ArticlePLoS medicine2025
Predicting knee osteoarthritis progression using neural network with longitudinal MRI radiomics, and biochemical biomarkers: A modeling study.
Article in PLoS medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Predictive Value of Machine Learning in Knee Osteoarthritis Progression: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2025Pooled it
- Artificial intelligence-based segmentation and quantitative assessment of knee cartilage in osteoarthritis: a systematic review and meta-analysis.International orthopaedics · 2026Review
- Artificial Intelligence-Guided Precision Orthobiologics in Musculoskeletal Conditions.Journal of orthopaedic case reports · 2026Review
- Osteoarthritis: Epidemiology, Diagnosis, and Treatment.MedComm · 2026Review
- Progress in machine learning-assisted medical imaging for osteoarthritis and osteoporosis diagnosis: a narrative review.Quantitative imaging in medicine and surgery · 2026Review
- Usefulness of 3D joint space width on weight-bearing CT in comparison with 2D joint space width on radiographs for predicting 24-month worsening of knee osteoarthritis pain and function in the MOST study.Osteoarthritis and cartilage · 2026Article
- Nomograms Based on X-Ray Radiomics for Predicting Pain Progression in Knee Osteoarthritis Using Data From the Foundation for the National Institutes of Health: Development and Validation Study.JMIR medical informatics · 2026Article
- The osteoarthritis inflammatory ecosystem: a node-edge-boundary-emergent framework toward precision stratification and therapeutic hypothesis generation.Frontiers in medicine · 2026Review
- Artificial intelligence in the biomechanics of knee osteoarthritis: biomechanical phenotyping, risk stratification, diagnosis, treatment and clinical translation-a narrative review.Frontiers in bioengineering and biotechnology · 2026Review
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15 authors.
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Abstract
backgroundKnee osteoarthritis (KOA) worsens both structurally and symptomatically, yet no model predicts KOA progression using Magnetic Resonance Image (MRI) radiomics and biomarkers. This study aimed to develop and test the longitudinal Load-Bearing Tissue Radiomic plus Biochemical biomarker and Clinical variable Model (LBTRBC-M) to predict KOA progression. METHODS AND
findingsData from the Foundation of the National Institutes of Health Osteoarthritis Biomarkers Consortium were used. We selected 594 participants with Kellgren-Lawrence grades 1-3 and complete biomarker data. The mean age was 61.6 ± 8.9 years, 58.8% were female, and the racial distribution was 79.3% White or White, 18.0% Black or African American, and 2.7% Asian or other non-White. A total of 1,753 knee MRIs were included across the study period, comprising 594 at baseline, 575 at 1-year follow-up, and 584 at 2-year follow-up. Outcomes included (1) both Joint Space Narrowing (JSN) and pain progression (n = 567), (2) only JSN progression (n = 303), (3) only pain progression (n = 295), and (4) non-progression (JSN or pain) (n = 588), corresponding to an approximate ratio of 2:1:1:2. JSN progression was defined as a minimum joint space width (JSW) loss of ≥0.7 mm, and pain progression as a sustained (≥2 time points) increase of ≥9 points on the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) pain subscale (0-100 scale). Using the eXtreme Gradient BOOSTing (XGBOOST) algorithm, the model was developed in the total development cohort (n = 877) and tested in the total test cohort (n = 876). In the total test cohort, the Area Under the receiver operating characteristic Curve (AUC) of LBTRBC-M for predicting JSN and pain progression, JSN progression, pain progression, and non-progression were 0.880 (95% confidence interval (CI) [0.853, 0.903]), 0.913 (95% CI [0.881, 0.937]), 0.886 (95% CI [0.856, 0.910]), and 0.909 (95% CI [0.888, 0.926]), respectively. The overall accuracy of LBTRBC-M was 70.1%. With LBTRBC-M assistance, the prognostic accuracy of resident physicians (n = 7) improved from 44.7%-49.0% to 64.4%-66.5%. The main limitations include the use of a non-routine MRI sequence, the lack of external validation in independent cohorts, and limited incorporation of all knee joint structures in radiomic feature extraction.
conclusionsIn this study, we observed that longitudinal MRI radiomic features of load-bearing knee joint tissues provide potentially informative markers for predicting knee osteoarthritis progression. These findings may help guide future efforts toward early risk stratification and personalized management of KOA.
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