MRI-based radiomics assessment of the imminent new vertebral fracture after vertebral augmentation

Background Imminent 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. Purpose To develop and validate predictive models that integrated the radiomic features and cl...

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Bibliographic Details
Published in:European spine journal 2023-11, Vol.32 (11), p.3892-3905
Main Authors: Cai, Jinhui, Shen, Chen, Yang, Tingqian, Jiang, Yang, Ye, Haoyi, Ruan, Yaoqin, Zhu, Xuemin, Liu, Zhifeng, Liu, Qingyu
Format: Article
Language:English
Subjects:
Algorithms
Fractures
Learning algorithms
Machine learning
Magnetic resonance imaging
Medicine
Medicine & Public Health
Neurosurgery
Original Article
Osteoporosis
Prediction models
Radiomics
Risk factors
Surgical Orthopedics
Vertebrae
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Summary:Background Imminent 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. Purpose To 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 methods In 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. Results The 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  
ISSN:0940-6719
1432-0932
DOI:10.1007/s00586-023-07887-y