Ketola, JHJ, Inkinen, SI, Karppinen, J, Niinimäki, J, Tervonen, O, Nieminen, MT. T2-weighted magnetic resonance imaging texture as predictor of low back pain: A texture analysis-based classification pipeline to symptomatic and asymptomatic cases. J Orthop Res. 2021; 39: 2428- 2438. https://doi.org/10.1002/jor.24973
T₂-weighted magnetic resonance imaging texture as predictor of low back pain : a texture analysis-based classification pipeline to symptomatic and asymptomatic cases
|Author:||Ketola, Juuso H. J.1; Inkinen, Satu I.1; Karppinen, Jaro2,3,4;|
1Research Unit of Medical Imaging, Physics and Technology, University of Oulu, Oulu, Finland
2Medical Research Center Oulu, Oulu University Hospital and University of Oulu, Oulu, Finland
3Department of Physical and Rehabilitation Medicine, Rehabilitation Services of South Karelia Social and Health Care District, Lappeenranta, Finland
4Department of Occupational Health, Finnish Institute of Occupational Health, Oulu, Finland
5Department of Diagnostic Radiology, Oulu University Hospital, Oulu, Finland
|Online Access:||PDF Full Text (PDF, 2.3 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2021111154700
John Wiley & Sons,
|Publish Date:|| 2021-11-11
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 T₂-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 T₂-weighted magnetic resonance images can be applied in low back pain classification.
Journal of orthopaedic research
|Pages:||2428 - 2438|
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
3126 Surgery, anesthesiology, intensive care, radiology
We gratefully acknowledge support from the Technology Industries of Finland Centennial Foundation and Jane & Aatos Erkko Foundation funds (the Future Makers –program), as well as personal grants from the Tauno Tönning Foundation.
© 2020 The Authors. Journal of Orthopaedic Research® published by Wiley Periodicals LLC on behalf of Orthopaedic Research Society. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.