Subchondral tibial bone texture analysis predicts knee osteoarthritis progression: data from the Osteoarthritis Initiative: Tibial bone texture & knee OA progression

Machine learning and deep neural network-based learning in osteoarthritis knee

Osteoarthritis (OA) of the knee joint is considered the commonest musculoskeletal condition leading to marked disability for patients residing in various regions around the globe. Application of machine learning (ML) in doing research regarding OA has brought about various clinical advances viz, OA being diagnosed at preliminary stages, prediction of chances of development of OA among the population, discovering various phenotypes of OA, calculating the severity in OA structure and also discovering people with slow and fast progression of disease pathology, etc. Various publications are available regarding machine learning methods for the early detection of osteoarthritis. The key features are detected by morphology, molecular architecture, and electrical and mechanical functions. In addition, this particular technique was utilized to assess non-interfering, non-ionizing, and in-vivo techniques using magnetic resonance imaging. ML is being utilized in OA, chiefly with the formulation of large cohorts viz, the OA Initiative, a cohort observational study, the Multi-centre Osteoarthritis Study, an observational, prospective longitudinal study and the Cohort Hip & Cohort Knee, an observational cohort prospective study of both hip and knee OA. Though ML has various contributions and enhancing applications, it remains an imminent field with high potential, also with its limitations. Many more studies are to be carried out to find more about the link between machine learning and knee osteoarthritis, which would help in the improvement of making decisions clinically, and expedite the necessary interventions.

Short-term variations in trabecular bone texture parameters associated to radio-clinical biomarkers improve the prediction of radiographic knee osteoarthritis progression

The present study aims to examine whether the short-term variations in trabecular bone texture (TBT) parameters, combined with a targeted set of clinical and radiographic data, would improve the prediction of long-term radiographic knee osteoarthritis (KOA) progression. Longitudinal (baseline, 24 and 48-month) data, obtained from the Osteoarthritis Initiative cohort, were available for 1352 individuals, with preexisting OA (1 < Kellgren-Lawrence < 4) at baseline. KOA progression was defined as an increase in the medial joint space narrowing score from the 24-months to the 48-months control point. 16 regions of interest were automatically selected from each radiographic knee and analyzed using fractal dimension. Variations from baseline to 24 months in TBT descriptors as well as selected radiographic and clinical readings were calculated. Different logistic regression models were developed to evaluate the progression prediction performance when associating TBT variations with the selected clinical and radiographic readings. The most predictive model was mainly determined using the area under the receiver operating characteristic curve (AUC). The proposed prediction model including short-term variations in TBT parameters, associated with clinical covariates and radiographic scores, improved the capacity of predicting long-term radiographic KOA progression (AUC of 0.739), compared to models based solely on baseline values (AUC of 0.676, p-value < 0.008).

Subchondral bone in knee osteoarthritis: bystander or treatment target?

The subchondral bone is an important structural component of the knee joint relevant for osteoarthritis (OA) incidence and progression once disease is established. Experimental studies have demonstrated that subchondral bone changes are not simply the result of altered biomechanics, i.e., pathologic loading. In fact, subchondral bone alterations have an impact on joint homeostasis leading to articular cartilage loss already early in the disease process. This narrative review aims to summarize the available and emerging imaging techniques used to evaluate knee OA-related subchondral bone changes and their potential role in clinical trials of disease-modifying OA drugs (DMOADs). Radiographic fractal signature analysis has been used to quantify OA-associated changes in subchondral texture and integrity. Cross-sectional modalities such as cone-beam computed tomography (CT), contrast-enhanced cone beam CT, and micro-CT can also provide high-resolution imaging of the subchondral trabecular morphometry. Magnetic resonance imaging (MRI) has been the most commonly used advanced imaging modality to evaluate OA-related subchondral bone changes such as bone marrow lesions and altered trabecular bone texture. Dual-energy X-ray absorptiometry can provide insight into OA-related changes in periarticular subchondral bone mineral density. Positron emission tomography, using physiological biomarkers of subchondral bone regeneration, has provided additional insight into OA pathogenesis. Finally, artificial intelligence algorithms have been developed to automate some of the above subchondral bone measurements. This paper will particularly focus on semiquantitative methods for assessing bone marrow lesions and their utility in identifying subjects at risk of symptomatic and structural OA progression, and evaluating treatment responses in DMOAD clinical trials.

Biomarkers in osteoarthritis: current status and outlook - the FNIH Biomarkers Consortium PROGRESS OA study

Currently, no disease-modifying therapies are approved for osteoarthritis (OA) use. One obstacle to trial success in this field has been our existing endpoints' limited validity and responsiveness. To overcome this impasse, the Foundation for the NIH OA Biomarkers Consortium is focused on investigating biomarkers for a prognostic context of use for subsequent qualification through regulatory agencies. This narrative review describes this activity and the work underway, focusing on the PROGRESS OA study.

3D profiling of mouse epiphyses across ages reveals new potential imaging biomarkers of early spontaneous osteoarthritis

Worldwide research groups and funding bodies have highlighted the need for imaging biomarkers to predict osteoarthritis (OA) progression and treatment effectiveness. Changes in trabecular architecture, which can be detected with non-destructive high-resolution CT imaging, may reveal OA progression before apparent articular surface damage. Here, we analysed the tibial epiphyses of STR/Ort (OA-prone) and CBA (healthy, parental control) mice at different ages to characterise the effects of mouse age and strain on multiple bony parameters. We isolated epiphyseal components using a semi-automated method, and measured the total epiphyseal volume; cortical bone, trabecular bone and marrow space volumes; mean trabecular and cortical bone thicknesses; trabecular volume relative to cortical volume; trabecular volume relative to epiphyseal interior (trabecular BV/TV); and the trabecular degree of anisotropy. Using two-way ANOVA (significance level ≤0.05), we confirmed that all of these parameters change significantly with age, and that the two strains were significantly different in cortical and trabecular bone volumes, and trabecular degree of anisotropy. STR/Ort mice had higher cortical and trabecular volumes and a lower degree of anisotropy. As the two mouse strains reflect markedly divergent OA predispositions, these parameters have potential as bioimaging markers to monitor OA susceptibility and progression. Additionally, significant age/strain interaction effects were identified for total epiphyseal volume, marrow space volume and trabecular BV/TV. These interactions confirm that the two mouse strains have different epiphyseal growth patterns throughout life, some of which emerge prior to OA onset. Our findings not only propose valuable imaging biomarkers of OA, but also provide insight into ageing 3D epiphyseal architecture bone profiles and skeletal biology underlying the onset and development of age-related OA in STR/Ort mice.

A discriminative shape-texture convolutional neural network for early diagnosis of knee osteoarthritis from X-ray images

Knee Osteoarthritis (OA) is one of the most common causes of physical disability worldwide associated with a significant personal and socioeconomic burden. Deep Learning approaches based on Convolutional Neural Networks (CNNs) achieved remarkable improvements in knee OA detection. Despite this success, the problem of early knee OA diagnosis from plain radiographs remains a challenging task. This is due to the high similarity between the X-ray images of OA and non-OA subjects and the disappearance of texture information regarding bone microarchitecture changes in the top layers during the learning process of the CNN models. To address these issues, we propose a Discriminative Shape-Texture Convolutional Neural Network (DST-CNN), which automatically diagnoses early knee OA from X-ray images. The proposed model incorporates a discriminative loss to improve class separability and deal with high inter-class similarities. In addition, a new Gram Matrix Descriptor (GMD) block is embedded in the CNN architecture to compute texture features from several intermediate layers and combine them with the shape features in the top layers. We show that merging texture features with deep ones leads to better prediction of the early stages of OA. Comprehensive experimental results on two large public databases, Osteoarthritis Initiative (OAI) and Multicenter Osteoarthritis Study (MOST) demonstrate the potential of the proposed network. Ablation studies and visualizations are provided for a detailed understanding of our proposed approach.

A joint multi-modal learning method for early-stage knee osteoarthritis disease classification

Osteoarthritis (OA) is a progressive and chronic disease. Identifying the early stages of OA disease is important for the treatment and care of patients. However, most state-of-the-art methods only use single-modal data to predict disease status, so that these methods usually ignore complementary information in multi-modal data. In this study, we develop an integrated multi-modal learning method (MMLM) that uses an interpretable strategy to select and fuse clinical, imaging, and demographic features to classify the grade of early-stage knee OA disease. MMLM applies XGboost and ResNet50 to extract two heterogeneous features from the clinical data and imaging data, respectively. And then we integrate these extracted features with demographic data. To avoid the negative effects of redundant features in a direct integration of multiple features, we propose a L1-norm-based optimization method (MMLM) to regularize the inter-correlations among the multiple features. MMLM was assessed using the Osteoarthritis Initiative (OAI) data set with machine learning classifiers. Extensive experiments demonstrate that MMLM improves the performance of the classifiers. Furthermore, a visual analysis of the important features in the multimodal data verified the relations among the modalities when classifying the grade of knee OA disease.

Identification of Immune-Related Risk Genes in Osteoarthritis Based on Bioinformatics Analysis and Machine Learning

In this research, we aimed to perform a comprehensive bioinformatic analysis of immune cell infiltration in osteoarthritic cartilage and synovium and identify potential risk genes. Datasets were downloaded from the Gene Expression Omnibus database. We integrated the datasets, removed the batch effects and analyzed immune cell infiltration along with differentially expressed genes (DEGs). Weighted gene co-expression network analysis (WGCNA) was used to identify the positively correlated gene modules. LASSO (least absolute shrinkage and selection operator)-cox regression analysis was performed to screen the characteristic genes. The intersection of the DEGs, characteristic genes and module genes was identified as the risk genes. The WGCNA analysis demonstrates that the blue module was highly correlated and statistically significant as well as enriched in immune-related signaling pathways and biological functions in the KEGG and GO enrichment. LASSO-cox regression analysis screened 11 characteristic genes from the hub genes of the blue module. After the DEG, characteristic gene and immune-related gene datasets were intersected, three genes, PTGS1, HLA-DMB and GPR137B, were identified as the risk genes in this research. In this research, we identified three risk genes related to the immune system in osteoarthritis and provide a feasible approach to drug development in the future.

Radiographic Biomarkers for Knee Osteoarthritis: A Narrative Review

Conventional radiography remains the most widely available imaging modality in clinical practice in knee osteoarthritis. Recent research has been carried out to develop novel radiographic biomarkers to establish the diagnosis and to monitor the progression of the disease. The growing number of publications on this topic over time highlights the necessity of a renewed review. Herein, we propose a narrative review of a selection of original full-text articles describing human studies on radiographic imaging biomarkers used for the prediction of knee osteoarthritis-related outcomes. To achieve this, a PubMed database search was used. A total of 24 studies were obtained and then classified based on three outcomes: (1) prediction of radiographic knee osteoarthritis incidence, (2) knee osteoarthritis progression and (3) knee arthroplasty risk. Results showed that numerous studies have reported the relevance of joint space narrowing score, Kellgren-Lawrence score and trabecular bone texture features as potential bioimaging markers in the prediction of the three outcomes. Performance results of reviewed prediction models were presented in terms of the area under the receiver operating characteristic curves. However, fair and valid comparisons of the models' performance were not possible due to the lack of a unique definition of each of the three outcomes.

Defining and predicting radiographic knee osteoarthritis progression: a systematic review of findings from the osteoarthritis initiative

PURPOSE

The purposes of this systematic review were to (1) identify the commonly used definitions of radiographic KOA progression, (2) summarize the important associative risk factors for disease progression based on findings from the OAI study and (3) summarize findings from radiographic KOA progression prediction modeling studies regarding the characterization of progression and outcomes.

METHODS

A systematic review was performed by conducting a literature search of definitions, risk factors and predictive models for radiographic KOA progression that utilized data from the OAI database. Radiographic progression was further characterized into "accelerated KOA" and "typical progression," as defined by included studies.

RESULTS

Of 314 studies identified, 41 studies were included in the present review. Twenty-eight (28) studies analyzed risk factors associated with KOA progression, and 13 studies created or validated prediction models or risk calculators for progression. Kellgren-Lawrence (KL) grade based on radiographs was most commonly used to characterize KOA progression (50%), followed by joint space width (JSW) narrowing (32%) generally over 48 months. Risk factors with the highest odds ratios (OR) for progression included periarticular bone mineral density (OR 10.40), any knee injury within 1 year (OR 9.22) and baseline bone mineral lesions (OR 7.92). Nine prediction modeling studies utilized both clinical and structural risk factors to inform their models, and combined models outperformed purely clinical or structural models.

CONCLUSION

The cumulative evidence suggests that combinations of structural and clinical risk factors may be able to predict radiographic KOA progression, particularly in patients with accelerated progression. Clinically relevant and feasible prediction models and risk calculators may provide valuable decision-making support when caring for patients at risk of KOA progression, although standardization in modeling and variable identification does not yet exist.

Subchondral tibial bone texture of conventional X-rays predicts total knee arthroplasty

Lacking disease-modifying osteoarthritis drugs (DMOADs) for knee osteoarthritis (KOA), Total Knee Arthroplasty (TKA) is often considered an important clinical outcome. Thus, it is important to determine the most relevant factors that are associated with the risk of TKA. The present study aims to develop a model based on a combination of X-ray trabecular bone texture (TBT) analysis, and clinical and radiological information to predict TKA risk in patients with or at risk of developing KOA. This study involved 4382 radiographs, obtained from the OsteoArthritis Initiative (OAI) cohort. Cases were defined as patients with TKA on at least one knee prior to the 108-month follow-up time point and controls were defined as patients who had never undergone TKA. The proposed TKA-risk prediction model, combining TBT parameters and Kellgren–Lawrence (KL) grades, was performed using logistic regression. The proposed model achieved an AUC of 0.92 (95% Confidence Interval [CI] 0.90, 0.93), while the KL model achieved an AUC of 0.86 (95% CI 0.84, 0.86; p < 0.001). This study presents a new TKA prediction model with a good performance permitting the identification of at risk patient with a good sensitivy and specificity, with a 60% increase in TKA case prediction as reflected by the recall values.

Prediction of knee osteoarthritis progression using radiological descriptors obtained from bone texture analysis and Siamese neural networks: data from OAI and MOST cohorts

Background

Trabecular bone texture (TBT) analysis has been identified as an imaging biomarker that provides information on trabecular bone changes due to knee osteoarthritis (KOA). In parallel with the improvement in medical imaging technologies, machine learning methods have received growing interest in the scientific osteoarthritis community to potentially provide clinicians with prognostic data from conventional knee X-ray datasets, in particular from the Osteoarthritis Initiative (OAI) and the Multicenter Osteoarthritis Study (MOST) cohorts.

Patients and methods

This study included 1888 patients from OAI and 683 patients from MOST cohorts. Radiographs were automatically segmented to determine 16 regions of interest. Patients with an early stage of OA risk, with Kellgren and Lawrence (KL) grade of 1 < KL < 4, were selected. The definition of OA progression was an increase in the OARSI medial joint space narrowing (mJSN) grades over 48 months in OAI and 60 months in MOST. The performance of the TBT-CNN model was evaluated and compared to well-known prediction models using logistic regression.

Results

The TBT-CNN model was predictive of the JSN progression with an area under the curve (AUC) up to 0.75 in OAI and 0.81 in MOST. The predictive ability of the TBT-CNN model was invariant with respect to the acquisition modality or image quality. The prediction models performed significantly better with estimated KL (KLprob) grades than those provided by radiologists. TBT-based models significantly outperformed KLprob-based models in MOST and provided similar performances in OAI. In addition, the combined model, when trained in one cohort, was able to predict OA progression in the other cohort.

Conclusion

The proposed combined model provides a good performance in the prediction of mJSN over 4 to 6 years in patients with relevant KOA. Furthermore, the current study presents an important contribution in showing that TBT-based OA prediction models can work with different databases.

Trabecular bone texture analysis of conventional radiographs in the assessment of knee osteoarthritis: review and viewpoint

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.

Association Between Clinical Symptoms and Radiographic Features in Late-Stage Knee Osteoarthritis Using a New Radiographic Parameter

OBJECTIVE

In this study, we proposed a new radiographic parameter, the plateau attrition index (PAI), and the PAI grades (PAIs) to explore the relationship between subchondral attrition of the tibial plateau and symptoms of knee osteoarthritis (OA) in patients with late-stage knee osteoarthritis.

METHOD

One hundred nineteen patients with late-stage knee osteoarthritis were enrolled. The Kellgren and Lawrence (K/L) grades and hip-knee-ankle (HKA) angle were used to characterize the radiographic features of knee OA. The bone attrition of the tibial plateau was determined by the PAI and PAIs. The symptoms of knee OA were assessed by the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), which is composed of the WOMAC pain (WOMP), WOMAC stiffness (WOMS), and WOMAC function (WOMF) subscores. WOMAC pain scores were divided into non-weight-bearing pain (NWBP) and weight-bearing pain (WBP) subcategories. The Pearson correlation coefficient was used to determine the relationship between the PAI, HKA angle, and WOMAC scores. The Spearman rank correlation coefficient was used to evaluate the correlation between the WOMAC score and the PAIs and K/L grades.

RESULTS

The distribution of the WOMAC scores according to the PAIs was significant (P < .01). A positive correlation was identified between the PAI and the WOMAC, WOMP, WOMF and WBP scores (r = 0.29, 0.34, 0.26 and 0.34, P < .01, respectively). In addition, the PAIs was also significantly correlated with the WOMAC, WOMP, WOMF, and WBP scores (r = 0.37, 0.38, 0.35 and 0.44, P < .01, respectively).

CONCLUSIONS

The attrition of tibial subchondral bone determined by the new parameter, the plateau attrition index, was correlated with symptoms, especially weight-bearing pain in late-stage knee OA.

Discrimination of Low-Energy Acetabular Fractures from Controls Using Computed Tomography-Based Bone Characteristics

The incidence of low-energy acetabular fractures has increased. However, the structural factors for these fractures remain unclear. The objective of this study was to extract trabecular bone architecture and proximal femur geometry (PFG) measures from clinical computed tomography (CT) images to (1) identify possible structural risk factors of acetabular fractures, and (2) to discriminate fracture cases from controls using machine learning methods. CT images of 107 acetabular fracture subjects (25 females, 82 males) and 107 age-gender matched controls were examined. Three volumes of interest, one at the acetabulum and two at the femoral head, were extracted to calculate bone volume fraction (BV/TV), gray-level co-occurrence matrix and histogram of the gray values (GV). The PFG was defined by neck shaft angle and femoral neck axis length. Relationships between the variables were assessed by statistical mean comparisons and correlation analyses. Bayesian logistic regression and Elastic net machine learning models were implemented for classification. We found lower BV/TV at the femoral head (0.51 vs. 0.55, p = 0.012) and lower mean GV at both the acetabulum (98.81 vs. 115.33, p < 0.001) and femoral head (150.63 vs. 163.47, p = 0.005) of fracture subjects when compared to their matched controls. The trabeculae within the femoral heads of the acetabular fracture sides differed in structure, density and texture from the corresponding control sides of the fracture subjects. Moreover, the PFG and trabecular architectural variables, alone and in combination, were able to discriminate fracture cases from controls (area under the receiver operating characteristics curve 0.70 to 0.79). In conclusion, lower density in the acetabulum and femoral head with abnormal trabecular structure and texture at the femoral head, appear to be risk factors for low-energy acetabular fractures.

Discriminative Regularized Auto-Encoder for Early Detection of Knee OsteoArthritis: Data from the Osteoarthritis Initiative

OsteoArthritis (OA) is the most common disorder of the musculoskeletal system and the major cause of reduced mobility among seniors. The visual evaluation of OA still suffers from subjectivity. Recently, Computer-Aided Diagnosis (CAD) systems based on learning methods showed potential for improving knee OA diagnostic accuracy. However, learning discriminative properties can be a challenging task, particularly when dealing with complex data such as X-ray images, typically used for knee OA diagnosis. In this paper, we introduce a Discriminative Regularized Auto Encoder (DRAE) that allows to learn both relevant and discriminative properties that improve the classification performance. More specifically, a penalty term, called discriminative loss is combined with the standard Auto-Encoder training criterion. This additional term aims to force the learned representation to contain discriminative information. Our experimental results on data from the public multicenter OsteoArthritis Initiative (OAI) show that the developed method presents potential results for early knee OA detection.

A novel method for determining the Femoral-Tibial Angle of Knee Osteoarthritis on X-ray radiographs: data from the Osteoarthritis Initiative

Femoral-tibial alignment is a prominent risk factor for Knee Osteoarthritis (KOA) incidence and progression. One way of assessing alignment is by determining the Femoral-Tibial Angle (FTA). Several studies have investigated FTA determination; however, methods of assessment of FTA still present challenges. This paper introduces a new method for semi-automatic measurement of FTA as part of KOA research. Our novel approach combines preprocessing of X-ray images and the use of Active Shape Model (ASM) as the femoral and tibial segmentation method, followed by a thinning process. The result of the thinning process is used to predict FTA automatically by measuring the angle between the intersection of the two vectors of branching points on the femoral and tibial areas. The proposed method is trained on 10 x-ray images and tested on 50 different x-ray images of the Osteoarthritis Initiative (OAI) dataset. The outcomes of this approach were compared with manually obtained FTA measurements from the OAI dataset as the ground truth. Based on experiments, the difference in measurement results between the FTA of the OAI and the FTA obtained using our method is quite small, i.e., below 0.81° for the right FTA and below 0.77° for the left FTA with minimal average errors. This result indicates that this method is clinically suitable for semi-automatic measurement of the FTA.

Deep learning risk assessment models for predicting progression of radiographic medial joint space loss over a 48-MONTH follow-up period

OBJECTIVE

To develop and evaluate deep learning (DL) risk assessment models for predicting the progression of radiographic medial joint space loss using baseline knee X-rays.

METHODS

Knees from the Osteoarthritis Initiative without and with progression of radiographic joint space loss (defined as ≥ 0.7 mm decrease in medial joint space width measurement between baseline and 48-month follow-up X-rays) were randomly stratified into training (1400 knees) and hold-out testing (400 knees) datasets. A DL network was trained to predict the progression of radiographic joint space loss using the baseline knee X-rays. An artificial neural network was used to develop a traditional model for predicting progression utilizing demographic and radiographic risk factors. A combined joint training model was developed using a DL network to extract information from baseline knee X-rays as a feature vector, which was further concatenated with the risk factor data vector. Area under the curve (AUC) analysis was performed using the hold-out test dataset to evaluate model performance.

RESULTS

The traditional model had an AUC of 0.660 (61.5% sensitivity and 64.0% specificity) for predicting progression. The DL model had an AUC of 0.799 (78.0% sensitivity and 75.5% specificity), which was significantly higher (P < 0.001) than the traditional model. The combined model had an AUC of 0.863 (80.5% sensitivity and specificity), which was significantly higher than the DL (P = 0.015) and traditional (P < 0.001) models.

CONCLUSION

DL models using baseline knee X-rays had higher diagnostic performance for predicting the progression of radiographic joint space loss than the traditional model using demographic and radiographic risk factors.

Multimodal Machine Learning-based Knee Osteoarthritis Progression Prediction from Plain Radiographs and Clinical Data

Knee osteoarthritis (OA) is the most common musculoskeletal disease without a cure, and current treatment options are limited to symptomatic relief. Prediction of OA progression is a very challenging and timely issue, and it could, if resolved, accelerate the disease modifying drug development and ultimately help to prevent millions of total joint replacement surgeries performed annually. Here, we present a multi-modal machine learning-based OA progression prediction model that utilises raw radiographic data, clinical examination results and previous medical history of the patient. We validated this approach on an independent test set of 3,918 knee images from 2,129 subjects. Our method yielded area under the ROC curve (AUC) of 0.79 (0.78–0.81) and Average Precision (AP) of 0.68 (0.66–0.70). In contrast, a reference approach, based on logistic regression, yielded AUC of 0.75 (0.74–0.77) and AP of 0.62 (0.60–0.64). The proposed method could significantly improve the subject selection process for OA drug-development trials and help the development of personalised therapeutic plans.

A pilot study of peripheral blood DNA methylation models as predictors of knee osteoarthritis radiographic progression: data from the Osteoarthritis Initiative (OAI)

Knee osteoarthritis (OA) is a leading cause of chronic disability worldwide, but no diagnostic or prognostic biomarkers are available. Increasing evidence supports epigenetic dysregulation as a contributor to OA pathogenesis. In this pilot study, we investigated epigenetic patterns in peripheral blood mononuclear cells (PBMCs) as models to predict future radiographic progression in OA patients enrolled in the longitudinal Osteoarthritis Initiative (OAI) study. PBMC DNA was analyzed from baseline OAI visits in 58 future radiographic progressors (joint space narrowing at 24 months, sustained at 48 months) compared to 58 non-progressors. DNA methylation was quantified via Illumina microarrays and beta- and M-values were used to generate linear classification models. Data were randomly split into a 60% development and 40% validation subsets, models developed and tested, and cross-validated in a total of 40 cycles. M-value based models outperformed beta-value based models (ROC-AUC 0.81 ± 0.01 vs. 0.73 ± 0.02, mean ± SEM, comparison p = 0.002), with a mean classification accuracy of 73 ± 1% (mean ± SEM) for M- and 69 ± 1% for beta-based models. Adjusting for covariates did not significantly alter model performance. Our findings suggest that PBMC DNA methylation-based models may be useful as biomarkers of OA progression and warrant additional evaluation in larger patient cohorts.

Bone texture analysis for prediction of incident radiographic hip osteoarthritis using machine learning: data from the Cohort Hip and Cohort Knee (CHECK) study

OBJECTIVE

To assess the ability of radiography-based bone texture variables in proximal femur and acetabulum to predict incident radiographic hip osteoarthritis (rHOA) over a 10 years period.

DESIGN

Pelvic radiographs from CHECK at baseline (987 hips) were analyzed for bone texture using fractal signature analysis (FSA) in proximal femur and acetabulum. Elastic net (machine learning) was used to predict the incidence of rHOA (including 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).

RESULTS

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 (BMI) to predict incident rHOA, JSN, and OST were 0.59, 0.54, and 0.51, respectively. The inclusion of bone texture variables in the models improved the prediction of incident rHOA (ROC AUC 0.68 and 0.71 when baseline KL was also included in the model) and JSN (ROC AUC 0.62), but not incident OST (ROC AUC 0.52).

CONCLUSION

Bone texture analysis provides additional information for predicting incident rHOA or THR over 10 years.

Bone Density and Texture from Minimally Post-Processed Knee Radiographs in Subjects with Knee Osteoarthritis

Plain radiography is the most common modality to assess the stage of osteoarthritis. Our aims were to assess the relationship of radiography-based bone density and texture between radiographs with minimal and clinical post-processing, and to compare the differences in bone characteristics between controls and subjects with knee osteoarthritis or medial tibial bone marrow lesions (BMLs). Tibial bone density and texture was evaluated from radiographs with both minimal and clinical post-processing in 109 subjects with and without osteoarthritis. Bone texture was evaluated using fractal signature analysis. Significant correlations (p < 0.001) were found in all regions (between 0.94 and 0.97) for calibrated bone density between radiographs with minimal and clinical post-processing. Correlations varied between 0.51 and 0.97 (p < 0.001) for FD_Ver texture parameter and between − 0.10 and 0.97 for FD_Hor. Bone density and texture were different (p < 0.05) between controls and subjects with osteoarthritis or BMLs mainly in medial tibial regions. When classifying healthy and osteoarthritic subjects using a machine learning-based elastic net model with bone characteristics, area under the receiver operating characteristics (ROCAUC) curve was 0.77. For classifying controls and subjects with BMLs, ROCAUC was 0.85. In conclusion, differences in bone density and texture can be assessed from knee radiographs when using minimal post-processing.

A decision support tool for early detection of knee OsteoArthritis using X-ray imaging and machine learning: Data from the OsteoArthritis Initiative

This paper presents a fully developed computer aided diagnosis (CAD) system for early knee OsteoArthritis (OA) detection using knee X-ray imaging and machine learning algorithms. The X-ray images are first preprocessed in the Fourier domain using a circular Fourier filter. Then, a novel normalization method based on predictive modeling using multivariate linear regression (MLR) is applied to the data in order to reduce the variability between OA and healthy subjects. At the feature selection/extraction stage, an independent component analysis (ICA) approach is used in order to reduce the dimensionality. Finally, Naive Bayes and random forest classifiers are used for the classification task. This novel image-based approach is applied on 1024 knee X-ray images from the public database OsteoArthritis Initiative (OAI). The results show that the proposed system has a good predictive classification rate for OA detection (82.98% for accuracy, 87.15% for sensitivity and up to 80.65% for specificity).

Assessing the effects of long-term osteoporosis treatment by using conventional spine radiographs: results from a pilot study in a sub-cohort of a large randomized controlled trial

OBJECTIVE

To evaluate the clinical applicability of a software tool developed to extract bone textural information from conventional lumbar spine radiographs, and to test it in a subset of postmenopausal women treated for osteoporosis with the fully human monoclonal antibody denosumab.

METHODS

The software was developed based on the principles of a fractal model using pixel grey-level variations together with a specific machine-learning algorithm. The obtained dimensionless parameter, termed bone structure value (BSV), was then tested and compared to bone mineral density (BMD) in a sub-cohort of postmenopausal women with osteoporosis who were treated with the monoclonal antibody denosumab, within the framework of a large randomized controlled trial and its open-label extension phase.

RESULTS

After 3 years and after 8 years of treatment with denosumab, mean lumbar spine BMD as well as mean lumbar BSV were significantly higher compared to study entry (one-way repeated measures ANOVA for DXA: F = 108.2, p < 0.00001; and for BSV: F = 84.3, p < 0.00001). The overall increase in DXA-derived lumbar spine BMD at year 8 was + 42% (mean ± SD; 0.725 ± 0.038 g/cm² to 1.031 ± 0.092 g/cm²; p < 0.0001), and the overall increase of BSV was 255% (mean ± SD; 0.076 ± 0.022 to 0.270 ± 0.09, p < 0.0001). Overall, BMD and BSV were significantly correlated (R = 0.51; p < 0.0001).

CONCLUSIONS

This pilot study provides evidence that lumbar spine BSV as obtained from conventional radiographs constitutes a useful means for the assessment of bone-specific treatment effects in postmenopausal women with osteoporosis.

Subchondral trabecular bone integrity changes following ACL injury and reconstruction: a cohort study with a nested, matched case-control analysis

OBJECTIVE

There is limited information regarding changes in bone architecture following anterior cruciate ligament (ACL) injury. The objective of this study was to evaluate differences in tibial fractal signature in the medial and lateral compartments following ACL injury and describe how these values change following ACL-reconstruction and return to activity.

DESIGN

This was a prospective cohort study with a nested case-control analysis. ACL-injured subjects and matched controls were evaluated at pre-surgical baseline and post ACL reconstruction follow-up at a mean of 46 months. Serial Fractal Dimensions (FD) of tibial subchondral bone architecture were calculated in medial and lateral regions of interest in the horizontal and vertical dimensions.

RESULTS

In the medial and lateral compartments, there were significant differences in the vertical FD signature for ACL-injured subjects at final follow-up, when compared to the contralateral healthy tibia (medial P < 0.0001; lateral P < 0.0001) and the control group (medial P = 0.01; lateral P < 0.0001). Similarly, in the lateral compartment, there were significant differences in the horizontal FD profile for ACL-injured subjects at final follow-up, when compared to the contralateral healthy tibia (P = 0.003) and the controls (P < 0.0001). There were no significant side-to-side differences in FDs among healthy control subjects in the medial or lateral compartments at baseline or final follow-up.

CONCLUSION

At 46-month follow-up, FD profiles are significantly different, and show an overall lower FD signature, for ACL-injured knees when compared to the contralateral healthy knee and uninjured controls. Additionally, this study provided the first side-to-side symmetry data of medial and lateral FD values in healthy controls.

Predictive Validity of Radiographic Trabecular Bone Texture in Knee Osteoarthritis: The Osteoarthritis Research Society International/Foundation for the National Institutes of Health Osteoarthritis Biomarkers Consortium

OBJECTIVE

To evaluate radiographic subchondral trabecular bone texture (TBT) as a predictor of clinically relevant osteoarthritis (OA) progression (combination of symptom and structural worsening).

METHODS

The Foundation for the National Institutes of Health (FNIH) OA Biomarkers Consortium undertook a study of progressive knee OA cases (n = 194 knees with both radiographic and pain progression over 24-48 months) and comparators (n = 406 OA knees not meeting the case definition). TBT parameters were extracted from a medial subchondral tibial region of interest by fractal signature analysis of radiographs using validated semiautomated software. Baseline TBT and time-integrated values over 12 and 24 months were evaluated for association with case status and separately with radiographic and pain progression status, adjusted for age, sex, body mass index, race, baseline Kellgren/Lawrence grade, baseline joint space width, Western Ontario and McMaster Universities Osteoarthritis Index pain score, and pain medication use. C statistics were generated from receiver operating characteristic curves.

RESULTS

Relative to comparators, cases were characterized by thinner vertical and thicker horizontal trabeculae. The summed composite of 3 TBT parameters at baseline and over 12 and 24 months best predicted case status (odds ratios 1.24-1.43). The C statistic for predicting case status using the TBT composite score (0.633-0.649) was improved modestly but statistically significantly over the use of covariates alone (0.608). One TBT parameter, reflecting thickened horizontal trabeculae in cases, at baseline and over 12 and 24 months, predicted risk of any progression (radiographic and/or pain progression).

CONCLUSION

Although associations are modest, TBT could be an attractive means of enriching OA trials for progressors since it can be generated from screening knee radiographs already standard in knee OA clinical trials.

Association between radiography-based subchondral bone structure and MRI-based cartilage composition in postmenopausal women with mild osteoarthritis

OBJECTIVE

Our aim was to investigate the relation between radiograph-based subchondral bone structure and cartilage composition assessed with delayed gadolinium enhanced magnetic resonance imaging of cartilage (dGEMRIC) and T₂ relaxation time.

DESIGN

Ninety-three postmenopausal women (Kellgren-Lawrence grade 0: n = 13, 1: n = 26, 2: n = 54) were included. Radiograph-based bone structure was assessed using entropy of the Laplacian-based image (E_Lap) and local binary patterns (E_LBP), homogeneity indices of the local angles (HI_Angles,mean, HI_Angles,Perp, HI_Angles,Paral), and horizontal (FD_Hor) and vertical fractal dimensions (FD_Ver). Mean dGEMRIC index and T₂ relaxation time of tibial cartilage were calculated to estimate cartilage composition.

RESULTS

HI_Angles,mean (r_s = -0.22) and HI_Angles,Paral (r_s = -0.24) in medial subchondral bone were related (P < 0.05) to dGEMRIC index of the medial tibial cartilage. E_Lap (r_s = -0.23), FD_Hor,0.34 mm (r = 0.21) and FD_Ver,0.68 mm (r = 0.24) in medial subchondral bone were related (P < 0.05) to T₂ relaxation time values of the medial tibial cartilage. FD_Hor at different scales in lateral subchondral bone were related (P < 0.01) to dGEMRIC index (r = 0.29-0.41) and T₂ values of lateral tibial cartilage (r = -0.28 to -0.36). FD_Ver at larger scales were related (P < 0.05) to dGEMRIC index (r = 0.24-0.25) and T₂ values of lateral tibial cartilage (r = -0.21). HI_Angles,Paral (r = -0.25) and FD_Ver,0.68 mm (r_s = 0.22) in the lateral tibial trabecular bone were related (P < 0.05) to dGEMRIC index of the lateral tibial cartilage.

CONCLUSION

Our results support the presumption that several tissues are affected in the early osteoarthritis (OA). Furthermore, they indicate that the detailed analysis of radiographs may serve as a complementary imaging tool for OA studies.

Subchondral tibial bone texture predicts the incidence of radiographic knee osteoarthritis: data from the Osteoarthritis Initiative

OBJECTIVES

To evaluate whether trabecular bone texture (TBT) parameters measured on computed radiographs (CR) could predict the onset of radiographic knee osteoarthritis (OA).

MATERIALS AND METHODS

Subjects from the Osteoarthritis Initiative (OAI) 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 CR were analyzed using a fractal method to characterize the local variations. The prediction was explored using logistic regression models evaluated by the area under the receiver operating characteristic curves (AUC).

RESULTS

From the 344 knees, 79 (23%) developed radiographic OA after 48 months, 44 (13%) developed progressive JSN and 59 (17%) developed osteophytes. Neither age, gender and BMI, nor their combination predicted poorer KL (AUC 0.57), JSN or TOS (AUC 0.59) scores. The inclusion of the TBT parameters in the models improved the global prediction results for KL (AUC 0.69), JSN (AUC 0.73) and TOS (AUC 0.71) scores.

CONCLUSIONS

Several differences were found between the models predictive of three different outcomes (KL, JSN and TOS), indicating different underlying mechanisms. These results suggest that TBT parameters assessed when radiographic signs are not yet apparent on radiographs may be useful in predicting the onset of radiological tibiofemoral OA as well as identifying at-risk patients for future clinical trials.