Development and validation of a clinical prediction model for osteoporosis diagnosis by lumbosacral X-ray and radiomics
PurposeTo develop a clinical prediction model for the diagnosis of osteoporosis using lumbosacral X-ray images through radiomics analysis.MethodsA total of 272 patients who underwent dual-energy X-ray absorptiometry (DXA) and lumbosacral X-ray examinations were categorized into two groups: (1) the t...
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Frontiers Media S.A.
2025-07-01
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| Series: | Frontiers in Aging |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fragi.2025.1476902/full |
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| author | Xiaofeng Chen Xiaofeng Chen Dongling Cai Dongling Cai Hao Li Weijun Guo Qian Li Qian Li Jinjun Liang Junxian Xie Jincheng Liu Zhen Xiang Wenxuan Dong Sihong OuYang Zhuozheng Deng Qipeng Wei |
| author_facet | Xiaofeng Chen Xiaofeng Chen Dongling Cai Dongling Cai Hao Li Weijun Guo Qian Li Qian Li Jinjun Liang Junxian Xie Jincheng Liu Zhen Xiang Wenxuan Dong Sihong OuYang Zhuozheng Deng Qipeng Wei |
| author_sort | Xiaofeng Chen |
| collection | DOAJ |
| description | PurposeTo develop a clinical prediction model for the diagnosis of osteoporosis using lumbosacral X-ray images through radiomics analysis.MethodsA total of 272 patients who underwent dual-energy X-ray absorptiometry (DXA) and lumbosacral X-ray examinations were categorized into two groups: (1) the training set (n = 191) and (2) the validation set (n = 81). Radiomic features were extracted using 3D Slicer software, and radiomic scores were calculated using the least absolute contraction and selection operator logistic regression, facilitating the generation of radiomic features. Subsequently, a clinical model, in conjunction with the radiomic features, was employed to develop a column-line diagram for the clinical and imaging feature prediction model. Performance evaluations for various models were conducted, encompassing recognition ability, accuracy, and clinical value, with the aim of identifying and optimizing prediction models.ResultsThe 12 most optimal imaging features were identified. Upon comprehensive performance analysis across different models, the clinical and radiomics model emerged as the most effective. The training set and test set area under the curves (AUCs) were 0.818 and 0.740, respectively. Additionally, the model exhibited a sensitivity and specificity of 81.6%, 80.6% and 77.5%, 73.2%, respectively.ConclusionIn this study, we developed a column-line diagram that integrates clinical and radiomics feature, presenting a novel screening tool for osteoporosis in primary hospitals. This tool aims to enhance the efficiency of osteoporosis diagnosis in primary hospitals. |
| format | Article |
| id | doaj-art-c8c5db2bc6af4695b7a9d7e15a508086 |
| institution | Matheson Library |
| issn | 2673-6217 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Aging |
| spelling | doaj-art-c8c5db2bc6af4695b7a9d7e15a5080862025-07-01T05:27:46ZengFrontiers Media S.A.Frontiers in Aging2673-62172025-07-01610.3389/fragi.2025.14769021476902Development and validation of a clinical prediction model for osteoporosis diagnosis by lumbosacral X-ray and radiomicsXiaofeng Chen0Xiaofeng Chen1Dongling Cai2Dongling Cai3Hao Li4Weijun Guo5Qian Li6Qian Li7Jinjun Liang8Junxian Xie9Jincheng Liu10Zhen Xiang11Wenxuan Dong12Sihong OuYang13Zhuozheng Deng14Qipeng Wei15Department of Orthopedics, Panyu Hospital of Chinese Medicine, Guangzhou, ChinaGuangzhou University of Chinese Medicine, Guangzhou, ChinaDepartment of Orthopedics, Panyu Hospital of Chinese Medicine, Guangzhou, ChinaGuangzhou University of Chinese Medicine, Guangzhou, ChinaDepartment of Orthopedics, Panyu Hospital of Chinese Medicine, Guangzhou, ChinaDepartment of Orthopedics, Panyu Hospital of Chinese Medicine, Guangzhou, ChinaGuangzhou University of Chinese Medicine, Guangzhou, ChinaDepartment of Dermatology, Panyu Hospital of Chinese Medicine, Guangzhou, ChinaDepartment of Orthopedics, Panyu Hospital of Chinese Medicine, Guangzhou, ChinaDepartment of Orthopedics, Panyu Hospital of Chinese Medicine, Guangzhou, ChinaGuangzhou University of Chinese Medicine, Guangzhou, ChinaGuangzhou University of Chinese Medicine, Guangzhou, ChinaGuangzhou University of Chinese Medicine, Guangzhou, ChinaGuangzhou University of Chinese Medicine, Guangzhou, ChinaGuangzhou University of Chinese Medicine, Guangzhou, ChinaDepartment of Orthopedics, Panyu Hospital of Chinese Medicine, Guangzhou, ChinaPurposeTo develop a clinical prediction model for the diagnosis of osteoporosis using lumbosacral X-ray images through radiomics analysis.MethodsA total of 272 patients who underwent dual-energy X-ray absorptiometry (DXA) and lumbosacral X-ray examinations were categorized into two groups: (1) the training set (n = 191) and (2) the validation set (n = 81). Radiomic features were extracted using 3D Slicer software, and radiomic scores were calculated using the least absolute contraction and selection operator logistic regression, facilitating the generation of radiomic features. Subsequently, a clinical model, in conjunction with the radiomic features, was employed to develop a column-line diagram for the clinical and imaging feature prediction model. Performance evaluations for various models were conducted, encompassing recognition ability, accuracy, and clinical value, with the aim of identifying and optimizing prediction models.ResultsThe 12 most optimal imaging features were identified. Upon comprehensive performance analysis across different models, the clinical and radiomics model emerged as the most effective. The training set and test set area under the curves (AUCs) were 0.818 and 0.740, respectively. Additionally, the model exhibited a sensitivity and specificity of 81.6%, 80.6% and 77.5%, 73.2%, respectively.ConclusionIn this study, we developed a column-line diagram that integrates clinical and radiomics feature, presenting a novel screening tool for osteoporosis in primary hospitals. This tool aims to enhance the efficiency of osteoporosis diagnosis in primary hospitals.https://www.frontiersin.org/articles/10.3389/fragi.2025.1476902/fullosteoporosislumbosacral X-raypredictive modelradiomicsnomogram |
| spellingShingle | Xiaofeng Chen Xiaofeng Chen Dongling Cai Dongling Cai Hao Li Weijun Guo Qian Li Qian Li Jinjun Liang Junxian Xie Jincheng Liu Zhen Xiang Wenxuan Dong Sihong OuYang Zhuozheng Deng Qipeng Wei Development and validation of a clinical prediction model for osteoporosis diagnosis by lumbosacral X-ray and radiomics Frontiers in Aging osteoporosis lumbosacral X-ray predictive model radiomics nomogram |
| title | Development and validation of a clinical prediction model for osteoporosis diagnosis by lumbosacral X-ray and radiomics |
| title_full | Development and validation of a clinical prediction model for osteoporosis diagnosis by lumbosacral X-ray and radiomics |
| title_fullStr | Development and validation of a clinical prediction model for osteoporosis diagnosis by lumbosacral X-ray and radiomics |
| title_full_unstemmed | Development and validation of a clinical prediction model for osteoporosis diagnosis by lumbosacral X-ray and radiomics |
| title_short | Development and validation of a clinical prediction model for osteoporosis diagnosis by lumbosacral X-ray and radiomics |
| title_sort | development and validation of a clinical prediction model for osteoporosis diagnosis by lumbosacral x ray and radiomics |
| topic | osteoporosis lumbosacral X-ray predictive model radiomics nomogram |
| url | https://www.frontiersin.org/articles/10.3389/fragi.2025.1476902/full |
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