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A radiomics nomogram for predicting the meningioma grade based on enhanced T1WI images
Chongfeng Duan1, Xiaoming Zhou1, Jiachen Wang1
1Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China.
Objectives:
The objective of this study was to develop a radiomics nomogram for predicting the meningioma grade based on enhanced T1 weighted imaging (T1WI) images.
Methods:
188 patients with meningioma were analyzed retrospectively. There were 94 high-grade meningioma to form high-grade group and 94 low-grade meningioma were selected randomly to form low-grade group. Clinical data and MRI features were analyzed and compared. The clinical model was built by using the significant variables. The least absolute shrinkage and selection operator regression was used to select the most valuable radiomics feature. The radiomics signature was built and the Rad-score was calculated. The radiomics nomogram was developed by the significant variables of the clinical factors and Rad-score. The calibration curve and the Hosmer-Lemeshow test were used to evaluate the radiomics nomogram. Different models were compared by Delong test and decision curve analysis curve.
Results:
The sex, size and surrounding invasion were used to build clinical model. The area under the receiver operator characteristic curve (AUC) of clinical model was 0.870 (95% CI: 0.782-0.959). Nine features were used to construct the radiomics signature. The AUC of the radiomics signature was 0.885 (95% CI: 0.802-0.968). The AUC of radiomics nomogram was 0.952 (95% CI: 0.904-1). The AUC of radiomics nomogram was higher than that of clinical model and radiomics signature with a significant difference (p＜0.05). The decision curve analysis curve showed that the radiomics nomogram had a larger net benefit than the clinical model and radiomics signature.
Conclusion:
The radiomics nomogram based on enhanced T weighted imaging images for predicting the meningioma grade showed high predictive value and might contribute to the diagnosis and treatment of meningioma.
Advances In Knowledge:
1. We first constructed a radiomic nomogram to predict the meningioma grade.2. We compared the results of the clinical model, radiomics signature and radiomics nomogram.
