Texture analysis in the classification of T2 -weighted magnetic resonance images in persons with and without low back pain.
J Orthop Res
; 39(10): 2187-2196, 2021 10.
Article
in En
| MEDLINE
| ID: mdl-33247597
ABSTRACT
Magnetic resonance imaging findings often do not distinguish between people with and without low back pain (LBP). However, there are still a large number of people who undergo magnetic resonance imaging to help determine the etiology of their back pain. Texture analysis shows promise for the classification of tissues that look similar, and machine learning can minimize the number of comparisons. This study aimed to determine if texture features from lumbar spine magnetic resonance imaging differ between people with and without LBP. In total, 14 participants with chronic LBP were matched for age, weight, and gender with 14 healthy volunteers. A custom texture analysis software was used to construct a gray-level co-occurrence matrix with one to four pixels offset in 0° direction for the disc and superior and inferior endplate regions. The Random Forests Algorithm was used to select the most promising classifiers. The linear mixed-effect model analysis was used to compare groups (pain vs. pain-free) at each level controlling for age. The Random Forest Algorithm recommended focusing on intervertebral discs and endplate zones at L4-5 and L5-S1. Differences were observed between groups for L5-S1 superior endplate contrast, homogeneity, and energy (p = .02). Differences were observed for L5-S1 disc contrast and homogeneity (p < .01), as well as for the inferior endplates contrast, homogeneity, and energy (p < .03). Magnetic resonance imaging textural features may have potential in identifying structures that may be the target of further investigations about the reasons for LBP.
Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Low Back Pain
/
Intervertebral Disc Degeneration
/
Intervertebral Disc
Type of study:
Etiology_studies
/
Prognostic_studies
Limits:
Humans
Language:
En
Journal:
J Orthop Res
Year:
2021
Document type:
Article
Affiliation country:
Canada
Country of publication:
EEUU
/
ESTADOS UNIDOS
/
ESTADOS UNIDOS DA AMERICA
/
EUA
/
UNITED STATES
/
UNITED STATES OF AMERICA
/
US
/
USA