Correlation of texture analysis of paraspinal musculature on MRI with different clinical endpoints: Lumbar Stenosis Outcome Study (LSOS)

  • Manoj Mannil
  • Jakob M. Burgstaller
  • Ulrike Held
  • Mazda Farshad
  • Roman Guggenberger
Musculoskeletal

Abstract

Objectives

The aim of this study was to apply texture analysis (TA) on paraspinal musculature in T2-weighted (T2w) magnetic resonance images (MRI) of symptomatic lumbar spinal stenosis (LSS) patients and correlate the findings with clinical outcome measures.

Methods

Ninety patients were prospectively enrolled in the multi-centric Lumbar Stenosis Outcome Study (LSOS). All patients received a T2w MRI, from which we selected axial images perpendicular to the intervertebral disc at level L3/4 for TA. Regions-of-interest (ROI) were drawn of the paraspinal musculature and 304 TA features/ ROI were calculated. As clinical outcome measurements, we analysed three commonly applied measures: Spinal Stenosis Measure (SSM), Roland-Morris Disability Questionnaire (RMDQ), as well as the Numeric Rating Scale (NRS). We used two machine learning-based classifiers: Decision table, and k-nearest neighbours (k-NN).

Results

We observed no meaningful correlation between TA in paraspinal musculature and the two clinical outcome measures SSM symptoms and SSM function, while a moderate correlation was observed regarding the outcome measures RMDQ (k-NN: r = 0.56) and NRS (Decision Table: r = 0.72).

Conclusions

In conclusion, MR TA is a viable tool to quantify medical images and illustrate correlations of microarchitectural changes invisible to a human reader with potential clinical impact.

Key Points

• TA is feasible on paraspinal musculature using MRI.

• TA on paraspinal musculature correlates with SSM and RMDQ.

• TA may enable a statement regarding clinical impact of imaging findings.

Keywords

Magnetic resonance imaging Machine learning Spine Muscles 

Abbreviations

AUC

Area under the curve

BMI

Body-mass-index

CSA

Cross-sectional muscle area

DICOM

Digital imaging and communications in medicine

GLCM

Grey-level co-occurrence matrix

k-NN

k-Nearest Neighbour

LBP

Lumbar back pain

LSS

Lumbar spinal stenosis

MRI

Magnetic resonance imaging

NRS

Numeric rating scale (pain)

PACS

Picture archiving and communication system

RLM

Run-length matrix

RMDQ

Roland-Morris-disability questionnaire

ROC

Receiver-operating characteristics

ROI

Region of interest

SSM

Spinal stenosis measure

TA

Texture analysis

TSE

Turbo spin echo

Notes

Funding

This study has received funding by Helmut Horten Foundation, Baugarten Foundation, Pfizer-Foundation for geriatrics & research in geriatrics, Symphasis Charitable Foundation and OPO Foundation funds.

Compliance with ethical standards

Guarantor

The scientific guarantor of this publication is Roman Guggenberger, MD.

Conflict of interest

The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.

Statistics and biometry

Ulrike Held and Jakob M. Burgstaller kindly provided statistical advice for this manuscript.

Informed consent

Written informed consent was obtained from all subjects (patients) in this study.

Ethical approval

Institutional Review Board approval was obtained.

Study subjects or cohorts overlap

http://www.lumbalstenose.ch/home/

Methodology

• retrospective

• observational

• multicentre study

Supplementary material

330_2018_5552_MOESM1_ESM.docx (20 kb)
ESM 1 (DOCX 19 kb)

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Copyright information

© European Society of Radiology 2018

Authors and Affiliations

  • Manoj Mannil
    • 1
  • Jakob M. Burgstaller
    • 2
  • Ulrike Held
    • 2
  • Mazda Farshad
    • 3
  • Roman Guggenberger
    • 1
  1. 1.Institute of Diagnostic and Interventional Radiology, University Hospital ZurichUniversity of ZurichZurichSwitzerland
  2. 2.Horten Centre for Patient Oriented Research and Knowledge TransferUniversity of ZurichZurichSwitzerland
  3. 3.Department of Orthopaedics, Balgrist University Hospital ZurichUniversity of ZurichZurichSwitzerland

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