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A machine learning approach to distinguish between knees without and with osteoarthritis using MRI-based radiomic features from tibial bone
OBJECTIVES: Our aim was to assess the ability of semi-automatically extracted magnetic resonance imaging (MRI)–based radiomic features from tibial subchondral bone to distinguish between knees without and with osteoarthritis. METHODS: The right knees of 665 females from the population-based Rotterda...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8523397/ https://www.ncbi.nlm.nih.gov/pubmed/33884470 http://dx.doi.org/10.1007/s00330-021-07951-5 |
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author | Hirvasniemi, Jukka Klein, Stefan Bierma-Zeinstra, Sita Vernooij, Meike W. Schiphof, Dieuwke Oei, Edwin H. G. |
author_facet | Hirvasniemi, Jukka Klein, Stefan Bierma-Zeinstra, Sita Vernooij, Meike W. Schiphof, Dieuwke Oei, Edwin H. G. |
author_sort | Hirvasniemi, Jukka |
collection | PubMed |
description | OBJECTIVES: Our aim was to assess the ability of semi-automatically extracted magnetic resonance imaging (MRI)–based radiomic features from tibial subchondral bone to distinguish between knees without and with osteoarthritis. METHODS: The right knees of 665 females from the population-based Rotterdam Study scanned with 1.5T MRI were analyzed. A fast imaging employing steady-state acquisition sequence was used for the quantitative bone analyses. Tibial bone was segmented using a method that combines multi-atlas and appearance models. Radiomic features related to the shape and texture were calculated from six volumes of interests (VOIs) in the proximal tibia. Machine learning–based Elastic Net models with 10-fold cross-validation were used to distinguish between knees without and with MRI Osteoarthritis Knee Score (MOAKS)–based tibiofemoral osteoarthritis. Performance of the covariate (age and body mass index), image features, and combined covariate + image features models were assessed using the area under the receiver operating characteristic curve (ROC AUC). RESULTS: Of 665 analyzed knees, 76 (11.4%) had osteoarthritis. An ROC AUC of 0.68 (95% confidence interval (CI): 0.60–0.75) was obtained using the covariate model. The image features model yielded an ROC AUC of 0.80 (CI: 0.73–0.87). The model that combined image features from all VOIs and covariates yielded an ROC AUC of 0.80 (CI: 0.73–0.87). CONCLUSION: Our results suggest that radiomic features are useful imaging biomarkers of subchondral bone for the diagnosis of osteoarthritis. An advantage of assessing bone on MRI instead of on radiographs is that other tissues can be assessed simultaneously. KEY POINTS: • Subchondral bone plays a role in the osteoarthritis disease processes. • MRI radiomics is a potential method for quantifying changes in subchondral bone. • Semi-automatically extracted radiomic features of tibia differ between subjects without and with osteoarthritis. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00330-021-07951-5. |
format | Online Article Text |
id | pubmed-8523397 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-85233972021-10-22 A machine learning approach to distinguish between knees without and with osteoarthritis using MRI-based radiomic features from tibial bone Hirvasniemi, Jukka Klein, Stefan Bierma-Zeinstra, Sita Vernooij, Meike W. Schiphof, Dieuwke Oei, Edwin H. G. Eur Radiol Musculoskeletal OBJECTIVES: Our aim was to assess the ability of semi-automatically extracted magnetic resonance imaging (MRI)–based radiomic features from tibial subchondral bone to distinguish between knees without and with osteoarthritis. METHODS: The right knees of 665 females from the population-based Rotterdam Study scanned with 1.5T MRI were analyzed. A fast imaging employing steady-state acquisition sequence was used for the quantitative bone analyses. Tibial bone was segmented using a method that combines multi-atlas and appearance models. Radiomic features related to the shape and texture were calculated from six volumes of interests (VOIs) in the proximal tibia. Machine learning–based Elastic Net models with 10-fold cross-validation were used to distinguish between knees without and with MRI Osteoarthritis Knee Score (MOAKS)–based tibiofemoral osteoarthritis. Performance of the covariate (age and body mass index), image features, and combined covariate + image features models were assessed using the area under the receiver operating characteristic curve (ROC AUC). RESULTS: Of 665 analyzed knees, 76 (11.4%) had osteoarthritis. An ROC AUC of 0.68 (95% confidence interval (CI): 0.60–0.75) was obtained using the covariate model. The image features model yielded an ROC AUC of 0.80 (CI: 0.73–0.87). The model that combined image features from all VOIs and covariates yielded an ROC AUC of 0.80 (CI: 0.73–0.87). CONCLUSION: Our results suggest that radiomic features are useful imaging biomarkers of subchondral bone for the diagnosis of osteoarthritis. An advantage of assessing bone on MRI instead of on radiographs is that other tissues can be assessed simultaneously. KEY POINTS: • Subchondral bone plays a role in the osteoarthritis disease processes. • MRI radiomics is a potential method for quantifying changes in subchondral bone. • Semi-automatically extracted radiomic features of tibia differ between subjects without and with osteoarthritis. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00330-021-07951-5. Springer Berlin Heidelberg 2021-04-21 2021 /pmc/articles/PMC8523397/ /pubmed/33884470 http://dx.doi.org/10.1007/s00330-021-07951-5 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Musculoskeletal Hirvasniemi, Jukka Klein, Stefan Bierma-Zeinstra, Sita Vernooij, Meike W. Schiphof, Dieuwke Oei, Edwin H. G. A machine learning approach to distinguish between knees without and with osteoarthritis using MRI-based radiomic features from tibial bone |
title | A machine learning approach to distinguish between knees without and with osteoarthritis using MRI-based radiomic features from tibial bone |
title_full | A machine learning approach to distinguish between knees without and with osteoarthritis using MRI-based radiomic features from tibial bone |
title_fullStr | A machine learning approach to distinguish between knees without and with osteoarthritis using MRI-based radiomic features from tibial bone |
title_full_unstemmed | A machine learning approach to distinguish between knees without and with osteoarthritis using MRI-based radiomic features from tibial bone |
title_short | A machine learning approach to distinguish between knees without and with osteoarthritis using MRI-based radiomic features from tibial bone |
title_sort | machine learning approach to distinguish between knees without and with osteoarthritis using mri-based radiomic features from tibial bone |
topic | Musculoskeletal |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8523397/ https://www.ncbi.nlm.nih.gov/pubmed/33884470 http://dx.doi.org/10.1007/s00330-021-07951-5 |
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