Evidence map›Paper›PMID 35285561›Full record

ArticleJournal of magnetic resonance imaging : JMRI2022

Paraspinal Muscle in Chronic Low Back Pain: Comparison Between Standard Parameters and Chemical Shift Encoding-Based Water-Fat MRI.

Nico Sollmann, Noah B Bonnheim, Gabby B Joseph, Ravi Chachad, Jiamin Zhou, Zehra Akkaya, Amir M Pirmoazen, Jeannie F Bailey, Xiaojie Guo, Ann A Lazar and 3 more

Open access · greenAbstract read
In one paragraph

Article in Journal of magnetic resonance imaging : JMRI, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed
4.0field-weighted citation impact, top 6% of its field
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

21 citing papers in PubMed, 25 citations in OpenAlex.

  1. Review
  2. Article
  3. Article
  4. Spatial patterns of fat within the deep multifidus as a biomarker for chronic low back pain.The spine journal : official journal of the North American Spine Society · 2026
    Observational
  5. Article
  6. Article
  7. Article
  8. Color Maps: Facilitating the Clinical Impact of Quantitative MRI.Journal of magnetic resonance imaging : JMRI · 2025
    Review
  9. Article
  10. Myosteatosis: diagnostic significance and assessment by imaging approaches.Quantitative imaging in medicine and surgery · 2024
    Review
  11. Article
  12. Article
  13. Article
  14. Article
  15. Journal of orthopaedic translation · 2023
    Review
  16. Article
  17. Article
  18. Article
  19. Review
  20. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

13 authors at 1 institution in 2 countries.

Nico SollmannDepartment of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, California.ORCID 0000-0002-8120-2223
Noah B BonnheimDepartment of Orthopaedic Surgery, University of California San Francisco, San Francisco, California, USA.
Gabby B JosephDepartment of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, California.
Ravi ChachadDepartment of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, California.
Jiamin ZhouDepartment of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, California.
Zehra AkkayaDepartment of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, California.
Amir M PirmoazenDepartment of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, California.ORCID 0000-0003-0010-8794
Jeannie F BaileyDepartment of Orthopaedic Surgery, University of California San Francisco, San Francisco, California, USA.
Xiaojie GuoDepartment of Orthopaedic Surgery, University of California San Francisco, San Francisco, California, USA.
Ann A LazarDepartment of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, California, USA.
Thomas M LinkDepartment of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, California.
Aaron J FieldsDepartment of Orthopaedic Surgery, University of California San Francisco, San Francisco, California, USA.
Roland KrugDepartment of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, California.
University of California, San Francisco · US

Funding

UCSF Core Center for Patient-centric Mechanistic Phenotyping in Chronic Low Back PainU19AR076737 · NIAMS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI LOTZ, JEFFREY C. · 2019 to 2023
$34.1M
Skeletal Biology and Biomechanics (SBB) CoreP30AR075055 · NIAMS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI Wenhan Chang · 2019 to 2026
$7.1M
UCSF Core Center for Musculoskeletal Biology and MedicineP30AR066262 · NIAMS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI LOTZ, JEFFREY C. · 2014 to 2018
$3.0M
Novel imaging of endplate biomarkers in chronic low back painUH3AR076719 · NIAMS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI FIELDS, AARON J · 2021 to 2021
$1.9M
Novel imaging of endplate biomarkers in chronic low back painUH2AR076719 · NIAMS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI FIELDS, AARON J, KRUG, ROLAND · 2019 to 2019
$1.1M
NIAMS NIH HHS P30 AR066262NIAMS NIH HHS P30 AR075055NIAMS NIH HHS U19 AR076737NIAMS NIH HHS UH2 AR076719NIAMS NIH HHS UH3 AR076719NIH HHS P30AR075055NIH HHS U19-AR076737NIH HHS UH2-AR07671
6 · The paper itself

Abstract

backgroundParaspinal musculature (PSM) is increasingly recognized as a contributor to low back pain (LBP), but with conventional MRI sequences, assessment is limited. Chemical shift encoding-based water-fat MRI (CSE-MRI) enables the measurement of PSM fat fraction (FF), which may assist investigations of chronic LBP. PURPOSE: To investigate associations between PSM parameters from conventional MRI and CSE-MRI and between PSM parameters and pain. STUDY TYPE: Prospective, cross-sectional. POPULATION: Eighty-four adults with chronic LBP (44.6 ± 13.4 years; 48 males). FIELD STRENGTH/SEQUENCE: 3-T, T1-weighted fast spin-echo and iterative decomposition of water and fat with echo asymmetry and least squares estimation sequences. ASSESSMENT: T1-weighted images for Goutallier classification (GC), muscle volume, lumbar indentation value, and muscle-fat index, CSE-MRI for FF extraction (L1/2-L5/S1). Pain was self-reported using a visual analogue scale (VAS). Intra- and/or interreader agreement was assessed for MRI-derived parameters. STATISTICAL TESTS: Mixed-effects and linear regression models to 1) assess relationships between PSM parameters (entire cohort and subgroup with GC grades 0 and 1; statistical significance α = 0.0025) and 2) evaluate associations of PSM parameters with pain (α = 0.05). Intraclass correlation coefficients (ICCs) for intra- and/or interreader agreement.

resultsThe FF showed excellent intra- and interreader agreement (ICC range: 0.97-0.99) and was significantly associated with GC at all spinal levels. Subgroup analysis suggested that early/subtle changes in PSM are detectable with FF but not with GC, given the absence of significant associations between FF and GC (P-value range: 0.036 at L5/S1 to 0.784 at L2/L3). Averaged over all spinal levels, FF and GC were significantly associated with VAS scores. DATA

conclusionIn the absence of FF, GC may be the best surrogate for PSM quality. Given the ability of CSE-MRI to detect muscle alterations at early stages of PSM degeneration, this technique may have potential for further investigations of the role of PSM in chronic LBP. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY STAGE: 2.

Indexed as

Low Back PainParaspinal MusclesAdultCross-Sectional StudiesHumansLumbar VertebraeMagnetic Resonance ImagingMaleProspective StudiesWaterWaterchemical shiftfat fractionGoutallier classificationlow back painparaspinal musculature

Identifiers

PMID35285561
PMCPMC9470775
OpenAlexW4221032494

What OpenQuestion holds

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LicenceCC BY-NC
Read underepoch 390

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.