# Current Applications and Development of Radiomics in Osteoporosis: A Narrative Review
## 5 Radiomics in Osteoporotic Fractures Classification and Fracture Risk Prediction  
### Osteoporotic Fractures Classification  
Comparative analysis of radiomics features between individuals with osteoporosis and a history of fragility fractures (fracture cohort) and healthy controls without osteoporosis or fracture history (non-fracture cohort) held critical value. This approach enabled the identification of imaging biomarkers for osteoporoptic fracture detection and may elucidate predictive signatures characterizing fracture risk. Analysis of vertebral MRI images revealed that the trabecular bone texture in the fracture cohort exhibited more heterogeneity compared to the non-fracture cohort. This heterogeneity was quantitatively described by radiomics features, showing 76% higher contrast, 10% higher entropy, and 50% lower angular second moment [78]. Bone texture features correlated with microstructural parameters such as bone volume fraction, trabecular number, trabecular separation, and fractal dimension [79]. Therefore, radiomics offered a more comprehensive method of fracture classification by enabling high-dimensional, quantitative analysis of texture features, thereby enhancing fracture risk evaluation.  
Radiomics features extracted from various imaging modalities have demonstrated promising efficacy in classifying fractures at multiple anatomical sites. In a study comparing bone volume fraction and texture features obtained from CT images, the low-energy acetabular fracture cohort exhibited lower bone volume fraction compared to the non-fracture cohort. Significant differences (\(p<0.05\)) were also observed in the femoral head texture characteristics between the two cohorts [80]. Similarly, texture features extracted from clinical opportunistic CT and MRI images, when combined with machine learning algorithms, were effective in distinguishing the vertebral fracture cohort from the non -fracture cohort, achieving AUC values surpassing 0.9 [81]. These findings indicated that radiomics features offered supplementary bone quality information beyond areal BMD, enhancing fracture identification. In patients with osteopororotic femoral fractures, MRI-based radiomic features, including lower kurtosis, reduced low gray-level emphasis, and higher dependent non-uniformity, were linked to changes in trabecular bone microstructure. These features demonstrated potential for detecting femoral fractures [82]. Furthermore, in a study utilizing thoracolumbar and lumbar spine radiographs from three hospitals, features were extracted using pre-trained deep transfer learning algorithms. A robust classification model for vertebral compression fractures was developed by integrating these extracted features with radiomics features [83]. When combined with clinical baseline data, the classification performance was further enhanced [84].  
The incorporation of imaging features that quantitatively describe the spatial heterogeneity of bone marrow across different populations was important for improving fracture classification. A study comparing vertebral MRI texture features from premenopausal and postmenopausal women revealed significant differences (\(p<0.05\)) in bone marrow spatial heterogeneity, further helping the classification of osteopororotic fractures and non-fractures [85; 86]. The mean proton density fat fraction effectively quantified spatial heterogeneity in bone marrow. Therefore, a fracture classification model in combination with proton density fat fraction, volumetric BMD, and two additional texture features derived from chemical shift encoding-based water-fat MRI and CT images demonstrated excellent performance [85]. In summary, the combination of multimodal image features and radiomics helped to improve the accuracy of osteopororotic fracture detection and fracture risk prediction.