Background

Osteoarthritis (OA) is a multifaceted disease with many variables affecting diagnosis and progression. Topological data analysis (TDA) is a state-of-the-art big data analytics tool that can combine all variables into multidimensional space. TDA is used to simultaneously analyze imaging and gait analysis techniques.

Purpose

To identify biochemical and biomechanical biomarkers able to classify different disease progression phenotypes in subjects with and without radiographic signs of hip OA.

Study type

Longitudinal study for comparison of progressive and nonprogressive subjects.

Population

In all, 102 subjects with and without radiographic signs of hip osteoarthritis.

Field strength/sequence

3T, SPGR 3D MAPSS T_1ρ /T₂ , intermediate-weighted fat-suppressed fast spin-echo (FSE).

Assessment

Multidimensional data analysis including cartilage composition, bone shape, Kellgren-Lawrence (KL) classification of osteoarthritis, scoring hip osteoarthritis with MRI (SHOMRI), hip disability and osteoarthritis outcome score (HOOS).

Statistical tests

Analysis done using TDA, Kolmogorov-Smirnov (KS) testing, and Benjamini-Hochberg to rank P-value results to correct for multiple comparisons.

Results

Subjects in the later stages of the disease had an increased SHOMRI score (P < 0.0001), increased KL (P = 0.0012), and older age (P < 0.0001). Subjects in the healthier group showed intact cartilage and less pain. Subjects found between these two groups had a range of symptoms. Analysis of this subgroup identified knee biomechanics (P < 0.0001) as an initial marker of the disease that is noticeable before the morphological progression and degeneration. Further analysis of an OA subgroup with femoroacetabular impingement (FAI) showed anterior labral tears to be the most significant marker (P = 0.0017) between those FAI subjects with and without OA symptoms.

Data conclusion

The data-driven analysis obtained with TDA proposes new phenotypes of these subjects that partially overlap with the radiographic-based classical disease status classification and also shows the potential for further examination of an early onset biomechanical intervention.

Level of evidence

2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;48:1046-1058.

The goal of this study was to use quantitative MRI analysis to longitudinally observe the relationship between 3D proximal femur shape and hip joint degenerative changes. Forty-six subjects underwent unilateral hip MR imaging at three time points (baseline, 18 and 36 months). 3D shape analysis, hip cartilage T_1ρ /T₂ relaxation time quantification, and SHOMRI MRI grading were performed at each time point. Subjects were grouped based on KL, SHOMRI, and HOOS pain scores. Associations between these score groupings, time, and longitudinal variation in shape, were analyzed using a generalized estimating equation. One-way ANCOVA was conducted to evaluate change in shape as a predictor of the worsening of degenerative changes at 36 months. Our results demonstrated that subjects displayed an increase in the volume of the femoral head and neck (Mode 3) over time. This shape mode was significantly more prevalent in patients that reported pain. Longitudinal changes in this shape mode also served as borderline predictors of elevated T_1ρ values (p = 0.055) and of cartilage lesions (p = 0.068). Subjects showed a change in the Femoral Neck Anteversion angle (FNA) over time (Mode 6). This shape mode showed a significant interaction with the presence of cartilage lesions. The results of this study suggest that specific variations in bone shape quantified through 3D-MRI based Statistical Shape modeling show an observable relationship with hip joint compositional and morphological changes. The shapes observed lead to early degenerative changes, which may lead into OA, thus confirming the important role of bone shape changes in the pathogenesis of OA. © 2018 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res.