren/Lawrence equivalents of ≤1 (see Patients and Methods).

  • Knees with Kellgren/Lawrence equivalents of ≥2 (see Patients and Methods).

Entire sample (n = 203)  
 R1*R221.3 (2.4, 40.0)0.03
 DegA*DirA12.6 (4.5, 20.5)<0.01
 R2*R2−12.4 (−17.3, −7.4)0.01
 DegA*DegA−7.5 (−10.4, −4.6)0.01
 DirA*DirA−4.9 (−6.3, −3.4)<0.01
Early OA progression (n = 135)  
 DegA*DirA12.2 (6.8, 17.6)0.02
 DegA*DegA−8.5 (−12.7, −4.2)0.05
 DirA*DirA−6.3 (−8.7, −3.9)0.02
Late OA progression (n = 68)  
 R1−16.9 (−24.5, −9.4)<0.01
 R216.5 (8.8, 24.1)<0.01
 R2*R2−23.2 (−33.3, −13.1)0.01
 Sex (male)−1.0 (−1.5, −0.5)0.02
 Body mass index2.2 (1.5, 3.0)<0.01
thumbnail image

Figure 2. Receiver operating characteristic (ROC) curves obtained for progression of early osteoarthritis (OA) and late OA, using the medial region of interest and loss of medial compartment joint space as outcome, with adjustment for age, sex, and body mass index. The solid line represents the ROC curve of the null model (area under the curve [AUC] 0.5).

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Prediction of early OA progression.

The analysis of prediction of early OA progression included knees (n = 135) that had K/L grade equivalents (calculated from the JSN and osteophyte grades) of ≤1 at examination A. The highest prediction accuracy (AUC 0.75) was found using model 2 for the medial ROI (Table 2). Significant associations were found for the interaction and quadratic terms of the DegA and DirA parameters (Table 3). Age (P = 0.85), sex (P = 0.89), and BMI (P = 0.71) were not significant

Prediction of late OA progression.

The analysis of prediction of late OA progression included knees (n = 68) with K/L grade equivalents of ≥2 at examination A. Once again, the highest prediction accuracy (AUC 0.77) was obtained using the medial ROI and model 2 (Table 2). The parameters R1, R2, their interaction term R1*R2, sex, and BMI were significantly associated with loss of medial compartment joint space (Table 3). Age (P = 0.82) was not statistically significant.

Results of models with fewer covariates.

In both models 1 and 2, the results were obtained using linear, quadratic, and interaction terms. If not all terms were used, the prediction accuracies of models 1 and 2 ranged from 0.54 to 0.60 AUC. For the models based on age, sex, and BMI, the accuracies were AUC 0.58 (for all knees), 0.52 (for knees with early OA progression), and 0.66 (for knees with late OA progression). Adding medial JSN grade at examination A to the model increased the AUC values by 0.02, and further adding the texture parameters increased them to 0.75 (for all knees), 0.74 (for knees with early OA progression), and 0.77 (for knees with late OA progression).

DISCUSSION

  1. Top of page
  2. Abstract
  3. PATIENTS AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. AUTHOR CONTRIBUTIONS
  7. REFERENCES

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.

AUTHOR CONTRIBUTIONS

  1. Top of page
  2. Abstract
  3. PATIENTS AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. AUTHOR CONTRIBUTIONS
  7. REFERENCES

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.

REFERENCES

  1. Top of page
  2. Abstract
  3. PATIENTS AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. AUTHOR CONTRIBUTIONS
  7. REFERENCES
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