ArticleMagnetic resonance in medicine2012
Multivariate analysis of cartilage degradation using the support vector machine algorithm.
Article in Magnetic resonance in medicine, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
17 citing papers in PubMed, 34 citations in OpenAlex.
- Quantitative MRI methods for the assessment of structure, composition, and function of musculoskeletal tissues in basic research and preclinical applications.Magma (New York, N.Y.) · 2024Review
- In vivo multimodal imaging of hyaluronan-mediated inflammatory response in articular cartilage.Osteoarthritis and cartilage · 2022Article
- A Comparative Systematic Literature Review on Knee Bone Reports from MRI, X-rays and CT Scans Using Deep Learning and Machine Learning Methodologies.Diagnostics (Basel, Switzerland) · 2020Review
- Non-destructive detection of matrix stabilization correlates with enhanced mechanical properties of self-assembled articular cartilage.Journal of tissue engineering and regenerative medicine · 2019Article
- Predicting early symptomatic osteoarthritis in the human knee using machine learning classification of magnetic resonance images from the osteoarthritis initiative.Journal of orthopaedic research : official publication of the Orthopaedic Research Society · 2017Article
- MR Parametric Mapping as a Biomarker of Early Joint Degeneration.Sports health · 2016Review
- Analysis of mcDESPOT- and CPMG-derived parameter estimates for two-component nonexchanging systems.Magnetic resonance in medicine · 2016Article
- Article
- Classification of sodium MRI data of cartilage using machine learning.Magnetic resonance in medicine · 2015Article
- Machine learning classification of OARSI-scored human articular cartilage using magnetic resonance imaging.Osteoarthritis and cartilage · 2015Article
- Sensitivity and specificity of univariate MRI analysis of experimentally degraded cartilage under clinical imaging conditions.Journal of magnetic resonance imaging : JMRI · 2015Article
- Techniques and applications of in vivo diffusion imaging of articular cartilage.Journal of magnetic resonance imaging : JMRI · 2015Review
- Prediction of cartilage compressive modulus using multiexponential analysis of T(2) relaxation data and support vector regression.NMR in biomedicine · 2014Article
- Near infrared spectroscopic evaluation of water in hyaline cartilage.Annals of biomedical engineering · 2013Article
- How do statistical differences in matrix-sensitive magnetic resonance outcomes translate into clinical assignment rules?The Journal of the American Academy of Orthopaedic Surgeons · 2013Article
- Probing articular cartilage damage and disease by quantitative magnetic resonance imaging.Journal of the Royal Society, Interface · 2013Review
- Characterization of engineered cartilage constructs using multiexponential T₂ relaxation analysis and support vector regression.Tissue engineering. Part C, Methods · 2012Article
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
Abstract
An important limitation in MRI studies of early osteoarthritis is that measured MRI parameters exhibit substantial overlap between different degrees of cartilage degradation. We investigated whether multivariate support vector machine analysis would permit improved tissue characterization. Bovine nasal cartilage samples were subjected to pathomimetic degradation and their T(1), T(2), magnetization transfer rate (k(m) ), and apparent diffusion coefficient (ADC) were measured. Support vector machine analysis performed using certain parameter combinations exhibited particularly favorable classification properties. The areas under the receiver operating characteristic (ROC) curve for detection of extensive and mild degradation were 1.00 and 0.94, respectively, using the set (T(1), k(m), ADC), compared with 0.97 and 0.60 using T(1), the best univariate classifier. Furthermore, a degradation probability for each sample, derived from the support vector machine formalism using the parameter set (T(1), k(m), ADC), demonstrated much stronger correlations (r(2) = 0.79-0.88) with direct measurements of tissue biochemical components than did even the best-performing individual MRI parameter, T(1) (r(2) = 0.53-0.64). These results, combined with our previous investigation of Gaussian cluster-based tissue discrimination, indicate that the combinations (T(1), k(m)) and (T(1), k(m), ADC) may emerge as particularly useful for characterization of early cartilage degradation.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.