MRI and biomechanics multidimensional data analysis reveals R2‐R1ρ as an early predictor of cartilage lesion progression in knee osteoarthritis
Purpose To couple quantitative compositional MRI, gait analysis, and machine learning multidimensional data analysis to study osteoarthritis (OA). OA is a multifactorial disorder accompanied by biochemical and morphological changes in the articular cartilage, modulated by skeletal biomechanics and g...
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| Vydáno v: | Journal of magnetic resonance imaging Ročník 47; číslo 1; s. 78 - 90 |
|---|---|
| Hlavní autoři: | , , , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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01.01.2018
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| ISSN: | 1053-1807, 1522-2586, 1522-2586 |
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| Abstract | Purpose
To couple quantitative compositional MRI, gait analysis, and machine learning multidimensional data analysis to study osteoarthritis (OA). OA is a multifactorial disorder accompanied by biochemical and morphological changes in the articular cartilage, modulated by skeletal biomechanics and gait. While we can now acquire detailed information about the knee joint structure and function, we are not yet able to leverage the multifactorial factors for diagnosis and disease management of knee OA.
Materials and Methods
We mapped 178 subjects in a multidimensional space integrating: demographic, clinical information, gait kinematics and kinetics, cartilage compositional T1ρ and T2 and R2‐R1ρ (1/T2–1/T1ρ) acquired at 3T and whole‐organ magnetic resonance imaging score morphological grading. Topological data analysis (TDA) and Kolmogorov–Smirnov test were adopted for data integration, analysis, and hypothesis generation. Regression models were used for hypothesis testing.
Results
The results of the TDA showed a network composed of three main patient subpopulations, thus potentially identifying new phenotypes. T2 and T1ρ values (T2 lateral femur P = 1.45*10‐8, T1ρ medial tibia P = 1.05*10‐5), the presence of femoral cartilage defects (P = 0.0013), lesions in the meniscus body (P = 0.0035), and race (P = 2.44*10‐4) were key markers in the subpopulation classification. Within one of the subpopulations we observed an association between the composite metric R2‐R1ρ and the longitudinal progression of cartilage lesions.
Conclusion
The analysis presented demonstrates some of the complex multitissue biochemical and biomechanical interactions that define joint degeneration and OA using a multidimensional approach, and potentially indicates that R2‐R1ρ may be an imaging biomarker for early OA.
Level of Evidence: 3
Technical Efficacy: Stage 2
J. Magn. Reson. Imaging 2018;47:78–90. |
|---|---|
| AbstractList | Purpose
To couple quantitative compositional MRI, gait analysis, and machine learning multidimensional data analysis to study osteoarthritis (OA). OA is a multifactorial disorder accompanied by biochemical and morphological changes in the articular cartilage, modulated by skeletal biomechanics and gait. While we can now acquire detailed information about the knee joint structure and function, we are not yet able to leverage the multifactorial factors for diagnosis and disease management of knee OA.
Materials and Methods
We mapped 178 subjects in a multidimensional space integrating: demographic, clinical information, gait kinematics and kinetics, cartilage compositional T1ρ and T2 and R2‐R1ρ (1/T2–1/T1ρ) acquired at 3T and whole‐organ magnetic resonance imaging score morphological grading. Topological data analysis (TDA) and Kolmogorov–Smirnov test were adopted for data integration, analysis, and hypothesis generation. Regression models were used for hypothesis testing.
Results
The results of the TDA showed a network composed of three main patient subpopulations, thus potentially identifying new phenotypes. T2 and T1ρ values (T2 lateral femur P = 1.45*10‐8, T1ρ medial tibia P = 1.05*10‐5), the presence of femoral cartilage defects (P = 0.0013), lesions in the meniscus body (P = 0.0035), and race (P = 2.44*10‐4) were key markers in the subpopulation classification. Within one of the subpopulations we observed an association between the composite metric R2‐R1ρ and the longitudinal progression of cartilage lesions.
Conclusion
The analysis presented demonstrates some of the complex multitissue biochemical and biomechanical interactions that define joint degeneration and OA using a multidimensional approach, and potentially indicates that R2‐R1ρ may be an imaging biomarker for early OA.
Level of Evidence: 3
Technical Efficacy: Stage 2
J. Magn. Reson. Imaging 2018;47:78–90. To couple quantitative compositional MRI, gait analysis, and machine learning multidimensional data analysis to study osteoarthritis (OA). OA is a multifactorial disorder accompanied by biochemical and morphological changes in the articular cartilage, modulated by skeletal biomechanics and gait. While we can now acquire detailed information about the knee joint structure and function, we are not yet able to leverage the multifactorial factors for diagnosis and disease management of knee OA.PURPOSETo couple quantitative compositional MRI, gait analysis, and machine learning multidimensional data analysis to study osteoarthritis (OA). OA is a multifactorial disorder accompanied by biochemical and morphological changes in the articular cartilage, modulated by skeletal biomechanics and gait. While we can now acquire detailed information about the knee joint structure and function, we are not yet able to leverage the multifactorial factors for diagnosis and disease management of knee OA.We mapped 178 subjects in a multidimensional space integrating: demographic, clinical information, gait kinematics and kinetics, cartilage compositional T1ρ and T2 and R2 -R1ρ (1/T2 -1/T1ρ ) acquired at 3T and whole-organ magnetic resonance imaging score morphological grading. Topological data analysis (TDA) and Kolmogorov-Smirnov test were adopted for data integration, analysis, and hypothesis generation. Regression models were used for hypothesis testing.MATERIALS AND METHODSWe mapped 178 subjects in a multidimensional space integrating: demographic, clinical information, gait kinematics and kinetics, cartilage compositional T1ρ and T2 and R2 -R1ρ (1/T2 -1/T1ρ ) acquired at 3T and whole-organ magnetic resonance imaging score morphological grading. Topological data analysis (TDA) and Kolmogorov-Smirnov test were adopted for data integration, analysis, and hypothesis generation. Regression models were used for hypothesis testing.The results of the TDA showed a network composed of three main patient subpopulations, thus potentially identifying new phenotypes. T2 and T1ρ values (T2 lateral femur P = 1.45*10-8 , T1ρ medial tibia P = 1.05*10-5 ), the presence of femoral cartilage defects (P = 0.0013), lesions in the meniscus body (P = 0.0035), and race (P = 2.44*10-4 ) were key markers in the subpopulation classification. Within one of the subpopulations we observed an association between the composite metric R2 -R1ρ and the longitudinal progression of cartilage lesions.RESULTSThe results of the TDA showed a network composed of three main patient subpopulations, thus potentially identifying new phenotypes. T2 and T1ρ values (T2 lateral femur P = 1.45*10-8 , T1ρ medial tibia P = 1.05*10-5 ), the presence of femoral cartilage defects (P = 0.0013), lesions in the meniscus body (P = 0.0035), and race (P = 2.44*10-4 ) were key markers in the subpopulation classification. Within one of the subpopulations we observed an association between the composite metric R2 -R1ρ and the longitudinal progression of cartilage lesions.The analysis presented demonstrates some of the complex multitissue biochemical and biomechanical interactions that define joint degeneration and OA using a multidimensional approach, and potentially indicates that R2 -R1ρ may be an imaging biomarker for early OA.CONCLUSIONThe analysis presented demonstrates some of the complex multitissue biochemical and biomechanical interactions that define joint degeneration and OA using a multidimensional approach, and potentially indicates that R2 -R1ρ may be an imaging biomarker for early OA.3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;47:78-90.LEVEL OF EVIDENCE3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;47:78-90. |
| Author | Morioka, Kazuhito Majumdar, Sharmila Souza, Richard B. Nardo, Lorenzo Ferguson, Adam R. Pedoia, Valentina Teng, Hsiang‐Ling Haefeli, Jenny |
| AuthorAffiliation | 4 San Francisco Veterans Affairs Medical Center, San Francisco, California, USA 2 Weill Institute for Neurosciences, Department of Neurological Surgery, Brain and Spinal Injury Center, University of California, San Francisco, California, USA 3 Department of Physical Therapy and Rehabilitation Science, University of California, San Francisco, California, USA 1 Department of Radiology and Biomedical Imaging, University of California, San Francisco, California, USA |
| AuthorAffiliation_xml | – name: 3 Department of Physical Therapy and Rehabilitation Science, University of California, San Francisco, California, USA – name: 1 Department of Radiology and Biomedical Imaging, University of California, San Francisco, California, USA – name: 4 San Francisco Veterans Affairs Medical Center, San Francisco, California, USA – name: 2 Weill Institute for Neurosciences, Department of Neurological Surgery, Brain and Spinal Injury Center, University of California, San Francisco, California, USA |
| Author_xml | – sequence: 1 givenname: Valentina surname: Pedoia fullname: Pedoia, Valentina email: valentina.pedoia@ucsf.edu organization: University of California – sequence: 2 givenname: Jenny surname: Haefeli fullname: Haefeli, Jenny organization: University of California – sequence: 3 givenname: Kazuhito surname: Morioka fullname: Morioka, Kazuhito organization: University of California – sequence: 4 givenname: Hsiang‐Ling surname: Teng fullname: Teng, Hsiang‐Ling organization: University of California – sequence: 5 givenname: Lorenzo surname: Nardo fullname: Nardo, Lorenzo organization: University of California – sequence: 6 givenname: Richard B. surname: Souza fullname: Souza, Richard B. organization: University of California – sequence: 7 givenname: Adam R. surname: Ferguson fullname: Ferguson, Adam R. email: adam.ferguson@ucsf.edu organization: San Francisco Veterans Affairs Medical Center – sequence: 8 givenname: Sharmila surname: Majumdar fullname: Majumdar, Sharmila organization: University of California |
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| Copyright | 2017 International Society for Magnetic Resonance in Medicine 2017 International Society for Magnetic Resonance in Medicine. |
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To couple quantitative compositional MRI, gait analysis, and machine learning multidimensional data analysis to study osteoarthritis (OA). OA is a... To couple quantitative compositional MRI, gait analysis, and machine learning multidimensional data analysis to study osteoarthritis (OA). OA is a... |
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| SubjectTerms | machine learning MRI osteoarthritis precision medicine R2‐R1ρ T1ρ/T2 topological data analysis |
| Title | MRI and biomechanics multidimensional data analysis reveals R2‐R1ρ as an early predictor of cartilage lesion progression in knee osteoarthritis |
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