# Current Applications and Development of Radiomics in Osteoporosis: A Narrative Review
## 3 Radiomics in Bone Strength Assessment  
The commonly applied clinical method for evaluating bone strength relies on BMD measurement by DXA, which may not accurately reflect actual bone strength [25]. Finite element analysis based on quantitative computed tomography (QCT) images offered a more precise evaluation of bone strength; however, the complexity, high costs, and elevated radiation exposure associated with this method presented challenges for its widespread clinical application [26]. The lumbar strength prediction model, which combined gray-scale and geometric features manually extracted from specific regions of the lumbar spine using QCT images and machine learning algorithms, provided an alternative to the QCT-based finite element analysis method for calculating lumbar spine strength [27]. Radiomics, which automatically extracted a wide range of gray-scale, geometric, and textural features, offered a less time-consuming and more user-friendly method for assessing bone strength. It is noteworthy that the application of radiomics in bone strength prediction has primarily focused on femoral strength. The femoral failure load was effectively estimated by the prediction model, which integrated the internal femoral shaft thickness with texture features extracted from standard radiographs at 200-micron resolution, such as entropy, homogeneity, and contrast from the gray-level co-occurrence matrix (GLCM). The accuracy of the texture features may be enhanced by utilizing femur images with higher resolution than the standard radiographs [22; 28]. Furthermore, the performance of the femoral failure load prediction model was improved by incorporating texture features such as fractal parameters, GLCM, and gray-scale run matrix with BMD, compared to using BMD alone. Specifically, this integration resulted in a significant increase in the coefficient of determination value from 0.74 to 0.82 (\(p<0.05\)) [29]. Similarly, the combination of the BMD and bone volume fraction from the principal tensile region with the texture feature (correlation feature computed in the GLCM) from the principal compressive region yielded superior predictive performance for failure load under stance configuration compared to individual features [30]. In a recent study, radiomics features extracted from QCT images of the proximal femur reflecting geometric, gray, and texture characteristics were utilized with a machine learning algorithm to develop a femoral strength prediction model, achieving a coefficient of determination of 0.820 and the normalized root mean square error of 10.645% [31]. The validity of the radiomics texture features in the bone strength assessment model was supported by the correlation between those features and bone structure parameters. The homogeneity of texture features derived from hip radiographs exhibited a significant correlation (\(p<0.05\)) with BMD and various bone structure parameters, such as bone volume fraction and structural model index calculated from CT images [32]. Additionally, GLCM features extracted from low-resolution MRI images were correlated with bone volume fraction, trabecular separation, and trabecular number as measured by high-resolution MRI (correlation coefficient R \(>\) 0.57) [33]. These findings suggested that radiomics features can effectively quantify bone structural characteristics, offering critical bone strength information distinct from BMD. Consequently, the bone strength assessment model incorporating radiomics features represented a non-invasive and effective method for evaluating bone strength, highlighting its potential for clinical application. When assessing femoral strength in different subjects using radiomics, it was crucial to emphasize the selection of a repeatable region of interest (ROI) that strongly correlated with femoral strength. To ensure the repeatability of the ROI, consistency was required among different operators. Furthermore, the same operator must demonstrate consistency when selecting the ROI on the same image at different times [16].