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2022, Vol. 26 ›› Issue (33): 5323-5328

A machine learning prediction model based on MRI radiomics for refracture of thoracolumbar segments

Liu Jin1, 2, Yin Hongkun3, Chen Guo2, Zhang Yu2, Gu Zuchao2, Tang Jing4   

  1. 1Department of Orthopedics, Chengdu Seventh People’s Hospital, Chengdu 610041, Sichuan Province, China; 2Department of Orthopedics, Chengdu First People’s Hospital, Chengdu 610041, Sichuan Province, China; 3Beijing Infervision Technology Co., Ltd., Beijing 100080, China; 4Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China

  • Received:2021-09-06 Accepted:2021-10-28 Online:2022-11-28 Published:2022-03-31

  • Contact: Tang Jing, MD, Associate chief physician, Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China

  • About author:Liu Jin, MD, Attending physician, Department of Orthopedics, Chengdu Seventh People’s Hospital, Chengdu 610041, Sichuan Province, China; Department of Orthopedics, Chengdu First People’s Hospital, Chengdu 610041, Sichuan Province, China

  • Supported by:

    Scientific Research Project of Sichuan Provincial Health Commission, No. 20PJ194 (to LJ); Scientific Research Project of Chengdu Municipal Health Commission, No. 2020133 (to LJ)


Abstract: BACKGROUND: Radiomics can be used to quantify image heterogeneity. Whether radiomics can be used to screen out features such as fingerprint from MRI images of osteoporotic vertebral bodies to predict the occurrence of new fracture is worth studying.  
OBJECTIVE: To explore the feasibility of constructing a machine learning prediction model for thoracolumbar refracture after vertebral augmentation through combining MRI radiomics features and clinical information.
METHODS: This study retrospectively collected the data of patients who were diagnosed with osteoporotic vertebral compression fracture by MRI and treated with percutaneous vertebral augmentation in Chengdu First People’s Hospital from May 2014 to April 2019. PyRadiomics was used to extract the imaging features of T1 sequences of vertebral MRI at the T11-L2 segments before percutaneous vertebral augmentation. All models were constructed in the training set, and prediction performance evaluation was performed in the validation set. Feature dimension reduction was conducted by applying least absolute shrinkage and selection operator regression. The corresponding refracture prediction models were constructed by multivariate logistic regression, random forest and adaptive lifting algorithm analysis using clinical parameters, selected features or the integrating of both. The diagnostic efficacy of the model was evaluated using the receiver operating characteristic curve. The decision analysis curve was used to compare the clinical value of each model.  
RESULTS AND CONCLUSION: (1) A total of 336 vertebrae were included in 135 patients, of which 67 vertebrae had refractures. 1 746 features were extracted from each vertebra, and 13 important features were obtained through dimension reduction. (2) Among the three models, area under curve of the combined model in the training set and validation set was significantly higher than that of the clinical model (P < 0.05), and the decision analysis curve also showed that the net benefit of the combined model in predicting thoracolumbar refracture was higher than that of the clinical model in most threshold intervals. (3) The results indicated that it was feasible to construct a refracture prediction model based on MRI T1 sequence imaging and clinical information, which could help to identify the vertebrae with high risk of refracture at early stage.
Key words: radiomics, osteoporotic, vertebral compression fracture, percutaneous vertebroplasty, refracture, machine learning, prediction model


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