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T 2 ‐weighted magnetic resonance imaging texture as predictor of low back pain: A texture analysis‐based classification pipeline to symptomatic and asymptomatic cases
Author(s) -
Ketola Juuso H. J.,
Inkinen Satu I.,
Karppinen Jaro,
Niinimäki Jaakko,
Tervonen Osmo,
Nieminen Miika T.
Publication year - 2021
Publication title -
journal of orthopaedic research®
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.041
H-Index - 155
eISSN - 1554-527X
pISSN - 0736-0266
DOI - 10.1002/jor.24973
Subject(s) - magnetic resonance imaging , asymptomatic , medicine , low back pain , logistic regression , radiology , surgery , pathology , alternative medicine
Low back pain is a very common symptom and the leading cause of disability throughout the world. Several degenerative imaging findings seen on magnetic resonance imaging are associated with low back pain but none of them is specific for the presence of low back pain as abnormal findings are prevalent among asymptomatic subjects as well. The purpose of this population‐based study was to investigate if more specific magnetic resonance imaging predictors of low back pain could be found via texture analysis and machine learning. We used this methodology to classify T 2 ‐weighted magnetic resonance images from the Northern Finland Birth Cohort 1966 data to symptomatic and asymptomatic groups. Lumbar spine magnetic resonance imaging was performed using a fast spin‐echo sequence at 1.5 T. Texture analysis pipeline consisting of textural feature extraction, principal component analysis, and logistic regression classifier was applied to the data to classify them into symptomatic (clinically relevant pain with frequency ≥30 days and intensity ≥6/10) and asymptomatic (frequency ≤7 days, intensity ≤3/10, and no previous pain episodes in the follow‐up period) groups. Best classification results were observed applying texture analysis to the two lowest intervertebral discs (L4‐L5 and L5‐S1), with accuracy of 83%, specificity of 83%, sensitivity of 82%, negative predictive value of 94%, precision of 56%, and receiver operating characteristic area‐under‐curve of 0.91. To conclude, textural features from T 2 ‐weighted magnetic resonance images can be applied in low back pain classification.