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

Q: What was the IMI-APPROACH structural progression outcome and how was it defined?

Structural progression was predicted and evaluated using an s-score defined by a >0.3 mm/year decrease in joint space width (JSW). Progressors were also identified using smallest detectable change (SDC) thresholds for quantitative metrics or a full SQ-score increase for semiquantitative features.

Aug 5, 2026

Upload by Eliana

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), defined as >0.3 mm/year joint space width (JSW) decrease, and applied as an inclusion criterion. Predicted and observed progression over 2 years were evaluated across radiographic and MRI structural parameters. Radiographs and MRI were collected at baseline and 2-year follow-up, and progressors were determined using SDC thresholds for quantitative measures or full SQ-score increases. Among 237 participants, approximately 1 in 6 met the predefined JSW-based progression criterion, with highest rates for bone density, cartilage thickness, and osteophyte size. Overall, KL grades outperformed s-scores as predictors, supporting use of the large dataset for improved whole-joint prediction models.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages12

Reading Time30 min

AccessFree to download

Introduction

Definition and rationale for predicting OA progression

Methods

Machine-learning prediction (s-score) and inclusion criterion

Imaging acquisition and structural parameter measurement

Progressor determination

Statistical analysis for prediction

Results

Progressor frequency and highest progression rates

Predictive performance of s-scores and Kellgren-Lawrence grades

Conclusion

Comparison of predictors and implications for future modeling

Machine-learning predicted and actual 2-year structural progression in the IMI-APPROACH cohort Mylène P. Jansen1^, Wolfgang Wirth2,3,4, Jaume Bacardit5, Eefje M. van Helvoort1, Anne C. A. Marijnissen1, Margreet Kloppenburg6,7, Francisco J. Blanco8, Ida K. Haugen9, Francis Berenbaum10,11, Cristoph H. Ladel12, Marieke Loef6,7, Floris P. J. G. Lafeber1, Paco M. Welsing1, Simon C. Mastbergen1, Frank W. Roemer13,14 1Department of Rheumatology & Clinical Immunology, University Medical Center Utrecht, Utrecht, The Netherlands; 2Department of Imaging & Functional Musculoskeletal Research, Institute of Anatomy & Cell Biology, Paracelsus Medical University Salzburg & Nuremberg, Salzburg, Austria; 3Ludwig Boltzmann Inst. for Arthritis and Rehabilitation, Paracelsus Medical University Salzburg & Nuremberg, Salzburg, Austria; 4Chondrometrics GmbH, Freilassing, Germany; 5School of Computing, Newcastle University, Newcastle, UK; 6Department of Rheumatology, Leiden University Medical Center, Leiden, The Netherlands; 7Clinical Epidemiology, Leiden University Medical Center, Leiden, The Netherlands; 8Grupo de Investigación de Reumatología (GIR), INIBIC-Complejo Hospitalario Universitario de A Coruña, SERGAS, Centro de Investigación CICA, Departamento de Fisioterapia y Medicina, Universidad de A Coruña, A Coruña, SpainServicio de Reumatologia, INIBIC-Universidade de A Coruña, A Coruña, Spain; 9Center for treatment of Rheumatic and Musculoskeletal Diseases (REMEDY), Diakonhjemmet Hospital, Oslo, Norway; 10Department of Rheumatology, AP-HP Saint-Antoine Hospital, Paris, France; 11INSERM, Sorbonne University, Paris, France; 12Independent Consultant, Darmstadt, Germany; 13Quantitative Imaging Center, Department of Radiology, Boston University School of Medicine, Boston, MA, USA; 14Department of Radiology, Universitätsklinikum Erlangen and Friedrich-Alexander-University Erlangen-Nürnberg (FAU), Erlangen, Germany Correspondence to: Mylène P. Jansen, PhD. Department of Rheumatology & Clinical Immunology, UMC Utrecht, HP G02.228, Heidelberglaan 100, 3584CX Utrecht, [The Netherlands. Email: m.p.jansen-36@umcutrecht.nl](The Netherlands. Email: m.p.jansen-36@umcutrecht.nl). 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 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 p

FAQs

What was the IMI-APPROACH structural progression outcome and how was it defined?

Structural progression was predicted and evaluated using an s-score defined by a >0.3 mm/year decrease in joint space width (JSW). Progressors were also identified using smallest detectable change (SDC) thresholds for quantitative metrics or a full SQ-score increase for semiquantitative features.

Which imaging data were collected to assess progression over two years?

Radiographs and MRI scans were acquired at baseline and at the 2-year follow-up. Structural outcomes included radiographic JSW, subchondral bone density, osteophytes, and MRI quantitative cartilage thickness and semiquantitative measures such as cartilage damage, bone marrow lesions, and osteophytes.

How did Kellgren-Lawrence (KL) grades compare to machine-learning s-scores for predicting progression?

Baseline s-scores showed limited predictive value for JSW-related progression parameters, with many comparisons not reaching significance. KL grades were more consistently associated with progression across multiple MRI-based and radiographic parameters, outperforming s-scores as progression predictors.

Aug 5, 2026

Upload by Eliana