ArticleEuropean radiology2021
A machine learning approach to distinguish between knees without and with osteoarthritis using MRI-based radiomic features from tibial bone.
Article in European radiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 2 of them syntheses that pooled it.
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Who cites it
32 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Evaluating potential for AI automation of quantitative and semi-quantitative MRI scoring in arthritis, especially at the knee: a systematic literature review.Skeletal radiology · 2025Pooled it
- The value of deep learning-based X-ray techniques in detecting and classifying K-L grades of knee osteoarthritis: a systematic review and meta-analysis.European radiology · 2025Pooled it
- Radiomics-based nomogram using digital radiography for early diagnosis of knee osteoarthritis.La Radiologia medica · 2026Article
- Progress in machine learning-assisted medical imaging for osteoarthritis and osteoporosis diagnosis: a narrative review.Quantitative imaging in medicine and surgery · 2026Review
- 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
- Non-femoral head MRI radiomics as a potential imaging biomarker for identifying osteonecrosis of the femoral head in systemic lupus erythematosus patients.Frontiers in medicine · 2026Article
- Interpretable machine learning integrates multi-source biomarkers for osteoarthritis diagnosis and mechanistic insights: A temporomandibular joint model.Osteoarthritis and cartilage · 2025Article
- Patellar and Femoral Bone Morphology Is Associated With Overweight and Sports Participation in Young Adolescents.Journal of orthopaedic research : official publication of the Orthopaedic Research Society · 2025Article
- Radiomics-based diagnosis of patellar chondromalacia using sagittal T2-weighted images.Radiologie (Heidelberg, Germany) · 2025Article
- Using magnetic resonance imaging-based subregional texture analysis models to classify knee osteoarthritis severity by compartment.Scientific reports · 2025Article
- Estimation of time-to-total knee replacement surgery with multimodal modeling and artificial intelligence.Computers in biology and medicine · 2025Article
- Effects of nonstationary system blur on radiomic texture features of trabecular bone in normal and ultra-high resolution CT.Medical physics · 2025Article
- Ultrasound-based radiomics and machine learning for enhanced diagnosis of knee osteoarthritis: Evaluation of diagnostic accuracy, sensitivity, specificity, and predictive value.European journal of radiology open · 2025Article
- Advancing osteoarthritis research: the role of AI in clinical, imaging and omics fields.Bone research · 2025Review
- Integration of longitudinal load-bearing tissue MRI radiomics and neural network to predict knee osteoarthritis incidence.Journal of orthopaedic translation · 2025Article
- Progress in multi-omics studies of osteoarthritis.Biomarker research · 2025Review
- Computed tomography radiomics in predicting patient satisfaction after robotic-assisted total knee arthroplasty.International journal of computer assisted radiology and surgery · 2025Article
- Automatic knee osteoarthritis severity grading based on X-ray images using a hierarchical classification method.Arthritis research & therapy · 2024Article
- Radiomics signature of osteoarthritis: Current status and perspective.Journal of orthopaedic translation · 2024Review
- Radiomics analysis using magnetic resonance imaging of bone marrow edema for diagnosing knee osteoarthritis.Frontiers in bioengineering and biotechnology · 2024Article
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6 authors.
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Abstract
objectivesOur aim was to assess the ability of semi-automatically extracted magnetic resonance imaging (MRI)-based radiomic features from tibial subchondral bone to distinguish between knees without and with osteoarthritis.
methodsThe right knees of 665 females from the population-based Rotterdam Study scanned with 1.5T MRI were analyzed. A fast imaging employing steady-state acquisition sequence was used for the quantitative bone analyses. Tibial bone was segmented using a method that combines multi-atlas and appearance models. Radiomic features related to the shape and texture were calculated from six volumes of interests (VOIs) in the proximal tibia. Machine learning-based Elastic Net models with 10-fold cross-validation were used to distinguish between knees without and with MRI Osteoarthritis Knee Score (MOAKS)-based tibiofemoral osteoarthritis. Performance of the covariate (age and body mass index), image features, and combined covariate + image features models were assessed using the area under the receiver operating characteristic curve (ROC AUC).
resultsOf 665 analyzed knees, 76 (11.4%) had osteoarthritis. An ROC AUC of 0.68 (95% confidence interval (CI): 0.60-0.75) was obtained using the covariate model. The image features model yielded an ROC AUC of 0.80 (CI: 0.73-0.87). The model that combined image features from all VOIs and covariates yielded an ROC AUC of 0.80 (CI: 0.73-0.87).
conclusionOur results suggest that radiomic features are useful imaging biomarkers of subchondral bone for the diagnosis of osteoarthritis. An advantage of assessing bone on MRI instead of on radiographs is that other tissues can be assessed simultaneously. KEY POINTS: • Subchondral bone plays a role in the osteoarthritis disease processes. • MRI radiomics is a potential method for quantifying changes in subchondral bone. • Semi-automatically extracted radiomic features of tibia differ between subjects without and with osteoarthritis.
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