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Our aim was to assess the ability of radiography-based bone textureparameters in proximal femur and acetabulum to predict incident radiographichip osteoarthritis (rHOA) over a 10 years period. Pelvic radiographs from CHECK(Cohort Hip and Cohort Knee) at baseline (987 hips) were analyzed for bonetexture 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) > 1 or total hip replacement (THR)), joint spacenarrowing score (JSN, range 0-3), and osteophyte score (OST, range 0-3) after10 years. Performance of prediction models was assessed using the area underthe receiver operating characteristic curve (ROC AUC). Of the 987 hips withoutrHOA at baseline, 435 (44%) had rHOA at 10-year follow-up. Of the 667 hips withJSN grade 0 at baseline, 471 (71%) had JSN grade > 0 at 10-year follow-up. Ofthe 613 hips with OST grade 0 at baseline, 526 (86%) had OST grade > 0 at10-year follow-up. AUCs for the models including age, gender, and body massindex 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 improvedthe prediction of incident rHOA (ROC AUC 0.66 and 0.71 when baseline KL wasalso included in the model) and JSN (ROC AUC 0.62), but not incident OST (ROCAUC 0.53). Bone texture analysis provides additional information for predictingincident rHOA or THR over 10 years.

Bone Texture Analysis for Prediction of Incident Radio-graphic Hip  Osteoarthritis Using Machine Learning: Data from the Cohort Hip and Cohort  Knee (CHECK) study