Bone Density and Texture from Minimally Post-Processed Knee Radiographs in Subjects with Knee Osteoarthritis (original) (raw)
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Arthritis Research & Therapy, 2021
Background: Trabecular bone texture analysis (TBTA) has been identified as an imaging biomarker that provides information on trabecular bone changes due to knee osteoarthritis (KOA). Consequently, it is important to conduct a comprehensive review that would permit a better understanding of this unfamiliar image analysis technique in the area of KOA research. We examined how TBTA, conducted on knee radiographs, is associated to (i) KOA incidence and progression, (ii) total knee arthroplasty, and (iii) KOA treatment responses. The primary aims of this study are twofold: to provide (i) a narrative review of the studies conducted on radiographic KOA using TBTA, and (ii) a viewpoint on future research priorities. Method: Literature searches were performed in the PubMed electronic database. Studies published between June 1991 and March 2020 and related to traditional and fractal image analysis of trabecular bone texture (TBT) on knee radiographs were identified. Results: The search resulted in 219 papers. After title and abstract scanning, 39 studies were found eligible and then classified in accordance to six criteria: cross-sectional evaluation of osteoarthritis and non-osteoarthritis knees, understanding of bone microarchitecture, prediction of KOA progression, KOA incidence, and total knee arthroplasty and association with treatment response. Numerous studies have reported the relevance of TBTA as a potential bioimaging marker in the prediction of KOA incidence and progression. However, only a few studies have focused on the association of TBTA with both OA treatment responses and the prediction of knee joint replacement. Conclusion: Clear evidence of biological plausibility for TBTA in KOA is already established. The review confirms the consistent association between TBT and important KOA endpoints such as KOA radiographic incidence and progression. TBTA could provide markers for enrichment of clinical trials enhancing the screening of KOA progressors. Major advances were made towards a fully automated assessment of KOA.
Osteoarthritis and Cartilage, 2017
Objectives: To evaluate whether trabecular bone texture (TBT) parameters measured on computed radiographs could predict the onset of radiographic knee osteoarthritis (OA). Materials and Methods: Subjects from the Osteoarthritis Initiative with no sign of radiographic OA at baseline were included. Cases that developed either a global radiographic OA defined by the Kellgren-Lawrence (KL) scale, a joint space narrowing (JSN) or tibial osteophytes (TOS) were compared with the controls with no changes after 48 months of follow-up. Baseline bilateral fixed flexion computed radiographs were analyzed using a
Osteoarthritis and Cartilage, 2010
Objective: To develop an accurate method for quantifying differences in the trabecular structure in the tibial bone between subjects with and without knee osteoarthritis (OA). Methods: Standard knee radiographs were taken from 26 subjects (seven women) with meniscectomy and radiographic OA Kellgren & Lawrence grade 2 or worse in the medial compartment. Each case knee was individually matched by sex, age, body mass index and medial or lateral compartment with a control knee. A newly developed augmented Hurst orientation transform (HOT) method was used to calculate texture parameters for regions selected in X-ray images of non-OA and OA tibial bones. This method produces a mean value of fractal dimensions (FD MEAN), FDs in the vertical (FD V) and horizontal (FD H) directions and along a direction of the roughest part of the tibial bone (FD Sta), fractal signatures and a texture aspect ratio (Str). The ratio determines a degree of the bone texture anisotropy. Reproducibility was calculated using an intraclass correlation coefficient (ICC). Comparisons between cases and controls were made with paired t tests. The performance of the HOT method was evaluated against a benchmark fractal signature analysis (FSA) method. Results: Compared with controls, trabecular bone in OA knees showed significantly lower FD MEAN , FD V , FD H and FD Sta and higher Str at trabecular image sizes 0.2e1.1 mm (P < 0.05, HOT). The reproducibility of all parameters was very good (ICC > 0.8). In the medial compartment, fractal signatures calculated for OA horizontal and vertical trabeculae were significantly lower at sizes 0.3e0.55 mm (P < 0.05, HOT) and 0.3e0.65 mm (P < 0.001, FSA). In the lateral compartment, FDs calculated for OA trabeculae were lower than controls (horizontal: 0.3e0.55 mm (P < 0.05, HOT) and 0.3e0.65 mm (P < 0.001, FSA); vertical: 0.3e0.4 mm (P < 0.05, HOT) and 0.3e0.35 mm (P < 0.001, FSA). Conclusion: The augmented HOT method produces fractal signatures that are comparable to those obtained from the benchmark FSA method. The HOT method provides a more detailed description of OA changes in bone anisotropy than the FSA method. This includes a degree of bone anisotropy measured using data from all possible directions and a texture roughness calculated for the roughest part of the bone. It appears that the augmented HOT method is well suited to quantify OA changes in the tibial bone structure.
Osteoarthritis and Cartilage
Our aim was to assess the ability of radiography-based bone texture parameters in proximal femur and acetabulum to predict incident radiographic hip osteoarthritis (rHOA) over a 10 years period. Pelvic radiographs from CHECK (Cohort Hip and Cohort Knee) at baseline (987 hips) were analyzed for bone texture using fractal signature analysis in proximal femur and acetabulum. Elastic net (machine learning) was used to predict the incidence of rHOA (Kellgren-Lawrence grade (KL) ≥ 2 or total hip replacement (THR)), joint space narrowing score (JSN, range 0-3), and osteophyte score (OST, range 0-3) after 10 years. Performance of prediction models was assessed using the area under the receiver operating characteristic curve (ROC AUC). Of the 987 hips without rHOA at baseline, 435 (44%) had rHOA at 10-year follow-up. Of the 667 hips with JSN grade 0 at baseline, 471 (71%) had JSN grade ≥ 1 at 10-year follow-up. Of the 613 hips with OST grade 0 at baseline, 526 (86%) had OST grade ≥ 1 at 10-year follow-up. AUCs for the models including age, gender, and body mass index to predict incident rHOA, JSN, and OST were 0.59, 0.54, and 0.51, respectively. The inclusion of bone texture parameters in the models improved the prediction of incident rHOA (ROC AUC 0.66 and 0.71 when baseline KL was also included in the model) and JSN (ROC AUC 0.62), but not incident OST (ROC AUC 0.53). Bone texture analysis provides additional information for predicting incident rHOA or THR over 10 years.
Osteoarthritis and Cartilage, 2017
Objectives: To examine whether trabecular bone texture (TBT) parameters assessed on computed radiographs could predict knee osteoarthritis (OA) progression. Methods: This study was performed using data from the Osteoarthritis Initiative. 1647 knees in 1124 patients had bilateral fixed flexion radiographs acquired 48 months apart. Images were semi-automatically segmented to extract a patchwork of regions of interest (ROI). A fractal texture analysis was performed using different methods. OA progression was defined as an increase in the joint space narrowing (JSN) over 48 months. The predictive ability of TBT was evaluated using logistic regression and receiver operating characteristic (ROC) curve. An optimization method for features selection was used to reduce the size of models and assess the impact of each ROI. Results: Fractal dimensions were predictive of the JSN progression for each method tested with an area under the ROC curve (AUC) up to 0.71. Baseline JSN grade was not correlated with TBT parameters (R < 0.21) but had the same predictive capacity (AUC 0.71). The most predictive model included the clinical covariates (age,gender, body mass index), JSN and TBT parameters (AUC 0.77). From a statistical point of view we found higher differences in TBT parameters computed in medial ROI between progressors and non-progressors. However, the integration of TBT results from the whole patchwork including the lateral ROIs in the model provided the best predictive model. Conclusions: Our findings indicate that TBT parameters assessed in different locations in the joint provided a good predictive ability to detect knee OA progression.
BoneKEy reports, 2014
Articular cartilage and subchondral bone are the key tissues in osteoarthritis (OA). The role of the cancellous bone increasingly attracts attention in OA research. Because of its fast adaptation to changes in the loading distribution across joints, its quantification is expected to improve the diagnosis and monitoring of OA. In this study, we simulated OA progression-related changes of trabecular structure in a series of digital bone models and then characterized the potential of texture parameters and bone mineral density (BMD) as surrogate measures to quantify trabecular bone structure. Five texture parameters were studied: entropy, global and local inhomogeneity, anisotropy and variogram slope. Their dependence on OA relevant structural changes was investigated for three spatial resolutions typically used in micro computed tomography (CT; 10 μm), high-resolution peripheral quantitative CT (HR-pQCT) (90 μm) and clinical whole-body CT equipment (250 μm). At all resolutions, OA-rel...
Arthritis Research & Therapy, 2017
Background: A change of loading conditions in the knee causes changes in the subchondral bone and may be a cause of osteoarthritis (OA). However, quantification of trabecular architecture in vivo is difficult due to the limiting spatial resolution of the imaging equipment; one approach is the use of texture parameters. In previous studies, we have used digital models to simulate changes of subchondral bone architecture under OA progression. One major result was that, using computed tomography (CT) images, subchondral bone mineral density (BMD) in combination with anisotropy and global homogeneity could characterize this progression. The primary goal of this study was a comparison of BMD, entropy, anisotropy, variogram slope, and local and global inhomogeneity measurements between high-resolution peripheral quantitative CT (HR-pQCT) and CT using human cadaveric knees. The secondary goal was the verification of the spatial resolution dependence of texture parameters observed in the earlier simulations, two important prerequisites for the interpretation of in vivo measurements in OA patients. Method: The applicability of texture analysis to characterize bone architecture in clinical CT examinations was investigated and compared to results obtained from HR-pQCT. Fifty-seven human knee cadavers (OA status unknown) were examined with both imaging modalities. Three-dimensional (3D) segmentation and registration processes, together with automatic positioning of 3D analysis volumes of interest (VOIs), ensured the measurement of BMD and texture parameters at the same anatomical locations in CT and HR-pQCT datasets. Results: According to the calculation of dice ratios (>0.978), the accuracy of VOI locations between methods was excellent. Entropy, anisotropy, and global inhomogeneity showed significant and high linear correlation between both methods (0.68 < R 2 < 1.00). The resolution dependence of these parameters simulated earlier was confirmed by the in vitro measurements.
The role of bone marrow lesions in knee osteoarthritis : textural analysis of subchondral bone
2020
Osteoarthritis (OA) is the most common joint disorder in the world that affects various joints particularly hand, hip, and knee joint. The knee OA has been identified as the most impactful OA because it is the major cause of disability worldwide. Generally, OA progression leads to joint replacement surgery and causes enormous amount of financial costs. Thus, it is crucial to diagnose OA at an early stage and prevent or slow down its progression. Currently, clinical diagnosis of OA includes physical examination and clinical imaging. However, they are insensitive to early OA changes. On the other hand, it was shown that several imaging bio markers can be captured at an early stage of the disease. One of the important imaging bio markers for OA is the alternations of subchondral bone texture. Besides, there are other factors that cause these alternations such as bone marrow lesions (BML). Two sub-studies have been conducted in this thesis. The aim of the first substudy is to investigate the association between BML and OA diagnosis by using subchondral bone texture from plain radiography. OA subjects are defined by Kellgren-Lawrence (KL) grading scale. KL grade 0 and 1 represent no OA and grade 2, 3, and 4 are OA subjects. In this work, subjects at the baseline (first visit) of osteoarthritis initiative (OAI) dataset were selected. Then, they were categorised into three groups including subjects who has BML in medial tibia (group 1), subjects without BMLs at all (group 2), and lastly the subjects without medial tibia BMLs (group 3). In the next step, region of interest (ROI) was selected at the margin of medial tibia in plain radiographs. After that, 29 textural features from 4 textural descriptors including grey-level co-occurrence matrix (GLCM), histogram of image, absolute gradient, and fractal signature analysis (FSA) were computed from the extracted ROI. Subsequently, Fisher's exact test and Mann-Whitney U test were used in order to discover how textural features change among OA and non OA subjects in each group (first analysis) and how those differences change across the groups (second analysis). Our results showed that there are significant textural differences between OA and non OA subjects when they have BMLs at medial tibia. Moreover, there were no significant textural differences among subjects with no BMLs and subjects with no BMLs in medial tibia. These results indicate that the presence of BML as well as its location at subchondral bone may have association with OA incidence. In the second sub-study, for research oriented purposes we built a deep convolutional neural network (CNN) based models to automatically detect OA from subchondral bone texture according to the Kellgren-Lawrence (KL) grading scale. We selected subjects without BMLs to make a fair comparison between magnetic resonance imaging (MRI) data and plain radiographs. In this study, subjects with no BMLs who have sagittal 3-D Double Echo Steady State sequence (3D DESS MRI) and plain radiography at the baseline of OAI were selected. In both imaging modalities, square sized ROIs were chosen located at the marginal region of medial tibia. Confusion matrix and area under the receiver operating characteristics curve (ROC AUC) were used to evaluate the model performance. Our results demonstrated that when subjects do not have BMLs, our model was not able to detect OA from the subchondral bone texture.