# Current Applications and Development of Radiomics in Osteoporosis: A Narrative Review
## 6 Future Perspectives and Conclusions  
Radiomics has been successfully applied in bone strength assessment, osteoporosis diagnosis, and fracture risk prediction, contributing to enhanced performance and efficiency while demonstrating considerable clinical potential. The integration of radiomics features from multimodal images, such as CT, radiographs, and MRI, along with the incorporation of clinical features, deep learning features, and other relevant features, facilitates a more comprehensive assessment of bone quality. This multimodal method is expected to further improve the performance of radiomics in assessing bone strength, diagnosing osteoporosis, and predicting fracture risk. Geometric parameters (such as femoral neck-shaft angle, femoral head diameter, and femoral neck width) and microstructural parameters (such as bone volume fraction and trabecular number) are commonly utilized to quantitatively describe bone quality. While radiomics features derived from bone images also provide quantitative depictions of bone quality, the relationship between these radiomics features and the parameters above requires further investigation. Clarifying these relationships will facilitate better interpretation of radiomics features by clinicians, thereby enhancing the understanding, adoption, and clinical utility of radiomics in osteoporosis diagnosis and treatment. While radiomics is not intended to supplant clinical judgment, it has demonstrated considerable potential as an adjunctive tool in supporting clinical decision-making. Consequently, radiomics may facilitate the development of diagnostic, predictive, and prognostic models, thereby enhancing personalized management strategies [95; 96]. Nonetheless, several limitations have constrained its clinical translation, including the variability in feature repeatability due to differences in scanning parameters, challenges in ROI selection, the absence of multi-center external validation, and the limited clinical interpretability of radiomics features [97]. Many studies have focused on improving the reproducibility of radiomics features and associated outcomes through the development and implementation of standardized frameworks and guidelines, including the image biomarker standardization initiative (IBSI), the radiomics quality score (RQS), and the transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD) [96]. Furthermore, the facilitation of data sharing to enhance dataset robustness is crucial for advancing the clinical translation of radiomics [98]. Lastly, the integration of radiomics into routine clinical radiology workflows, including incorporation within existing radiology information systems (RIS) and picture archiving and communication systems (PACS), remains a critical challenge [20]. In summary, as challenges related to standardization, multi-center validation, technical integration, and interpretability are progressively addressed, radiomics is anticipated to evolve into a robust and efficient adjunctive tool in the clinic, with the potential to revolutionize the diagnosis, prognostication, and personalized management of diseases such as osteoporosis. Conceptualization, S.L. ; methodology, S.L. ; investigation, S.L., P.S., C.L., Q.Z., and J.Z. ; resources, S.L. ; data curation, S.L., P.S., C.L., Q.Z., and J.Z. ; writing--original draft preparation, S.L. ; writing--review and editing, S.L. and H.G. ; project administration, H.G. ; funding acquisition, H.G. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Natural Science Foundation of China (12272029), and a research grant from Hangzhou International Innovation Institute, Beihang University (2024 KQ093). Not applicable. No new data were created or analyzed in this study. Data sharing is not applicable to this article.