Machine-learning predicted and actual 2-year structural progression in the IMI-APPROACH cohort

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 (2023)

Publication Type: Journal article

Publication year: 2023

Journal

Quantitative Imaging in Medicine and Surgery AME Publ.

DOI: 10.21037/qims-22-949

Abstract

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 follow-up. 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.

Involved external institutions

Leiden University NL Netherlands (NL) University Medical Centre Utrecht (UMC Utrecht) NL Netherlands (NL) Boston University US United States (USA) (US) Paracelsus Medizinische Privatuniversität AT Austria (AT) University of A Coruña / Universidade da Coruña ES Spain (ES) Diakonhjemmet Hospital NO Norway (NO) University of Paris 4 - Paris-Sorbonne / Université paris IV Paris-Sorbonne FR France (FR) Newcastle University GB United Kingdom (GB)

How to cite

APA:

Jansen, M.P., Wirth, W., Bacardit, J., Van Helvoort, E.M., Marijnissen, A.C.A., Kloppenburg, M.,... Roemer, F.W. (2023). Machine-learning predicted and actual 2-year structural progression in the IMI-APPROACH cohort. Quantitative Imaging in Medicine and Surgery. https://doi.org/10.21037/qims-22-949

MLA:

Jansen, Mylene P., et al. "Machine-learning predicted and actual 2-year structural progression in the IMI-APPROACH cohort." Quantitative Imaging in Medicine and Surgery (2023).

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