T 2 ‐weighted magnetic resonance imaging texture as predictor of low back pain: A texture analysis‐based classification pipeline to symptomatic and asymptomatic cases
Abstract 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...
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crwiley:10.1002/jor.24973 2024-09-15T18:25:41+00:00 T 2 ‐weighted magnetic resonance imaging texture as predictor of low back pain: A texture analysis‐based classification pipeline to symptomatic and asymptomatic cases Ketola, Juuso H. J. Inkinen, Satu I. Karppinen, Jaro Niinimäki, Jaakko Tervonen, Osmo Nieminen, Miika T. Tauno Tönningin Säätiö Jane ja Aatos Erkon Säätiö Teknologiateollisuuden 100-Vuotisjuhlasäätiö 2021 http://dx.doi.org/10.1002/jor.24973 https://onlinelibrary.wiley.com/doi/pdf/10.1002/jor.24973 https://onlinelibrary.wiley.com/doi/full-xml/10.1002/jor.24973 en eng Wiley http://creativecommons.org/licenses/by/4.0/ Journal of Orthopaedic Research volume 39, issue 11, page 2428-2438 ISSN 0736-0266 1554-527X journal-article 2021 crwiley https://doi.org/10.1002/jor.24973 2024-08-01T04:22:19Z Abstract 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. Article in Journal/Newspaper Northern Finland Wiley Online Library Journal of Orthopaedic Research 39 11 2428 2438 |
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Wiley Online Library |
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English |
description |
Abstract 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. |
author2 |
Tauno Tönningin Säätiö Jane ja Aatos Erkon Säätiö Teknologiateollisuuden 100-Vuotisjuhlasäätiö |
format |
Article in Journal/Newspaper |
author |
Ketola, Juuso H. J. Inkinen, Satu I. Karppinen, Jaro Niinimäki, Jaakko Tervonen, Osmo Nieminen, Miika T. |
spellingShingle |
Ketola, Juuso H. J. Inkinen, Satu I. Karppinen, Jaro Niinimäki, Jaakko Tervonen, Osmo Nieminen, Miika T. 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_facet |
Ketola, Juuso H. J. Inkinen, Satu I. Karppinen, Jaro Niinimäki, Jaakko Tervonen, Osmo Nieminen, Miika T. |
author_sort |
Ketola, Juuso H. J. |
title |
T 2 ‐weighted magnetic resonance imaging texture as predictor of low back pain: A texture analysis‐based classification pipeline to symptomatic and asymptomatic cases |
title_short |
T 2 ‐weighted magnetic resonance imaging texture as predictor of low back pain: A texture analysis‐based classification pipeline to symptomatic and asymptomatic cases |
title_full |
T 2 ‐weighted magnetic resonance imaging texture as predictor of low back pain: A texture analysis‐based classification pipeline to symptomatic and asymptomatic cases |
title_fullStr |
T 2 ‐weighted magnetic resonance imaging texture as predictor of low back pain: A texture analysis‐based classification pipeline to symptomatic and asymptomatic cases |
title_full_unstemmed |
T 2 ‐weighted magnetic resonance imaging texture as predictor of low back pain: A texture analysis‐based classification pipeline to symptomatic and asymptomatic cases |
title_sort |
t 2 ‐weighted magnetic resonance imaging texture as predictor of low back pain: a texture analysis‐based classification pipeline to symptomatic and asymptomatic cases |
publisher |
Wiley |
publishDate |
2021 |
url |
http://dx.doi.org/10.1002/jor.24973 https://onlinelibrary.wiley.com/doi/pdf/10.1002/jor.24973 https://onlinelibrary.wiley.com/doi/full-xml/10.1002/jor.24973 |
genre |
Northern Finland |
genre_facet |
Northern Finland |
op_source |
Journal of Orthopaedic Research volume 39, issue 11, page 2428-2438 ISSN 0736-0266 1554-527X |
op_rights |
http://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.1002/jor.24973 |
container_title |
Journal of Orthopaedic Research |
container_volume |
39 |
container_issue |
11 |
container_start_page |
2428 |
op_container_end_page |
2438 |
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1810466174825136128 |