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Lookup NU author(s): Professor Jaume Bacardit
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND).
© Quantitative Imaging in Medicine and Surgery. All rights reserved.In the Innovative Medicine’s Initiative Applied Public-Private Research enabling OsteoArthritis Clinical Headway (IMI-APPROACH) knee osteoarthritis (OA) study, machine learning models were trained to predict the probability of structural progression (s-score), predefined as >0.3 mm/year joint space width (JSW) decrease and used as inclusion criterion. The current objective was to evaluate predicted and observed structural progression over 2 years according to different radiographic and magnetic resonance imaging (MRI)-based structural parameters. Radiographs and MRI scans were acquired at baseline and 2-year followup. Radiographic (JSW, subchondral bone density, osteophytes), MRI quantitative (cartilage thickness), and MRI semiquantitative [SQ; cartilage damage, bone marrow lesions (BMLs), osteophytes] measurements were obtained. The number of progressors was calculated based on a change exceeding the smallest detectable change (SDC) for quantitative measures or a full SQ-score increase in any feature. Prediction of structural progression based on baseline s-scores and Kellgren-Lawrence (KL) grades was analyzed using logistic regression. Among 237 participants, around 1 in 6 participants was a structural progressor based on the predefined JSW-threshold. The highest progression rate was seen for radiographic bone density (39%), MRI cartilage thickness (38%), and radiographic osteophyte size (35%). Baseline s-scores could only predict JSW progression parameters (most P>0.05), while KL grades could predict progression of most MRI-based and radiographic parameters (P<0.05). In conclusion, between 1/6 and 1/3 of participants showed structural progression during 2-year follow-up. KL scores were observed to outperform the machine-learning-based s-scores as progression predictor. The large amount of data collected, and the wide range of disease stage, can be used for further development of more sensitive and successful (whole joint) prediction models. Trial Registration: Clinicaltrials.gov number NCT03883568.
Author(s): Jansen MP, Wirth W, Bacardit J, van Helvoort EM, Marijnissen ACA, Kloppenburg M, Blanco FJ, Haugen IK, Berenbaum F, Ladel CH, Loef M, Lafeber FPJG, Welsing PM, Mastbergen SC, Roemer FW
Publication type: Article
Publication status: Published
Journal: Quantitative Imaging in Medicine and Surgery
Year: 2023
Volume: 13
Issue: 5
Pages: 3298-3306
Print publication date: 01/05/2023
Online publication date: 10/03/2023
Acceptance date: 30/12/2022
Date deposited: 28/09/2023
ISSN (print): 2223-4292
ISSN (electronic): 2223-4306
Publisher: AME Publishing Company
URL: https://doi.org/10.21037/qims-22-949
DOI: 10.21037/qims-22-949
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