Evidence map›Paper›PMID 29734501›Full record

ArticleJournal of magnetic resonance imaging : JMRI2018

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

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of magnetic resonance imaging : JMRI, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

9 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Article
  6. Topological data analysis in biomedicine: A review.Journal of biomedical informatics · 2022
    Review
  7. [Journal of orthopaedic research : official publication of the Orthopaedic Research Society · 2019
    Article
  8. Diagnosing osteoarthritis from TOsteoarthritis and cartilage · 2019
    Review
  9. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Jasmine Rossi-deVriesDepartment of Radiology and Biomedical Imaging, University of California, San Francisco, California, USA.
Valentina PedoiaDepartment of Radiology and Biomedical Imaging, University of California, San Francisco, California, USA.
Michael A SamaanDepartment of Radiology and Biomedical Imaging, University of California, San Francisco, California, USA.
Adam R FergusonWeill Institute for Neurosciences, Department of Neurological Surgery, Brain and Spinal Injury Center, University of California, San Francisco, California, USA.
Richard B SouzaDepartment of Radiology and Biomedical Imaging, University of California, San Francisco, California, USA.
Sharmila MajumdarDepartment of Radiology and Biomedical Imaging, University of California, San Francisco, California, USA.

Funding

Morphological and Functional Musko-Skeletal ImagingR01AG017762 · NIA · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI MAJUMDAR, SHARMILA · 2000 to 2011
$9.1M
Structural, Biochemical and Functional Connectivity in Osteoarthritis using Quantitative Magnetic Resonance Imaging and Skeletal BiomechanicsR01AR069006 · NIAMS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI MAJUMDAR, SHARMILA, SOUZA, RICHARD BRIAN · 2016 to 2025
$7.2M
MRI TO STUDY CARTILAGE/BONE INTERACTIONSR01AR046905 · NIAMS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI MAJUMDAR, SHARMILA · 1999 to 2015
$6.3M
Translation of Quantitative Imaging in OsteoarthritisP50AR060752 · NIAMS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI SOUZA, RICHARD BRIAN · 2011 to 2015
$6.2M
UCSF Core Center for Musculoskeletal Biology and MedicineP30AR066262 · NIAMS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI MAJUMDAR, SHARMILA · 2014 to 2018
$3.0M
Mentoring Biomechanics Research in OsteoarthritisK24AR072133 · NIAMS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI Richard Brian Souza · 2018 to 2026
$1.5M
Multidimensional MRI-based Big Data Analytics to Study OsteoarthritisK99AR070902 · NIAMS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI PEDOIA, VALENTINA · 2017 to 2017
$94k
NIAMS NIH HHS K24 AR072133NIAMS NIH HHS K99 AR070902NIAMS NIH HHS P30 AR066262NIAMS NIH HHS P50 AR060752NIAMS NIH HHS R01 AR046905NIAMS NIH HHS R01 AR069006NIA NIH HHS R01 AG017762NIH-NIAMS K99AR070902NIH-NIAMS R01AR046905
6 · The paper itself

Abstract

backgroundOsteoarthritis (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 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.

resultsSubjects 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

conclusionThe 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.

Indexed as

GaitMagnetic Resonance ImagingAcetabulumAdultAlgorithmsBiomechanical PhenomenaCartilage, ArticularCase-Control StudiesDisease ProgressionFemaleFemurHip JointHumansImage Processing, Computer-AssistedKineticsLongitudinal Studiesbig datacartilagehip OAosteoarthritistopological data analysis

Identifiers

PMID29734501
PMCPMC6174097

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.