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Assessment of axial spondyloarthritis activity using a magnetic resonance imaging-based multi-region-of-interest fusion model
BACKGROUND: Identifying axial spondyloarthritis (axSpA) activity early and accurately is essential for treating physicians to adjust treatment plans and guide clinical decisions promptly. The current literature is mostly focused on axSpA diagnosis, and there has been thus far, no study that reported...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10668377/ https://www.ncbi.nlm.nih.gov/pubmed/38001465 http://dx.doi.org/10.1186/s13075-023-03193-6 |
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author | Xin, Peijin Wang, Qizheng Yan, Ruixin Chen, Yongye Zhu, Yupeng Zhang, Enlong Ren, Cui Lang, Ning |
author_facet | Xin, Peijin Wang, Qizheng Yan, Ruixin Chen, Yongye Zhu, Yupeng Zhang, Enlong Ren, Cui Lang, Ning |
author_sort | Xin, Peijin |
collection | PubMed |
description | BACKGROUND: Identifying axial spondyloarthritis (axSpA) activity early and accurately is essential for treating physicians to adjust treatment plans and guide clinical decisions promptly. The current literature is mostly focused on axSpA diagnosis, and there has been thus far, no study that reported the use of a radiomics approach for differentiating axSpA disease activity. In this study, the aim was to develop a radiomics model for differentiating active from non-active axSpA based on fat-suppressed (FS) T2-weighted (T2w) magnetic resonance imaging (MRI) of sacroiliac joints. METHODS: This retrospective study included 109 patients diagnosed with non-active axSpA (n = 68) and active axSpA (n = 41); patients were divided into training and testing cohorts at a ratio of 8:2. Radiomics features were extracted from 3.0 T sacroiliac MRI using two different heterogeneous regions of interest (ROIs, Circle and Facet). Various methods were used to select relevant and robust features, and different classifiers were used to build Circle-based, Facet-based, and a fusion prediction model. Their performance was compared using various statistical parameters. p < 0.05 is considered statistically significant. RESULTS: For both Circle- and Facet-based models, 2284 radiomics features were extracted. The combined fusion ROI model accurately differentiated between active and non-active axSpA, with high accuracy (0.90 vs.0.81), sensitivity (0.90 vs. 0.75), and specificity (0.90 vs. 0.85) in both training and testing cohorts. CONCLUSION: The multi-ROI fusion radiomics model developed in this study differentiated between active and non-active axSpA using sacroiliac FS T2w-MRI. The results suggest MRI-based radiomics of the SIJ can distinguish axSpA activity, which can improve the therapeutic result and patient prognosis. To our knowledge, this is the only study in the literature that used a radiomics approach to determine axSpA activity. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13075-023-03193-6. |
format | Online Article Text |
id | pubmed-10668377 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-106683772023-11-24 Assessment of axial spondyloarthritis activity using a magnetic resonance imaging-based multi-region-of-interest fusion model Xin, Peijin Wang, Qizheng Yan, Ruixin Chen, Yongye Zhu, Yupeng Zhang, Enlong Ren, Cui Lang, Ning Arthritis Res Ther Research BACKGROUND: Identifying axial spondyloarthritis (axSpA) activity early and accurately is essential for treating physicians to adjust treatment plans and guide clinical decisions promptly. The current literature is mostly focused on axSpA diagnosis, and there has been thus far, no study that reported the use of a radiomics approach for differentiating axSpA disease activity. In this study, the aim was to develop a radiomics model for differentiating active from non-active axSpA based on fat-suppressed (FS) T2-weighted (T2w) magnetic resonance imaging (MRI) of sacroiliac joints. METHODS: This retrospective study included 109 patients diagnosed with non-active axSpA (n = 68) and active axSpA (n = 41); patients were divided into training and testing cohorts at a ratio of 8:2. Radiomics features were extracted from 3.0 T sacroiliac MRI using two different heterogeneous regions of interest (ROIs, Circle and Facet). Various methods were used to select relevant and robust features, and different classifiers were used to build Circle-based, Facet-based, and a fusion prediction model. Their performance was compared using various statistical parameters. p < 0.05 is considered statistically significant. RESULTS: For both Circle- and Facet-based models, 2284 radiomics features were extracted. The combined fusion ROI model accurately differentiated between active and non-active axSpA, with high accuracy (0.90 vs.0.81), sensitivity (0.90 vs. 0.75), and specificity (0.90 vs. 0.85) in both training and testing cohorts. CONCLUSION: The multi-ROI fusion radiomics model developed in this study differentiated between active and non-active axSpA using sacroiliac FS T2w-MRI. The results suggest MRI-based radiomics of the SIJ can distinguish axSpA activity, which can improve the therapeutic result and patient prognosis. To our knowledge, this is the only study in the literature that used a radiomics approach to determine axSpA activity. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13075-023-03193-6. BioMed Central 2023-11-24 2023 /pmc/articles/PMC10668377/ /pubmed/38001465 http://dx.doi.org/10.1186/s13075-023-03193-6 Text en © The Author(s) 2023 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/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Xin, Peijin Wang, Qizheng Yan, Ruixin Chen, Yongye Zhu, Yupeng Zhang, Enlong Ren, Cui Lang, Ning Assessment of axial spondyloarthritis activity using a magnetic resonance imaging-based multi-region-of-interest fusion model |
title | Assessment of axial spondyloarthritis activity using a magnetic resonance imaging-based multi-region-of-interest fusion model |
title_full | Assessment of axial spondyloarthritis activity using a magnetic resonance imaging-based multi-region-of-interest fusion model |
title_fullStr | Assessment of axial spondyloarthritis activity using a magnetic resonance imaging-based multi-region-of-interest fusion model |
title_full_unstemmed | Assessment of axial spondyloarthritis activity using a magnetic resonance imaging-based multi-region-of-interest fusion model |
title_short | Assessment of axial spondyloarthritis activity using a magnetic resonance imaging-based multi-region-of-interest fusion model |
title_sort | assessment of axial spondyloarthritis activity using a magnetic resonance imaging-based multi-region-of-interest fusion model |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10668377/ https://www.ncbi.nlm.nih.gov/pubmed/38001465 http://dx.doi.org/10.1186/s13075-023-03193-6 |
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