Differences in tibial subchondral bone structure evaluated using plain radiographs between knees with and without cartilage damage or bone marrow lesions - the Oulu Knee Osteoarthritis study

Weaker subchondral bone and thinner articular cartilage of the knee are associated with elevated baseline fracture risk independently of osteoarthritis risk factors

Knee osteoarthritis involves damaged bones beneath joint surfaces but how current bone health predicts future disease state is unclear. We discovered higher osteoporotic fracture risk (FRAX) was linked to compromised knee bone quality and thinner cartilage. Integrating FRAX into osteoarthritis care could identify who may develop worse disease outcomes.

OBJECTIVES

To examine how well fracture risk predicts subchondral bone and cartilage morphometry independently of osteoarthritis (OA) clinical risk factors.

METHODS

Male and female participants in the Osteoarthritis Initiative (OAI) at visit 5 (36 months) were evaluated for fracture risk using FRAX and categorized into low-, moderate-, or high-risk groups. These groups were compared for bone marrow lesion (BML) size, number and effusion, subchondral bone structure, density, and cartilage morphometry in the most affected knee at either same time point, 1 or 2 years later, using general linear models. Sex interactions were examined in each case and probed if significant.

RESULTS

Among 1240 participants (58.8% female, age: 63.7 ± 8.8 years, and BMI: 30.1 ± 4.9 kg/m2), 20.32% had moderate or high FRAX and showed lower subchondral bone density (- 0.12 to - 0.25 g/cm2), less intact trabeculae, and thinner cartilage (- 0.14 to - 0.47 mm) compared to low FRAX (p < 0.05). Males showed larger positive FRAX correlations with bone density, and females had protective effects of FRAX against BML numbers, although the effects were small (sex interaction, p < 0.05). All FRAX models adjusting for OA risk factors yielded better model fit than OA risk factors alone. Having moderate/high versus low FRAX at baseline predicted a 1.36 (1.00, 1.86)-fold higher odds of reaching a Kellgren-Lawrence score of 3 or 4 within a year.

CONCLUSIONS

High FRAX predicts thinner cartilage and weaker subchondral bone within a year. Complementing standard OA clinical risk factors with FRAX calculation could help identify individuals likely to develop worse knee OA radiologic outcomes.

Individuals with Knee Osteoarthritis and Osteoporosis Represent a Distinctive Subgroup Whose Symptoms Originate from Differences in Subchondral Bone Rather than Cartilage

To explore the hypothesis that knee osteoarthritis patients with osteoporosis represent a sub-cohort with different disease characteristics and origin of symptoms. Men and women in the Osteoarthritis Initiative (OAI) at visit 5 (36 months) were examined for osteoporosis (N = 1483) using DXA (T-score at femoral neck ≤ -2.5), use of bisphosphonates, or having experienced a fracture. Those with and without osteoporosis were compared by subchondral bone quality, bone marrow lesion (BML) properties, and cartilage thickness from MRI, with general linear modeling. Relationships between symptoms (12 months later) and each of cartilage or subchondral bone features were examined conditional on osteoporosis status. Overall, 15.2% of 1246 participants (825 women, 658 men, mean age: 64.4 ± 8.9yrs, BMI: 30.1 ± 4.9 kg/m2) with knee OA likely had osteoporosis and showed lower medial and lateral subchondral bone density, smaller trabecular number and larger trabecular separation (all p < 0.01) compared to those without. Cartilage thickness appeared lower in this group (p = 0.04) but only by a small amount. Knee symptoms correlated with both BML properties and cartilage thickness; the latter but not the former being moderated by osteoporosis status. Those with osteoporosis showed no relationship between cartilage and knee symptoms, but demonstrated bone-related associations with symptoms. Osteoporosis affects the pattern of subchondral bone and cartilage properties in individuals with knee osteoarthritis. Knee symptoms in this subgroup likely originates in bone instead of cartilage. Osteoporosis screening may help identify knee osteoarthritis patients at further risk of subchondral bone damage leading to pain.

Functional scores and prosthetic implant placement are different for navigated medial UKA left in varus alignment

PURPOSE

The purpose of this study was to analyze the clinical outcomes and radiologic position of the knee in two groups of patients after medial unicompartmental knee arthroplasty (UKA): one group with residual varus axis (RVA) alignment and other one with neutral mechanical axis (NMA) of the lower limb.

METHODS

All patients who underwent UKA between January 2015 and January 2018 were evaluated retrospectively. Inclusion criteria were: medial UKA for isolated medial femoro-tibial osteoarthritis, a varus deformity of < 15°, and a minimal follow-up of 2 years. All patients had a preoperative and postoperative clinical examination with functional scores (New International Knee Score (NewIKS) and Knee injury and Osteoarthritis Outcome Score (KOOS) and radiographs. Preoperative and postoperative values for continuous outcomes were compared using the Student's t test for paired data and differences between the groups were compared with the Mann-Whitney U test. p < 0.05 was considered statistically significant.

RESULTS

The RVA group consisted of 48 cases of medial UKA in 48 patients (22 females). Mean postoperative hip-knee-ankle (HKA) angle was 174.3° ± 2.8 and the corresponding mean AKI angle (tibial mechanical angle) was 82.9° ± 2.9. The NMA group consisted of 35 cases of medial UKA in 35 patients (14 females). Mean postoperative HKA angle was 178.9° ± 3 and the corresponding mean AKI angle was 85.5° ± 3.1. A significant difference was found between the two groups for the KOOS score and for global NewIKS, with a better score in the RVA group.

CONCLUSIONS

RVA alignment after medial UKA results in a significant improvement in functional knee scores at 2-year post-surgery. Return to sport and recreational activities was better than in patients with postoperative NMA.

LEVEL OF EVIDENCE

Level 3; retrospective cohort study.

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.

Machine learning based texture analysis of patella from X-rays for detecting patellofemoral osteoarthritis

OBJECTIVE

To assess the ability of texture features for detecting radiographic patellofemoral osteoarthritis (PFOA) from knee lateral view radiographs.

DESIGN

We used lateral view knee radiographs from The Multicenter Osteoarthritis Study (MOST) public use datasets (n  = 5507 knees). Patellar region-of-interest (ROI) was automatically detected using landmark detection tool (BoneFinder), and subsequently, these anatomical landmarks were used to extract three different texture ROIs. Hand-crafted features, based on Local Binary Patterns (LBP), were then extracted to describe the patellar texture. First, a machine learning model (Gradient Boosting Machine) was trained to detect radiographic PFOA from the LBP features. Furthermore, we used end-to-end trained deep convolutional neural networks (CNNs) directly on the texture patches for detecting the PFOA. The proposed classification models were eventually compared with more conventional reference models that use clinical assessments and participant characteristics such as age, sex, body mass index (BMI), the total Western Ontario and McMaster Universities Arthritis Index (WOMAC) score, and tibiofemoral Kellgren-Lawrence (KL) grade. Atlas-guided visual assessment of PFOA status by expert readers provided in the MOST public use datasets was used as a classification outcome for the models. Performance of prediction models was assessed using the area under the receiver operating characteristic curve (ROC AUC), the area under the precision-recall (PR) curve -average precision (AP)-, and Brier score in the stratified 5-fold cross validation setting.

RESULTS

Of the 5507 knees, 953 (17.3%) had PFOA. AUC and AP for the strongest reference model including age, sex, BMI, WOMAC score, and tibiofemoral KL grade to predict PFOA were 0.817 and 0.487, respectively. Textural ROI classification using CNN significantly improved the prediction performance (ROC AUC = 0.889, AP = 0.714).

CONCLUSION

We present the first study that analyses patellar bone texture for diagnosing PFOA. Our results demonstrates the potential of using texture features of patella to predict PFOA.

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.

Principal Component Analysis of Simultaneous PET-MRI Reveals Patterns of Bone-Cartilage Interactions in Osteoarthritis

BACKGROUND

Bone-cartilage interactions have been implicated in causing osteoarthritis (OA).

PURPOSE

To use [18 F]-NaF PET-MRI to 1) develop automatic image processing code in MatLab to create a model of bone-cartilage interactions and 2) find associations of bone-cartilage interactions with known manifestations of OA.

STUDY TYPE

Prospective study aimed to evaluate a data analysis method.

POPULATION

Twenty-nine patients with knee pain or joint stiffness.

FIELD STRENGTH/SEQUENCE

3T MRI (GE), 3D CUBE FSE, 3D combined T1 ρ/T2 MAPSS, [18F]-sodium fluoride, SIGNA TOF (OSEM).

ASSESSMENT

Correlation between MRI (cartilage) and PET (bone) quantitative parameters, bone-cartilage interactions model described by modes of variation as derived by principal component analysis (PCA), WORMS scoring on cartilage lesions, bone marrow abnormalities, subchondral cysts.

STATISTICAL TESTS

Linear regression, Pearson correlation.

RESULTS

Mode 1 was a positive predictor of the bone abnormality score (P = 0.0003, P = 0.001, P = 0.0007) and the cartilage lesion score (P = 0.03, P = 0.01, P = 0.02) in the femur, tibia, and patella, respectively. For the cartilage lesion scores, mode 5 was the most important positive predictor in the femur (P = 3.9E-06), and mode 2 were predictors, significant negative predictor in the tibia (P = 0.007). In the patella, mode 1 was a significant positive predictor of the bone abnormality score (P = 0.0007).

DATA CONCLUSION

By successfully building an automatic code to create a bone-cartilage interface, we were able to observe dynamic relationships between biochemical changes in the cartilage accompanied with bone remodeling, extended to the whole knee joint instead of simple colocalized observations, shedding light on the interactions that occur between bone and cartilage in OA. Evidence Level: 3 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2020;52:1462-1474.

Adaptive segmentation of knee radiographs for selecting the optimal ROI in texture analysis

OBJECTIVE

The purposes of this study were to investigate: 1) the effect of placement of region-of-interest (ROI) for texture analysis of subchondral bone in knee radiographs, and 2) the ability of several texture descriptors to distinguish between the knees with and without radiographic osteoarthritis (OA).

DESIGN

Bilateral posterior-anterior knee radiographs were analyzed from the baseline of Osteoarthritis Initiative (OAI) (9012 knee radiographs) and Multicenter Osteoarthritis Study (MOST) (3,644 knee radiographs) datasets. A fully automatic method to locate the most informative region from subchondral bone using adaptive segmentation was developed. Subsequently, we built logistic regression models to identify and compare the performances of several texture descriptors and each ROI placement method using 5-fold cross validation. Importantly, we also investigated the generalizability of our approach by training the models on OAI and testing them on MOST dataset. We used area under the receiver operating characteristic curve (ROC AUC) and average precision (AP) obtained from the precision-recall (PR) curve to compare the results.

RESULTS

We found that the adaptive ROI improves the classification performance (OA vs non-OA) over the commonly-used standard ROI (up to 9% percent increase in AUC). We also observed that, from all texture parameters, Local Binary Pattern (LBP) yielded the best performance in all settings with the best AUC of 0.840 [0.825, 0.852] and associated AP of 0.804 [0.786, 0.820].

CONCLUSION

Compared to the current state-of-the-art approaches, our results suggest that the proposed adaptive ROI approach in texture analysis of subchondral bone can increase the diagnostic performance for detecting the presence of radiographic OA.

Quantifying Subresolution 3D Morphology of Bone with Clinical Computed Tomography

The aim of this study was to quantify sub-resolution trabecular bone morphometrics, which are also related to osteoarthritis (OA), from clinical resolution cone beam computed tomography (CBCT). Samples (n = 53) were harvested from human tibiae (N = 4) and femora (N = 7). Grey-level co-occurrence matrix (GLCM) texture and histogram-based parameters were calculated from CBCT imaged trabecular bone data, and compared with the morphometric parameters quantified from micro-computed tomography. As a reference for OA severity, histological sections were subjected to OARSI histopathological grading. GLCM and histogram parameters were correlated to bone morphometrics and OARSI individually. Furthermore, a statistical model of combined GLCM/histogram parameters was generated to estimate the bone morphometrics. Several individual histogram and GLCM parameters had strong associations with various bone morphometrics (|r| > 0.7). The most prominent correlation was observed between the histogram mean and bone volume fraction (r = 0.907). The statistical model combining GLCM and histogram-parameters resulted in even better association with bone volume fraction determined from CBCT data (adjusted R² change = 0.047). Histopathology showed mainly moderate associations with bone morphometrics (|r| > 0.4). In conclusion, we demonstrated that GLCM- and histogram-based parameters from CBCT imaged trabecular bone (ex vivo) are associated with sub-resolution morphometrics. Our results suggest that sub-resolution morphometrics can be estimated from clinical CBCT images, associations becoming even stronger when combining histogram and GLCM-based parameters.

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.

Osteoarthritis year in review 2018: imaging

PURPOSE

To provide a narrative review of the most relevant original research published in 2017/2018 on osteoarthritis imaging.

METHODS

The PubMed database was used to recover all relevant articles pertaining to osteoarthritis and medical imaging published between 1 April 2017 and 31 March 2018. Review articles, case studies and in vitro or animal studies were excluded. The original publications were subjectively sorted based on relevance, novelty and impact.

RESULTS AND CONCLUSIONS

The publication search yielded 1,155 references. In the assessed publications, the most common imaging modalities were radiography (N = 708) and magnetic resonance imaging (MRI) (355), followed by computed tomography (CT) (220), ultrasound (85) and nuclear medicine (17). An overview of the most important publications to the osteoarthritis (OA) research community is presented in this narrative review. Imaging studies play an increasingly important role in OA research, and have helped us to understand better the pathophysiology of OA. Radiography and MRI continue to be the most applied imaging modalities, while quantitative MRI methods and texture analysis are becoming more popular. The value of ultrasound in OA research has been demonstrated. Several multi-modality predictive models have been developed. Deep learning has potential for more automatic and standardized analyses in future OA imaging research.

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.

Kinematic alignment technique for medial OXFORD UKA: An in-silico study

BACKGROUND

Mobile bearing unicompartmental knee arthroplasty (UKA) Oxford™ components are recommended to be systematically and mechanically aligned (MA) for restoring the constitutional lower-limb alignment. Good long-term clinical outcomes have been generated with the medially implanted MA Oxford™, but some sub-optimal biomechanical-related complications still remain. Kinematic Alignment (KA) is a personalised technique for anatomically and kinematically implanting components (total knee, fixed bearing partial knee, total hip) aimed at creating more physiological prosthetic joint biomechanics. Interestingly, for decades the principles for implanting fixed bearing UKA components were consistent with those promoted by the KA technique, but differently formulated. We initiated this computational study to assess the feasibility of this technique with the Oxford™ components, as we thought this more anatomical implantation may be clinically advantageous.

HYPOTHESIS

We surmised that kinematically aligning the Oxford™ medial UKA would maximise the prosthesis-bone interface through maximising the implants' size used (question 1), and alter, within an acceptable limit, the components' orientation (question 2) compared to conventional mechanical alignment.

METHODS

A cohort of 40 consecutive medial osteoarthritic knee patients scheduled for UKA had a preoperative CT scan that was segmented to create 3D knee bone models. MA and KA of medial UKA Oxford^® components (Zimmer-Biomet, Warsaw, Indiana, USA) were simulated. Component sizing and positioning were compared between the two techniques.

RESULTS

We found no difference in component size, but significantly fewer occurrences of borderline fit with the KA simulation. KA technique oriented the femoral component 3.6° more valgus (from 1° varus to 7° valgus) and the tibial component 2.9° more varus (from 8° varus to 0°) compared to the MA technique. The tibial component slope in KA simulation was 6.4° posterior (from 0 to 12°) compared to a systematic 7° posterior for MA positioning.

DISCUSSION AND CONCLUSION

Kinematic alignment of the medial Oxford™ generated a different, albeit still acceptable (Oxford group recommendations), implant orientation, in addition to a likely better shape-fit between components and the supportive bone cut, compared to the MA technique. The potential to improve the implants' interaction and to restore a more physiological bone loading makes the KA of Oxford™ an attractive, potentially clinically beneficial option. Clinical investigations are needed to assess its true value.

LEVEL OF EVIDENCE

I, computational study.

Comparison of bone texture between normal individuals and patients with Kashin-Beck disease from plain radiographs in knee

To compare tibial bone texture between Kashin-Beck disease (KBD) patients and normal individuals from plain radiographs using an advanced image analysis. Plain knee radiographs were obtained from KBD patients (n = 49) and age-matched healthy controls (n = 98). KBD were graded with diagnostic criteria WS/T 207-2010. The textural values related to bone structure from medial and lateral tibial subchondral and trabecular bones were evaluated using entropy of Laplacian-based image (E_Lap), entropy of local binary patterns (E_LBP), homogeneity indices (HI) of local angles (HI_Mean, HI_Perp and HI_Paral), and fractal dimensions from horizontal (FD_Hor) and vertical (FD_Ver) structures. KBD patients were shorter in height and lighter in weight, and their tibial width was wider than controls. Anatomical angle of KBD patients showed more genu valgus. Total KBD patients and subgroups had higher E_Lap, HI_Mean, HI_Perp and HI_Paral in detected tibial subchondral and trabecular bones than controls, except E_Lap in lateral subchondral bone. E_LBP, FD_Hor and FD_Ver from the detected tibial bone in KBD patients and subgroups were lower than controls, except FD_Ver in lateral trabecular bone. Our results indicate that micro-scale in bone texture in KBD-affected knees can be quantitatively examined from plain radiographs using an advanced image analysis.

Association of subchondral bone texture on magnetic resonance imaging with radiographic knee osteoarthritis progression: data from the Osteoarthritis Initiative Bone Ancillary Study

Objectives

To assess whether initial or 12–18-month change in magnetic resonance imaging (MRI) subchondral bone texture is predictive of radiographic knee osteoarthritis (OA) progression over 36 months.

Methods

This was a nested case-control study including 122 knees/122 participants in the Osteoarthritis Initiative (OAI) Bone Ancillary Study, who underwent MRI optimised for subchondral bone assessment at either the 30- or 36-month and 48-month OAI visits. Case knees (n = 61) had radiographic OA progression between the 36- and 72-month OAI visits, defined as ≥ 0.7 mm minimum medial tibiofemoral radiographic joint space (minJSW) loss. Control knees (n = 61) without radiographic OA progression were matched (1:1) to cases for age, sex, body mass index and initial medial minJSW. Texture analysis was performed on the medial femoral and tibial subchondral bone. We assessed the association of texture features with radiographic progression by creating a composite texture score using penalised logistic regression and calculating odds ratios. We evaluated the predictive performance of texture features for predicting radiographic progression using c-statistics.

Results

Initial (odds ratio [95% confidence interval] = 2.13 [1.41–3.40]) and 12– 18-month change (3.76 [2.04–7.82]) texture scores were significantly associated with radiographic OA progression. Combinations of texture features were significant predictors of radiographic progression using initial (c-statistic [95% confidence interval] = 0.65 [0.64–0.65], p = 0.003) and 12–18-month change (0.68 [0.68-0.68], p < 0.001) data.

Conclusions

Initial and 12–18-month changes in MRI subchondral bone texture score were significantly associated with radiographic progression at 36 months, with better predictive performance for 12–18-month change in texture. These results suggest that texture analysis may be a useful biomarker of subchondral bone in OA.

Key Points

• Subchondral bone MRI texture analysis is a promising knee osteoarthritis imaging biomarker.

• In this study, subchondral bone texture was associated with knee osteoarthritis progression.

• This demonstrates predictive and concurrent validity of MRI subchondral bone texture analysis.

• This method may be useful in clinical trials with interventions targeting bone.

Electronic supplementary material

The online version of this article (10.1007/s00330-018-5444-9) contains supplementary material, which is available to authorized users.