Characterizing Osteophyte Formation in Knee Osteoarthritis: Application of Machine Learning Quantification of a Computerized Tomography Cohort: Implications for Treatment

Graham Vincent; Robert Marchand; Michael A Mont; Benjamin Harder; Hytham S Salem; Philip G Conaghan; Alan D Brett; Michael A Bowes

doi:10.1016/j.arth.2024.04.083

Characterizing Osteophyte Formation in Knee Osteoarthritis: Application of Machine Learning Quantification of a Computerized Tomography Cohort: Implications for Treatment

J Arthroplasty. 2024 May 7:S0883-5403(24)00431-5. doi: 10.1016/j.arth.2024.04.083. Online ahead of print.

Authors

Graham Vincent¹, Robert Marchand², Michael A Mont³, Benjamin Harder¹, Hytham S Salem³, Philip G Conaghan⁴, Alan D Brett¹, Michael A Bowes¹

Affiliations

¹ Stryker Orthopaedics, Mahwah, New Jersey.
² South County Orthopedics, Wakefield, Rhode Island.
³ The Rubin Institute for Advanced Orthopedics, Sinai Hospital of Baltimore, Baltimore, Maryland.
⁴ Leeds Institute of Rheumatic and Musculoskeletal Medicine, University of Leeds, and National Institute for Health and Care Research, Leeds Biomedical Research Centre, Leeds, United Kingdom.

PMID: 38723700
DOI: 10.1016/j.arth.2024.04.083

Abstract

Background: Osteophytes are commonly used to diagnose and guide knee osteoarthritis (OA) treatment, but their causes are unclear. Although they are not typically the focus of knee arthroplasty surgeons, they can predict case difficulty and length. Furthermore, their extent and location may yield much information about the knee joint status. The aims of this computed tomography-based study in patients awaiting total or partial knee arthroplasty were to: (1) measure osteophyte volume in anatomical subregions and relative change as total volume increases; (2) determine whether medial and/or lateral OA affects osteophyte distribution; and (3) explore relationships between osteophytes and OA severity.

Methods: Data were obtained from 4,928 computed tomography scans. Machine-learning-based imaging analyses enabled osteophyte segmentation and quantification, divided into anatomical regions. Mean three-dimensional joint space narrowing was assessed in medial and lateral compartments. A Bayesian model assessed the uniformity of osteophyte distribution. We correlated femoral osteophyte volumes with B-scores, a validated OA status measure.

Results: Total tibial (25%) and femoral osteophyte volumes (75%) within each knee correlated strongly (R² = 0.85). Medial osteophytes (65.3%) were larger than lateral osteophytes (34.6%), with similar proportions in both the femur and tibia. Osteophyte growth was found in all compartments, and as total osteophyte volume increased, the relative distribution of osteophytes between compartments did not markedly change. No evidence of variation was found in the regional distribution of osteophyte volume between knees with medial, lateral, both, or no three-dimensional joint space narrowing in the femur or tibia. There was a direct relationship between osteophyte volume and OA severity.

Conclusions: Osteophyte volume increased in both medial and lateral compartments proportionally with total osteophyte volume, regardless of OA location. The peripheral position of femoral osteophytes does not appear to contribute to load-bearing. This suggests that osteophytic growth represents a 'whole-knee'/global response. This work may have broad applications for knee OA, both surgically and nonoperatively.

Keywords: 3-D evaluation; cartilage; knee arthroplasty; machine learning; osteoarthritis; osteophyte.