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Radiomics nomogram based on dual-energy spectral CT imaging to diagnose low bone mineral density
BACKGROUND: Osteoporosis is associated with a decrease of bone mineralized component as well as a increase of bone marrow fat. At present, there are few studies using radiomics nomogram based fat-water material decomposition (MD) images of dual-energy spectral CT as an evaluation method of abnormall...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9074261/ https://www.ncbi.nlm.nih.gov/pubmed/35524240 http://dx.doi.org/10.1186/s12891-022-05389-4 |
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author | Yao, Qianqian Liu, Mengke Yuan, Kemei Xin, Yue Qiu, Xiaoqian Zheng, Xiuzhu Li, Changqin Duan, Shaofeng Qin, Jian |
author_facet | Yao, Qianqian Liu, Mengke Yuan, Kemei Xin, Yue Qiu, Xiaoqian Zheng, Xiuzhu Li, Changqin Duan, Shaofeng Qin, Jian |
author_sort | Yao, Qianqian |
collection | PubMed |
description | BACKGROUND: Osteoporosis is associated with a decrease of bone mineralized component as well as a increase of bone marrow fat. At present, there are few studies using radiomics nomogram based fat-water material decomposition (MD) images of dual-energy spectral CT as an evaluation method of abnormally low Bone Mineral Density (BMD). This study aims to establish and validate a radiomics nomogram based the fat-water imaging of dual-energy spectral CT in diagnosing low BMD. METHODS: Ninety-five patients who underwent dual-energy spectral CT included T11-L2 and dual x-ray absorptiometry (DXA) were collected. The patients were divided into two groups according to T-score, normal BMD(T ≥ -1) and abnormally low BMD (T < -1). Radiomic features were selected from fat-water imaging of the dual-energy spectral CT. Radscore was calculated by summing the selected features weighted by their coefficients. A nomogram combining the radiomics signature and significant clinical variables was built. The ROC curve was performed to evaluate the performance of the model. Finally, we used decision curve analysis (DCA) to evaluate the clinical usefulness of the model. RESULTS: Five radiomic features based on fat-water imaging of dual-energy spectral CT were constructed to distinguish abnormally low BMD from normal BMD, and its differential performance was high with an area under the curve (AUC) of 0.95 (95% CI, 0.89–1.00) in the training cohort and 0.97 (95% CI, 0.91–1.00) in the test cohort. The radiomics nomogram showed excellent differential ability with AUC of 0.96 (95%CI, 0.91–1.00) in the training cohort and 0.98 (95%CI, 0.93–1.00) in the test cohort, which performed better than the radiomics model and clinics model only. The DCA showed that the radiomics nomogram had a higher benefit in differentiating abnormally low BMD from normal BMD than the clinical model alone. CONCLUSION: The radiomics nomogram incorporated radiomics features and clinical factor based the fat-water imaging of dual-energy spectral CT may serve as an efficient tool to identify abnormally low BMD from normal BMD well. |
format | Online Article Text |
id | pubmed-9074261 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-90742612022-05-07 Radiomics nomogram based on dual-energy spectral CT imaging to diagnose low bone mineral density Yao, Qianqian Liu, Mengke Yuan, Kemei Xin, Yue Qiu, Xiaoqian Zheng, Xiuzhu Li, Changqin Duan, Shaofeng Qin, Jian BMC Musculoskelet Disord Research BACKGROUND: Osteoporosis is associated with a decrease of bone mineralized component as well as a increase of bone marrow fat. At present, there are few studies using radiomics nomogram based fat-water material decomposition (MD) images of dual-energy spectral CT as an evaluation method of abnormally low Bone Mineral Density (BMD). This study aims to establish and validate a radiomics nomogram based the fat-water imaging of dual-energy spectral CT in diagnosing low BMD. METHODS: Ninety-five patients who underwent dual-energy spectral CT included T11-L2 and dual x-ray absorptiometry (DXA) were collected. The patients were divided into two groups according to T-score, normal BMD(T ≥ -1) and abnormally low BMD (T < -1). Radiomic features were selected from fat-water imaging of the dual-energy spectral CT. Radscore was calculated by summing the selected features weighted by their coefficients. A nomogram combining the radiomics signature and significant clinical variables was built. The ROC curve was performed to evaluate the performance of the model. Finally, we used decision curve analysis (DCA) to evaluate the clinical usefulness of the model. RESULTS: Five radiomic features based on fat-water imaging of dual-energy spectral CT were constructed to distinguish abnormally low BMD from normal BMD, and its differential performance was high with an area under the curve (AUC) of 0.95 (95% CI, 0.89–1.00) in the training cohort and 0.97 (95% CI, 0.91–1.00) in the test cohort. The radiomics nomogram showed excellent differential ability with AUC of 0.96 (95%CI, 0.91–1.00) in the training cohort and 0.98 (95%CI, 0.93–1.00) in the test cohort, which performed better than the radiomics model and clinics model only. The DCA showed that the radiomics nomogram had a higher benefit in differentiating abnormally low BMD from normal BMD than the clinical model alone. CONCLUSION: The radiomics nomogram incorporated radiomics features and clinical factor based the fat-water imaging of dual-energy spectral CT may serve as an efficient tool to identify abnormally low BMD from normal BMD well. BioMed Central 2022-05-06 /pmc/articles/PMC9074261/ /pubmed/35524240 http://dx.doi.org/10.1186/s12891-022-05389-4 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis 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 Yao, Qianqian Liu, Mengke Yuan, Kemei Xin, Yue Qiu, Xiaoqian Zheng, Xiuzhu Li, Changqin Duan, Shaofeng Qin, Jian Radiomics nomogram based on dual-energy spectral CT imaging to diagnose low bone mineral density |
title | Radiomics nomogram based on dual-energy spectral CT imaging to diagnose low bone mineral density |
title_full | Radiomics nomogram based on dual-energy spectral CT imaging to diagnose low bone mineral density |
title_fullStr | Radiomics nomogram based on dual-energy spectral CT imaging to diagnose low bone mineral density |
title_full_unstemmed | Radiomics nomogram based on dual-energy spectral CT imaging to diagnose low bone mineral density |
title_short | Radiomics nomogram based on dual-energy spectral CT imaging to diagnose low bone mineral density |
title_sort | radiomics nomogram based on dual-energy spectral ct imaging to diagnose low bone mineral density |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9074261/ https://www.ncbi.nlm.nih.gov/pubmed/35524240 http://dx.doi.org/10.1186/s12891-022-05389-4 |
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