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.
- machine learning
- precision medicine
- topological data analysis
ASJC Scopus subject areas
- Radiology Nuclear Medicine and imaging