Texture analysis of paraspinal musculature in MRI of the lumbar spine: analysis of the lumbar stenosis outcome study (LSOS) data

Skeletal Radiol. 2018 Jul;47(7):947-954. doi: 10.1007/s00256-018-2919-3. Epub 2018 Mar 1.


Objective: To evaluate association of fatty infiltration in paraspinal musculature with clinical outcomes in patients suffering from lumbar spinal stenosis (LSS) using qualitative and quantitative grading in magnetic resonance imaging (MRI).

Materials and methods: In this retrospective study, texture analysis (TA) was performed on postprocessed axial T2 weighted (w) MR images at level L3/4 using dedicated software (MaZda) in 62 patients with LSS. Associations in fatty infiltration between qualitative Goutallier and quantitative TA findings with two clinical outcome measures, Spinal stenosis measure (SSM) score and walking distance, at baseline and regarding change over time were assessed using machine learning algorithms and multiple logistic regression models.

Results: Quantitative assessment of fatty infiltration using the histogram TA feature "mean" showed higher interreader reliability (ICC 0.83-0.97) compared to the Goutallier staging (κ = 0.69-0.93). No correlation between Goutallier staging and clinical outcome measures was observed. Among 151 TA features, only TA feature "mean" of the spinotransverse group showed a significant but weak correlation with worsened SSM (p = 0.046). TA feature "S(3,3) entropy" showed a significant but weak association with worsened WD over 12 months (p = 0.046).

Conclusion: MR TA is a reproducible tool to quantitatively assess paraspinal fatty infiltration, but there is no clear association with the clinical outcome in asymptomatic LSS patients.

Keywords: Fat quantification; LSOS; Lumbar stenosis; Magnetic resonance imaging; Paraspinal musculature; Texture analysis.

MeSH terms

  • Adipose Tissue / diagnostic imaging*
  • Aged
  • Algorithms
  • Female
  • Humans
  • Image Interpretation, Computer-Assisted / methods*
  • Lumbar Vertebrae / diagnostic imaging*
  • Machine Learning
  • Magnetic Resonance Imaging / methods*
  • Male
  • Paraspinal Muscles
  • Retrospective Studies
  • Spinal Stenosis / classification
  • Spinal Stenosis / diagnostic imaging*