A study in identifying potential vertebral fragility fracture risk based on MRI radiomics models of vertebrae and paraspinal muscles
10.3760/cma.j.cn112149-20240904-00539
- VernacularTitle:基于椎体和椎旁肌MRI影像组学模型识别椎体潜在脆性骨折风险的研究
- Author:
Yi YANG
1
;
Qianyi QIU
1
;
Yinxia ZHAO
1
;
Jiayi LUO
1
;
Xinru ZHANG
1
;
Qinglin XIE
1
;
Yiou WANG
1
;
Xiaodong ZHANG
1
Author Information
1. 南方医科大学第三附属医院医学影像科，广州 510630
- Publication Type:Journal Article
- Keywords:
Osteoporosis;
Osteopenia;
Fracture risk;
Magnetic resonance imaging;
Radiomics
- From:
Chinese Journal of Radiology
2025;59(9):1063-1070
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To explore the application value of radiomics models based on MRI of vertebrae and paravertebral muscles in identifying potential vertebral fragility fracture risk in osteoporosis and osteopenia.Methods:This cross-sectional study collected data from patients who underwent both dual-energy X-ray absorptiometry (DXA) and lumbar MRI at the Third Affiliated Hospital of Southern Medical University between January 2014 and December 2023,retrospectively. Based on DXA results, patients were categorized into osteoporosis group ( n=302) and osteopenia group ( n=264), with fracture and non-fracture patients matched at 1∶1 ratio by propensity score matching based on age, gender, and body mass index. The fourth lumbar vertebra was selected as the region of interest (ROI) for the vertebral body, and the bilateral psoas major, erector spinae, and multifidus muscles were selected as the ROIs for the paraspinal muscles. A total of 7 259 radiomics features were extracted from these ROIs. The dataset was divided into a training set and a test set in an 8∶2 ratio by simple random sampling (osteoporosis group 241 and 61 cases, osteopenia group 211 and 53 cases). The T-score was used to establish the clinical model. After feature normalization and dimensionality reduction, logistic regression was applied to build three radiomics models: vertebral model, paraspinal muscle model, and vertebral-paraspinal muscle model. The T-score was then combined with the radiomics model that achieved the highest area under the receiver operating characteristic curve (AUC) in the test set to construct a clinical-radiomics combined model. Model performance was evaluated using the AUC. The DeLong test was used to compare the diagnostic efficacy between models. Results:In the test set, the vertebral-paravertebral muscle model achieved the highest AUC among radiomics models and was selected for combination with the T-score. In identifying potential vertebral fragility fractures of osteoporosis group, the AUC (95% CI) of the clinical model, vertebral model, paraspinal muscle model, vertebral-paraspinal muscle model, and clinical-radiomics model were 0.523 (0.373-0.672), 0.869 (0.779-0.959), 0.608 (0.464-0.752), 0.876 (0.791-0.961), and 0.860 (0.769-0.952), respectively. For osteopenia group, the corresponding AUC(95% CI) were 0.625 (0.467-0.783), 0.696 (0.547-0.845), 0.706 (0.563-0.848), 0.816 (0.702-0.930), and 0.820 (0.710-0.930). The DeLong test showed that the vertebral model for identifying the potential vertebral fracture risk in osteoporosis group had better performance than the paraspinal muscle model ( Z=3.28, P=0.001). While for osteopenia group, there was no significant difference in diagnostic performance between the vertebral model and the paraspinal muscle model ( Z=0.09, P=0.932). The recognition efficacy of the clinical model and the vertebral-paraspinal muscle model was significantly different ( Z=3.69, 1.98; P<0.001, P=0.047), while there was no significant difference between the clinical-radiomics combined model and the vertebral-paraspinal muscle model ( Z=1.51, 0.12; P=0.131, 0.904). Conclusion:The MRI-based vertebral-paraspinal muscle radiomics model can effectively identify osteoporosis or osteopenia patients with potential fragility fracture risk. In osteopenia group, the efficacy of the MRI radiomics models based on the vertebra and paraspinal muscles in identifying potential vertebral fragility fracture risk is comparable.
