The value of radiomics nomogram based on CT in differentiating arteriovenous malformation cerebral hemorrhage from primary cerebral hemorrhage
10.3760/cma.j.cn112149-20201021-01174
- VernacularTitle:基于CT平扫的放射组学列线图鉴别动静脉畸形脑出血与原发性脑出血的价值
- Author:
Xing XIONG
1
;
Jia WANG
;
Yao DAI
;
Xinyi ZHA
;
Yuanqing LIU
;
Yu ZHANG
;
Chunhong HU
Author Information
1. 苏州大学附属第一医院影像科 215006
- Keywords:
Cerebral hemorrhage;
Arteriovenous malformations;
Radiomics;
Nomogram
- From:
Chinese Journal of Radiology
2021;55(8):799-804
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To develop a radiomics nomogram model based on CT to distinguish arteriovenous malformation(AVM) intracerebral hemorrhage from primary intracerebral hemorrhage.Methods:One hundred and thirty-five patients with cerebral hemorrhage confirmed by operation in the First Affiliated Hospital of Soochow University were analyzed retrospectively, including 52 patients with AVM cerebral hemorrhage and 83 patients with primary cerebral hemorrhage. Radiomics features were extracted from baseline CT, radiomics score (Radscore) was calculated and radiomic labels were constructed. Multiple logistic regression analysis was used for clinical features combined with CT signs to establish a clinical model. And then the nomogram model was generated according to the Radscore and the clinical model. The ROC curve and decision curve analysis (DCA) were used to evaluate the discrimination performance of the model.Results:Six features were selected and used to establish radiomic labels. The clinical model consisted of age (OR: 4.739, 95%CI 1.382-16.250) and hematoma location (OR: 0.111, 95%CI 0.032-0.385), while the nomogram model consisted of age, hematoma location and Radscore. In the training group, there was a significant difference between the nomogram model [area under curve (AUC) 0.912] and the clinical model (AUC 0.816), the radiomics model (AUC 0.857) ( Z=2.776, 2.034, P=0.006, 0.042, respectively); While in the validation group, there was no significant difference between the nomogram model (AUC 0.919) and the clinical model (AUC 0.788), the radiomics model (AUC 0.810) ( Z=1.796, 1.788, P=0.073, 0.074, respectively). DCA analysis showed that the clinical value of the nomogram model was superior to the clinical model and radiomic model. Conclusion:The radiomics nomogram can effectively distinguish AVM-related cerebral hemorrhage from primary cerebral hemorrhage, which is helpful for clinical decision-making.
