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Eur Radiol. 2019 Jan;29(1):22-30. doi: 10.1007/s00330-018-5552-6. Epub 2018 Jun 14.

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

Author information

1
Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Raemistrasse 100, 8091, Zurich, Switzerland.
2
Horten Centre for Patient Oriented Research and Knowledge Transfer, University of Zurich, Pestalozzistrasse 24, 8032, Zurich, Switzerland.
3
Department of Orthopaedics, Balgrist University Hospital Zurich, University of Zurich, Forchstrasse 340, 8008, Zurich, Switzerland.
4
Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Raemistrasse 100, 8091, Zurich, Switzerland. roman.guggenberger@usz.ch.

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:

Machine learning; Magnetic resonance imaging; Muscles; Spine

PMID:
29948080
DOI:
10.1007/s00330-018-5552-6
[Indexed for MEDLINE]

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