A clinical-radiomics nomogram based on multisequence MRI for predicting intraoperative vertebral artery injury in patients with primary cervical spine tumor: a diagnostic study.
Authors
Affiliations (7)
Affiliations (7)
- Department of Orthopaedic Oncology, Second Affiliated Hospital of Naval Medical University, Shanghai, China.
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China.
- Department of Radiology, Second Affiliated Hospital of Naval Medical University, Shanghai, China.
- Department of Orthopaedic Oncology, Second Affiliated Hospital of Naval Medical University, Shanghai, China. [email protected].
- Department of Orthopaedic Oncology, Second Affiliated Hospital of Naval Medical University, Shanghai, China. [email protected].
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China. [email protected].
- Department of Radiology, Second Affiliated Hospital of Naval Medical University, Shanghai, China. [email protected].
Abstract
Currently, reliable preoperative methods for predicting vertebral artery (VA) invasion are lacking. The authors develop a novel model based on MRI radiomic signatures combined with clinical and imaging features for predicting intraoperative vertebral artery injury in patients with primary cervical tumors. Included in this retrospective study were 168 patients who received surgical resection for primary cervical tumors. They were randomly assigned to a training set (n = 117) and a test set (n = 51) . Least absolute shrinkage and selection operator logistic regression was applied for feature selection and radiomic signature construction. A multilayer perceptron (MLP) model and 10 machine learning models were used to develop diverse prediction models. Independent risk factors of clinical variables were screened by Logistic regression, based on which a clinical model was constructed. A combined model was established by combining the radiomic signatures and clinical factors. The predictive performance of the combined model was evaluated in both training and test sets using Hosmer-Lemeshow test and decision curve analysis (DCA). According to the scoring system, the MLP model obtained the highest total score of 87, meaning that its prediction performance was the best of all evaluated models, so the MLP was selected to construct the radiomics model. The AUC of the combined model in the training and test cohorts was 0.951 and 0.950 respectively, and both were higher than that of the radiomics model (AUC 0.900 in training set, p = 0.010, AUC 0.780 in test set, p = 0.001) and the clinical model (AUC 0.740 in training set, p < 0.001, AUC 0.781 in test set, p = 0.008) alone. The present study presents a nomogram that incorporates radiomic signatures and clinical features, which could be used to predict the risk of intraoperative VA injury in patients with primary cervical tumors.