# Current Applications and Development of Radiomics in Osteoporosis: A Narrative Review
## 4 Radiomics in Osteoporosis Diagnosis
### Radiomics Based on CT Images in Osteoporosis Diagnosis  
CT images of the chest, abdomen, and hip were frequently utilized in routine physical examination or disease follow-up. It was cost-effective to leverage these clinical opportunistic CT images for osteoporosis screening, as it avoided additional radiation exposure and enhanced screening rates for osteoporosis [38; 51]. Currently, machine learning algorithms that utilize Hounsfield Unit values or radiomics features, along with fully automated deep learning algorithms, have become widely adopted as artificial intelligence (AI)-driven tools for osteoporosis screening based on CT images [40; 52]. Hounsfield Unit values derived from CT images exhibited a moderate correlation with BMD measured by DXA, providing a useful diagnostic marker for osteoporosis [53]. The advantage of this method lies in its ability to identify patients with low BMD before orthopedic surgery, offering a fast and effective screening tool. However, radiomics features, which encompass three-dimensional geometric and textural features, have proven superior to Hounsfield Unit values in diagnosing osteoporosis, with AUC values of 0.92 and 0.84, respectively [36]. These radiomics features demonstrated strong diagnostic performance across various anatomical regions. For instance, radiomics features extracted from lumbar or hip CT images, when combined with machine learning algorithms such as random forest, \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  
* Best performance indicators represent the best performance of the best model on the testing set. ANN, Artificial neural network; AUC, Area under curve; BLS, Broad-learning system; CT, Computed tomography; DSC, Dice similarity coefficient; ET, Extra tree; GBT, Gradient-boosted tree; ICC, Intradass correlation coefficient; KNN, K-nearest neighbors; LDCT, Low-dose chest computed tomography; LightGBM, Light gradient boosting machine; LR, Logistic regression; MLP, Multilayer perceptron; MRI, Magnetic resonance imaging; NA, Not specified; NB, Naive Bayes; RF, Random forest; SDCT, Standard-dose chest computed tomography; SVM, Support vector training from \\ \hline \hline \end{tabular}  
\end{table} 
Table 1: _Cont._support vector machines, and extreme gradient boosting, have demonstrated AUC values of no less than 0.886 in osteoporosis diagnosis [40; 41]. Notably, the performance of both Hounsfield Unit values and radiomics features in osteoporosis diagnosis was influenced by the selection of ROI and CT scan parameters, such as tube voltage and reconstructed slice thickness [39; 54]. Variations in tube voltage can negatively impact the repeatability of radiomics features, reducing the model's diagnostic accuracy. Therefore, it was critical to implement the standardized processes of radiomics to maintain consistency in ROI selection and scan parameter settings for improving diagnostic reliability [43; 55]. Fully automated deep learning algorithms offered advantages in ROI selection, given their high automation and repeatability. Deep learning algorithms typically require large, labeled CT image datasets for training. However, it is challenging to obtain such high-quality clinical data [56]. Moreover, deep learning algorithms might struggle with learning texture features effectively due to their inherent higher dimensionality [57]. In contrast, radiomics excels in extracting a wide array of high-dimensional texture features, offering a unique advantage in osteoporosis diagnosis. To enhance the efficiency and accuracy of osteoporosis diagnosis, researchers have increasingly explored the integration of radiomics with deep learning algorithms [44]. Two advantages in osteoporosis diagnosis were offered by integrating radiomics with deep learning algorithms. On one hand, it effectively addressed the challenges of time consumption and low repeatability inherent in the traditional manual or semi-automatic segmentation methods by applying deep learning algorithms to ROI segmentation in radiomics [42; 58; 59].