Automated Radiographic Measurements of Knee Osteoarthritis.

OBJECTIVE: Herewith, we report the development of Orthopedic Digital Image Analysis (ODIA) software that is developed to obtain quantitative measurements of knee osteoarthritis (OA) radiographs automatically. Manual segmentation and measurement of OA parameters currently hamper large-cohort analyses, and therefore, automated and reproducible methods are a valuable addition in OA research. This study aims to test the automated ODIA measurements and compare them with available manual Knee Imaging Digital Analysis (KIDA) measurements as comparison. DESIGN: This study included data from the CHECK (Cohort Hip and Cohort Knee) initiative, a prospective multicentre cohort study in the Netherlands with 1,002 participants. Knee radiographs obtained at baseline of the CHECK cohort were included and mean medial/lateral joint space width (JSW), minimal JSW, joint line convergence angle (JLCA), eminence heights, and subchondral bone intensities were compared between ODIA and KIDA. RESULTS: Of the potential 2,004 radiographs, 1,743 were included for analyses. Poor intraclass correlation coefficients (ICCs) were reported for the JLCA (0.422) and minimal JSW (0.299). The mean medial and lateral JSW, eminence height, and subchondral bone intensities reported a moderate to good ICC (0.7 or higher). Discrepancies in JLCA and minimal JSW between the 2 methods were mostly a problem in the lateral tibia plateau. CONCLUSIONS: The current ODIA tool provides important measurements of OA parameters in an automated manner from standard radiographs of the knee. Given the automated and computerized methodology that has very high reproducibility, ODIA is suitable for large epidemiological cohorts with various follow-up time points to investigate structural progression, such as CHECK or the Osteoarthritis Initiative (OAI).

The association between statistical shape modeling-defined hip morphology and features of early hip osteoarthritis in young adult football players: Data from the femoroacetabular impingement and hip osteoarthritis cohort (FORCe) study.

Objective: To explore the relationship between radiographic hip shape and features of early hip osteoarthritis (OA) on magnetic resonance imaging (MRI) in young male and female football players without radiographic hip OA. Design: We used baseline data from a cohort of symptomatic and asymptomatic football players aged 18-50 years. Hip shape was assessed on anteroposterior radiographs with statistical shape modeling (SSM) for men and women separately. Cartilage defects and labral tears were graded using the Scoring Hip Osteoarthritis with MRI (SHOMRI) system. We used logistic regression with generalized estimating equations to estimate associations between each hip shape variant, called shape modes, and cartilage defects or labral tears. Results: We included 229 participants (446 hips, 77.4% male). For each sex, 15 shape modes were analyzed. In men, three shape modes were associated with cartilage defects: adjusted odds ratios (aOR) 0.75 (95%CI 0.58-0.97) per standard deviation (SD) for mode 1; 1.34 (95%CI 1.05-1.69) per SD for mode 12; and 0.61 (95%CI 0.48-0.78) per SD for mode 15; and one also with labral tears: aOR 1.30 (95%CI 1.01-1.69) per SD for mode 12. These modes generally represented variations in the femoral neck and subtypes of cam morphology, with and without pincer morphology. For women, there was no evidence for associations with the outcomes. Conclusions: Several hip shape variants were associated with cartilage defects on MRI in young male football players. Specifically, one subtype of cam morphology was associated with both cartilage defects and labral tears. Hip shape was not associated with early OA features in women.

An automated workflow based on hip shape improves personalized risk prediction for hip osteoarthritis in the CHECK study.

OBJECTIVE: To design an automated workflow for hip radiographs focused on joint shape and tests its prognostic value for future hip osteoarthritis. DESIGN: We used baseline and 8-year follow-up data from 1,002 participants of the CHECK-study. The primary outcome was definite radiographic hip osteoarthritis (rHOA) (Kellgren-Lawrence grade ≥2 or joint replacement) at 8-year follow-up. We designed a method to automatically segment the hip joint from radiographs. Subsequently, we applied machine learning algorithms (elastic net with automated parameter optimization) to provide the Shape-Score, a single value describing the risk for future rHOA based solely on joint shape. We built and internally validated prediction models using baseline demographics, physical examination, and radiologists scores and tested the added prognostic value of the Shape-Score using Area-Under-the-Curve (AUC). Missing data was imputed by multiple imputation by chained equations. Only hips with pain in the corresponding leg were included. RESULTS: 84% were female, mean age was 56 (±5.1) years, mean BMI 26.3 (±4.2). Of 1,044 hips with pain at baseline and complete follow-up, 143 showed radiographic osteoarthritis and 42 were replaced. 91.5% of the hips had follow-up data available. The Shape-Score was a significant predictor of rHOA (odds ratio per decimal increase 5.21, 95%-CI (3.74-7.24)). The prediction model using demographics, physical examination, and radiologists scores demonstrated an AUC of 0.795, 95%-CI (0.757-0.834). After addition of the Shape-Score the AUC rose to 0.864, 95%-CI (0.833-0.895). CONCLUSIONS: Our Shape-Score, automatically derived from radiographs using a novel machine learning workflow, may strongly improve risk prediction in hip osteoarthritis.

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.

A sex-specific association between incident radiographic osteoarthritis of hip or knee and incident peripheral arterial calcifications: 8-year prospective data from Cohort Hip and Cohort Knee (CHECK).

OBJECTIVES: There is sparse evidence for a relationship between cardiovascular disease (CVD) and osteoarthritis (OA). We investigated the association between incidence of arterial calcifications and incidence of radiographic knee and/or hip OA. DESIGN: We used baseline and 8-year follow-up data of Cohort Hip and Cohort Knee (CHECK). Knees and hips were either Kellgren-Lawrence (KL) grade 0 or 1 at baseline. Arterial calcifications were scored on hip and knee radiographs using a four-grade scale. Scores were summed for patient-level analyses. To investigate incidence, participants with arterial calcifications at baseline or missing follow-up were excluded. Incident OA was defined per joint as KL ≥ 2 or prosthesis at year eight. The association between incidenct of arterial calcifications and incident OA was studied using mixed-effects logistic regression. RESULTS: Of 763 participants included, 623 (82%) were women. Mean (sd) age was 56 (5.1) years, mean (sd) body mass index (BMI) 26.2 (4.1) kg/m2. Arterial calcifications developed in 174 participants (283 joints). OA developed in 456 participants (778 joints). Sex modified the association between arterial calcification and OA. In women, incident arterial calcification around a joint was positively associated with incident OA in that joint (adjusted OR 2.51 (95% CI 1.57-4.03)). In men, no association was observed on joint-level, but at patient-level the arterial calcification sum score was negatively associated with incident OA (adjusted OR per point increase 0.70 (95% CI 0.54-0.90)) indicating a systemic effect. CONCLUSIONS: We observed sex-dependent associations between incident arterial calcification and incident radiographic knee and/or hip OA, which differs between joint- and patient-level.