Using multidimensional topological data analysis to identify traits of hip osteoarthritis

Jasmine Rossi-deVries, Valentina Pedoia, Michael A. Samaan, Adam R. Ferguson, Richard B. Souza, Sharmila Majumdar

Research output: Contribution to journalArticlepeer-review

12 Scopus citations


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/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.

Original languageEnglish
Pages (from-to)1046-1058
Number of pages13
JournalJournal of Magnetic Resonance Imaging
Issue number4
StatePublished - Oct 2018

Bibliographical note

Funding Information:
Contract grant sponsor: NIH-NIAMS; contract grant numbers: P50 AR060752 (SM), R01AR046905 (SM), K99AR070902 (VP).

Publisher Copyright:
© 2018 International Society for Magnetic Resonance in Medicine


  • big data
  • cartilage
  • hip OA
  • osteoarthritis
  • topological data analysis

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging


Dive into the research topics of 'Using multidimensional topological data analysis to identify traits of hip osteoarthritis'. Together they form a unique fingerprint.

Cite this