MRI and biomechanics multidimensional data analysis reveals R2-R as an early predictor of cartilage lesion progression in knee osteoarthritis

Valentina Pedoia, Jenny Haefeli, Kazuhito Morioka, Hsiang Ling Teng, Lorenzo Nardo, Richard B. Souza, Adam R. Ferguson, Sharmila Majumdar

Research output: Contribution to journalArticle

9 Citations (Scopus)

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 T and T2 and R2-R (1/T2–1/T) 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 T values (T2 lateral femur P = 1.45*10-8, T 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-R 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-R may be an imaging biomarker for early OA. Level of Evidence: 3. Technical Efficacy: Stage 2. J. Magn. Reson. Imaging 2018;47:78–90.

Original languageEnglish (US)
Pages (from-to)78-90
Number of pages13
JournalJournal of Magnetic Resonance Imaging
Volume47
Issue number1
DOIs
StatePublished - Jan 1 2018
Externally publishedYes

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Knee Osteoarthritis
Biomechanical Phenomena
Osteoarthritis
Cartilage
Gait
Articular Cartilage
Disease Management
Knee Joint
Thigh
Tibia
Femur
Joints
Biomarkers
Magnetic Resonance Imaging
Demography
Phenotype

Keywords

  • machine learning
  • MRI
  • osteoarthritis
  • precision medicine
  • R-R
  • T/T
  • topological data analysis

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging

Cite this

MRI and biomechanics multidimensional data analysis reveals R2-R as an early predictor of cartilage lesion progression in knee osteoarthritis. / Pedoia, Valentina; Haefeli, Jenny; Morioka, Kazuhito; Teng, Hsiang Ling; Nardo, Lorenzo; Souza, Richard B.; Ferguson, Adam R.; Majumdar, Sharmila.

In: Journal of Magnetic Resonance Imaging, Vol. 47, No. 1, 01.01.2018, p. 78-90.

Research output: Contribution to journalArticle

Pedoia, Valentina ; Haefeli, Jenny ; Morioka, Kazuhito ; Teng, Hsiang Ling ; Nardo, Lorenzo ; Souza, Richard B. ; Ferguson, Adam R. ; Majumdar, Sharmila. / MRI and biomechanics multidimensional data analysis reveals R2-R as an early predictor of cartilage lesion progression in knee osteoarthritis. In: Journal of Magnetic Resonance Imaging. 2018 ; Vol. 47, No. 1. pp. 78-90.
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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.",
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AU - Pedoia, Valentina

AU - Haefeli, Jenny

AU - Morioka, Kazuhito

AU - Teng, Hsiang Ling

AU - Nardo, Lorenzo

AU - Souza, Richard B.

AU - Ferguson, Adam R.

AU - Majumdar, Sharmila

PY - 2018/1/1

Y1 - 2018/1/1

N2 - 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.

AB - 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.

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KW - precision medicine

KW - R-R

KW - T/T

KW - topological data analysis

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