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Development and validation of a predictive model for vertebral fracture risk in osteoporosis patients

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Abstract

Objective

This study aimed to develop and validate a predictive model for osteoporotic vertebral fractures (OVFs) risk by integrating demographic, bone mineral density (BMD), CT imaging, and deep learning radiomics features from CT images.

Methods

A total of 169 osteoporosis-diagnosed patients from three hospitals were randomly split into OVFs (n = 77) and Non-OVFs (n = 92) groups for training (n = 135) and test (n = 34). Demographic data, BMD, and CT imaging details were collected. Deep transfer learning (DTL) using ResNet-50 and radiomics features were fused, with the best model chosen via logistic regression. Cox proportional hazards models identified clinical factors. Three models were constructed: clinical, radiomics-DTL, and fusion (clinical-radiomics-DTL). Performance was assessed using AUC, C-index, Kaplan–Meier, and calibration curves. The best model was depicted as a nomogram, and clinical utility was evaluated using decision curve analysis (DCA).

Results

BMD, CT values of paravertebral muscles (PVM), and paravertebral muscles' cross-sectional area (CSA) significantly differed between OVFs and Non-OVFs groups (P < 0.05). No significant differences were found between training and test cohort. Multivariate Cox models identified BMD, CT values of PVM, and CSAPS reduction as independent OVFs risk factors (P < 0.05). The fusion model exhibited the highest predictive performance (C-index: 0.839 in training, 0.795 in test). DCA confirmed the nomogram's utility in OVFs risk prediction.

Conclusion

This study presents a robust predictive model for OVFs risk, integrating BMD, CT data, and radiomics-DTL features, offering high sensitivity and specificity. The model's visualizations can inform OVFs prevention and treatment strategies.

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Acknowledgements

We would like to express our great appreciation to the editor and anonymous reviewers for their comments, which helped us to improve the quality of our paper. Thank American Journal Experts (www.aje.com) for editing the language of a draft of this manuscript. And for advice regarding the code used in this revised manuscript, we thank PixelmedAI platform and its developers.

Funding

This work was supported by the Science and Technology Innovation Action Project of Science and Technology Commission of Shanghai Municipality (STCSM) (20Y11911800), Medical Imaging Artificial Intelligence Special Research Fund Project, Nanjing Medical Association Radiology Branch (No.16) and Medical Imaging Artificial Intelligence Special Research Fund Project, Nanjing Medical Association Radiology Branch (No.9).

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The authors thank the relevant staff for guidance and assistance for their support and collaboration. (I) Conception and design: G Tang, L Zhang, J Zhang, L Xia; (II) Administrative support: G Tang, L Zhang, J Liu, Z Liang, J Xia; (III) Provision of study materials or patients: J Liu, Y Liu, J Tang, L Xia, W Zhang, X Zhang; (IV) Collection and assembly of data: J Zhang, X Zhang, W Zhang, J Tang, Y LIU; (V) Data analysis and interpretation: G Tang, J Zhang, X Zhang, J Liu, L Xia; (VI) Manuscript writing: All authors; (VII) Final approval of manuscript: All authors.

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Correspondence to Liang Xia, Jianguo Xia, Guangyu Tang or Lin Zhang.

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The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). This study was approved by the Institutional Ethics Committee of the Sir RunRun Hospital affiliated to Nanjing Medical University on November 25th, 2023. Because of the retrospective nature of the study, Institutional Ethics Committee of the Sir RunRun Hospital affiliated to Nanjing Medical University has waived the written informed consent procedure of this study.

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Zhang, J., Xia, L., Zhang, X. et al. Development and validation of a predictive model for vertebral fracture risk in osteoporosis patients. Eur Spine J (2024). https://doi.org/10.1007/s00586-024-08235-4

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