Osteophyte size and location on hip DXA scans are associated with hip pain: Findings from a cross sectional study in UK Biobank

Abstract

Objective It remains unclear how the different features of radiographic hip osteoarthritis (rHOA) contribute to hip pain. We examined the relationship between rHOA, including its individual components, and hip pain using a novel dual-energy x-ray absorptiometry (DXA)-based method. Methods Hip DXAs were obtained from UK Biobank. A novel automated method obtained minimum joint space width (mJSW) from points placed around the femoral head and acetabulum. Osteophyte areas at the lateral acetabulum, superior and inferior femoral head were derived manually. Semi-quantitative measures of osteophytes and joint space narrowing (JSN) were combined to define rHOA. Logistic regression was used to examine the relationships between these variables and hip pain, obtained via questionnaires. Results 6807 hip DXAs were examined. rHOA was present in 353 (5.2%) individuals and was associated with hip pain [OR 2.42 (1.78–3.29)] and hospital diagnosed OA [6.01 (2.98–12.16)]. Total osteophyte area but not mJSW was associated with hip pain in mutually adjusted models [1.31 (1.23–1.39), 0.95 (0.87–1.04) respectively]. On the other hand, JSN as a categorical variable showed weak associations between grade ≥ 1 and grade ≥ 2 JSN with hip pain [1.30 (1.06–1.60), 1.80 (1.34–2.42) respectively]. Acetabular, superior and inferior femoral osteophyte areas were all independently associated with hip pain [1.13 (1.06–1.20), 1.13 (1.05–1.24), 1.10 (1.03–1.17) respectively]. Conclusion In this cohort, the relationship between rHOA and prevalent hip pain was explained by 2-dimensional osteophyte area, but not by the apparent mJSW. Osteophytes at different locations showed important, potentially independent, associations with hip pain, possibly reflecting the contribution of distinct biomechanical pathways.

A novel semi-automated classifier of hip osteoarthritis on DXA images shows expected relationships with clinical outcomes in UK Biobank

“…A machine learning Random Forest-based algorithm, which was initially trained on ∼7000 manually marked-up images, automatically placed 85 outline points around the left femoral head and acetabulum [ 11 , 20 , 21 ] ( Fig. 1 ).…”

“…This likely reflects not only ambiguity and subjectivity of scoring, but also limitations in how these scores are derived. For example, whereas KL and Croft grading both give equal weighting to joint space narrowing (JSN) and osteophytes, yet where these have been examined individually, osteophyte severity shows a stronger association with hip pain than does JSN [ 10 , 11 ]. On top of this, when examined in isolation in a large systematic review, minimum joint space width (mJSW), a continuous measure of JSN, showed weak associations with hip symptoms questioning its predominance in these scoring systems [ 12 ].…”

“…Examining hip images in tens of thousands of individuals requires methods that are scalable and ideally automated [ 17 ], some of which are now available. Automated calculation of mJSW and digital quantification of osteophyte area are examples of such methods developed on DXAs [ 11 ].…”

Deriving alpha angle from anterior-posterior dual-energy x-ray absorptiometry scans: an automated and validated approach

“…It was this wider research programme that guided the sample size selection as large samples are needed for machine learning. The remainder of the training and extension samples (80%) were selected randomly ensuring the sexes were equally weighted 19 . The combined sample is made up of both the training and extension sample.…”

“…Dual-energy x-ray absorptiometry (DXA) scans are commonly used to derive measures of bone mineral density, and are increasingly being used to look at hip shape 16 , 17 . In addition, a new generation of hip DXA scanner allows for higher resolution images which has led to the use of DXA scans in detecting radiographic OA for research purposes 18 , 19 . Compared to radiographs, DXA scans involve lower radiation doses and are available from larger population studies, such as the UK Biobank (UKB) enhanced imaging study 20 , 21 .…”