• Jinhui Cai, Chen Shen, Tingqian Yang, Yang Jiang, Haoyi Ye, Yaoqin Ruan, Xuemin Zhu, Zhifeng Liu, and Qingyu Liu.
• Department of Radiology, The Seventh Affiliated Hospital, Sun Yat-Sen University, 628 Zhenyuan Road, Xinhu Street, Guangming New District, Shenzhen, 518107, Guangdong, China.
• Eur Spine J. 2023 Nov 1; 32 (11): 389239053892-3905.

BackgroundImminent new vertebral fracture (NVF) is highly prevalent after vertebral augmentation (VA). An accurate assessment of the imminent risk of NVF could help to develop prompt treatment strategies.PurposeTo develop and validate predictive models that integrated the radiomic features and clinical risk factors based on machine learning algorithms to evaluate the imminent risk of NVF.Materials And MethodsIn this retrospective study, a total of 168 patients with painful osteoporotic vertebral compression fractures treated with VA were evaluated. Radiomic features of L1 vertebrae based on lumbar T2-weighted images were obtained. Univariate and LASSO-regression analyses were applied to select the optimal features and construct radiomic signature. The radiomic signature and clinical signature were integrated to develop a predictive model by using machine learning algorithms including LR, RF, SVM, and XGBoost. Receiver operating characteristic curve and calibration curve analyses were used to evaluate the predictive performance of the models.ResultsThe radiomic-XGBoost model with the highest AUC of 0.93 of the training cohort and 0.9 of the test cohort among the machine learning algorithms. The combined-XGBoost model with the best performance with an AUC of 0.9 in the training cohort and 0.9 in the test cohort. The radiomic-XGBoost model and combined-XGBoost model achieved better performance to assess the imminent risk of NVF than that of the clinical risk factors alone (p < 0.05).ConclusionRadiomic and machine learning modeling based on T2W images of preoperative lumbar MRI had an excellent ability to evaluate the imminent risk of NVF after VA.© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

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