University of Oulu

J. Hirvasniemi, W.P. Gielis, S. Arbabi, R. Agricola, W.E. van Spil, V. Arbabi, H. Weinans, Bone texture analysis for prediction of incident radiographic hip osteoarthritis using machine learning: data from the Cohort Hip and Cohort Knee (CHECK) study, Osteoarthritis and Cartilage, Volume 27, Issue 6, 2019, Pages 906-914, ISSN 1063-4584, https://doi.org/10.1016/j.joca.2019.02.796

Bone texture analysis for prediction of incident radiographic hip osteoarthritis using machine learning : data from the Cohort Hip and Cohort Knee (CHECK) study

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Author: Hirvasniemi, J.1,2; Gielis, W.P.2; Arbabi, S.3;
Organizations: 1Center for Machine Vision and Signal Analysis, Faculty of Information Technology and Electrical Engineering, University of Oulu, Oulu, Finland
2Department of Orthopedics, University Medical Center Utrecht, Utrecht, the Netherlands
3Department of Computer Engineering, Faculty of Engineering, University of Zabol, Zabol, Iran
4Department of Orthopaedics, Erasmus University Medical Center, Rotterdam, the Netherlands
5Department of Rheumatology & Clinical Immunology, University Medical Center Utrecht, Utrecht, the Netherlands
6Department of Biomechanical Engineering, Delft University of Technology, Delft, the Netherlands
7Department of Mechanical Engineering, Faculty of Engineering, University of Birjand, Birjand, Iran
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 4.5 MB)
Persistent link: http://urn.fi/urn:nbn:fi-fe2019070322648
Language: English
Published: Elsevier, 2019
Publish Date: 2020-06-30
Description:

Abstract

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.

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Series: Osteoarthritis and cartilage
ISSN: 1063-4584
ISSN-E: 1522-9653
ISSN-L: 1063-4584
Volume: 27
Issue: 6
Pages: 906 - 914
DOI: 10.1016/j.joca.2019.02.796
OADOI: https://oadoi.org/10.1016/j.joca.2019.02.796
Type of Publication: A1 Journal article – refereed
Field of Science: 3126 Surgery, anesthesiology, intensive care, radiology
Subjects:
Funding: The CHECK-cohort study is funded by the Dutch Arthritis Foundation.
Copyright information: © 2019 Osteoarthritis Research Society International. Published by Elsevier Ltd. All rights reserved. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/.
  https://creativecommons.org/licenses/by-nc-nd/4.0/