# Current Applications and Development of Radiomics in Osteoporosis: A Narrative Review
## 5 Radiomics in Osteoporotic Fractures Classification and Fracture Risk Prediction
### Osteoporotic Fracture Risk Prediction  
The commonly used FRAX estimates fracture risk by evaluating seven key clinical risk factors: prior fragility fractures, parental hip fracture history, smoking, glucocorticoid use, alcohol consumption, rheumatoid arthritis, and other causes of secondary osteoporosis (e.g., age, sex, body mass index). However, FRAX did not account for important fracture risk factors such as bone microstructure and muscle mass, leading to overestimation or underestimation of fracture risk in certain populations [5; 87]. This has prompted researchers to combine imaging features that reflect bone microstructure and muscle mass features with the above clinical risk factors to improve fracture risk prediction.  
As an advanced imaging method and image analysis technique, radiomics enables the extraction of additional bone quality features that may not be discernible to the naked eye or through traditional BMD measurements. These features can serve as valuable factors in assessing fracture risk, thereby enhancing the accuracy of fracture risk predictions [88; 89]. Three-dimensional texture features extracted from high-resolution peripheral quantitative computed tomography (HR-pQCT) images of the tibia demonstrated superior performance compared to clinical factors or femoral neck BMD alone in distinguishing volunteers with a history of fractures. These texture features have the potential to serve as fracture risk factors, thereby enhancing fracture risk identification [90]. Similarly, texture features derived from femur DXA images were found to be more effective in predicting trochanteric fractures than the commonly used femoral neck T-score, resulting in an increase in the AUC from 0.784 to 0.839, which suggested that texture features obtained from DXA images were valuable in fracture risk assessment [91]. Combining FRAX scores with radiomics features and additional clinical data improved the accuracy of osteopororotic fracture predictions [92]. Therefore, by comparing radiomics features extracted from vertebral body or hip CT images, radiographs, MRI images, and HR-pQCT images of osteopororotic fractures with those of non-fracture populations, additional fracture risk factors can be identified. This method aided in enhancing the accuracy of fracture risk prediction.  
Fracture timing played a pivotal role in accurately assessing fracture risk, underlining the necessity for longitudinal studies [93]. In a prospective cohort study with a follow-up of over four years, a multivariate Cox model was developed using a radiomics score derived from the femoral DXA image. After adjusting for clinical risk factors (age, body mass index, fracture history, and femoral neck T-score), it was found that for each point increase in the radiomics score, the risk of hip fracture increased by 4% [21]. Combining radiomics scores with clinical risk factors further improved fracture risk prediction, indicating that radiomics scores were independent predictors of hip fractures. Moreover, in predicting new fractures after percutaneous vertebral augmentation, the integration of clinical parameters with radiomics features from vertebral MRI achieved strong predictive performance, outperforming models based on clinical parameters or imaging features alone [94]. Additionally, the erector spinal muscle area was independently associated with vertebral compression fractures. A nomogram incorporating the erector spinal muscle area, radiomics score, and vertebral CT values demonstrated robust predictive performance for the risk of vertebral compression fracture over three to four years [23]. Collectively, these findings underscore the value of radiomics scores derived from clinical imaging in complementing traditional tools such as FRAX, thereby improving the precision of fracture risk predictions.