ArticleEuropean radiology2017
Prediction of medial tibiofemoral compartment joint space loss progression using volumetric cartilage measurements: Data from the FNIH OA biomarkers consortium.
Article in European radiology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed, 39 citations in OpenAlex.
- Osteoarthritis: Epidemiology, Diagnosis, and Treatment.MedComm · 2026Review
- Clinical validation of fully automated (peri-)articular tissue analysis for assessing osteoarthritis progression: A narrative review.Osteoarthritis and cartilage open · 2026Review
- Predicting knee osteoarthritis progression using neural network with longitudinal MRI radiomics, and biochemical biomarkers: A modeling study.PLoS medicine · 2025Article
- Next-Level Prediction of Structural Progression in Knee Osteoarthritis: A Perspective.International journal of molecular sciences · 2025Review
- Gaussian Aquila optimizer based dual convolutional neural networks for identification and grading of osteoarthritis using knee joint images.Scientific reports · 2024Article
- Investigating artificial intelligence models for predicting joint pain from serum biochemistry.Revista da Associacao Medica Brasileira (1992) · 2024Article
- DeepKOA: a deep-learning model for predicting progression in knee osteoarthritis using multimodal magnetic resonance images from the osteoarthritis initiative.Quantitative imaging in medicine and surgery · 2023Article
- Development and evaluation of nomograms for predicting osteoarthritis progression based on MRI cartilage parameters: data from the FNIH OA biomarkers Consortium.BMC medical imaging · 2023Article
- Conventional MRI-derived subchondral trabecular biomarkers and their association with knee cartilage volume loss as early as 1 year: a longitudinal analysis from Osteoarthritis Initiative.Skeletal radiology · 2022Article
- Deep learning approach to predict pain progression in knee osteoarthritis.Skeletal radiology · 2022Article
- Discovering Knee Osteoarthritis Imaging Features for Diagnosis and Prognosis: Review of Manual Imaging Grading and Machine Learning Approaches.Journal of healthcare engineering · 2022Review
- Novel nomogram for predicting the progression of osteoarthritis based on 3D-MRI bone shape: data from the FNIH OA biomarkers consortium.BMC musculoskeletal disorders · 2021Article
- Conventional MRI-based subchondral trabecular biomarkers as predictors of knee osteoarthritis progression: data from the Osteoarthritis Initiative.European radiology · 2021Article
- Practical Implementation of Artificial Intelligence-Based Deep Learning and Cloud Computing on the Application of Traditional Medicine and Western Medicine in the Diagnosis and Treatment of Rheumatoid Arthritis.Frontiers in pharmacology · 2021Review
- Multi-classifier prediction of knee osteoarthritis progression from incomplete imbalanced longitudinal data.Scientific reports · 2020Article
- Deep learning risk assessment models for predicting progression of radiographic medial joint space loss over a 48-MONTH follow-up period.Osteoarthritis and cartilage · 2020Article
- Unifying the seeds auto-generation (SAGE) with knee cartilage segmentation framework: data from the osteoarthritis initiative.International journal of computer assisted radiology and surgery · 2019Article
- Article
- Imaging of osteoarthritis-recent research developments and future perspective.The British journal of radiology · 2018Review
- 3T MRI of the knee with optimised isotropic 3D sequences: Accurate delineation of intra-articular pathology without prolonged acquisition times.European radiology · 2017Article
Corrections and comments
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Authors and funding
8 authors at 7 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesInvestigating the association between baseline cartilage volume measurements (and initial 24th month volume loss) with medial compartment Joint-Space-Loss (JSL) progression (>0.7 mm) during 24-48th months of study.
methodsCase and control cohorts (Biomarkers Consortium subset from the Osteoarthritis Initiative (OAI)) were defined as participants with (n=297) and without (n=303) medial JSL progression (during 24-48th months). Cartilage volume measurements (baseline and 24th month loss) were obtained at five knee plates (medial-tibial, lateral-tibial, medial-femoral, lateral-femoral and patellar), and standardized values were analysed. Multivariate logistic regression was used with adjustment for known confounders. Artificial-Neural-Network analysis was conducted by Multi-Layer-Perceptrons (MLPs) including baseline determinants, and baseline (1) and interval changes (2) in cartilage volumes.
resultsLarger baseline lateral-femoral cartilage volume was predictive of medial JSL (OR: 1.29 (1.01-1.64)). Greater initial 24th month lateral-femoral cartilage volume-loss (OR: 0.48 (0.27-0.84)) had protective effect on medial JSL during 24-48th months of study. Baseline and interval changes in lateral-femoral cartilage volume, were the most important estimators for medial JSL progression (importance values: 0.191(0.177-0.204), 0.218(0.207-0.228)) in the ANN analyses.
conclusionsCartilage volumes (both at baseline and their change during the initial 24 months) in the lateral femoral plate were predictive of medial JSL progression. KEY POINTS: • Baseline lateral femoral cartilage volume is directly associated with medial JSL progression. • 24-month lateral femoral cartilage loss is inversely associated with medial JSL progression. • Lateral femoral cartilage volume is most important in association with medial JSL progression.
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