AUTHOR=Xu Wen , Ding Zhongxiang , Shan Yanna , Chen Wenhui , Feng Zhan , Pang Peipei , Shen Qijun 

TITLE=A Nomogram Model of Radiomics and Satellite Sign Number as Imaging Predictor for Intracranial Hematoma Expansion

JOURNAL=Frontiers in Neuroscience

VOLUME=Volume 14 - 2020

YEAR=2020

URL=https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2020.00491

DOI=10.3389/fnins.2020.00491

ISSN=1662-453X

ABSTRACT=Background We aimed to construct and validate a nomogram model based on the combination of radiomic features and the satellite sign number for predicting intracerebral hematoma expansion.
Methods A total of 129 patients from two institutions were enrolled in this study. The preprocessed initial CT images were used for radiomic feature extraction. The ANOVA-KW test and LASSO regression were applied to identify candidate radiomic features and construct the Radscore. A nomogram model was developed by integrating Radscore with satellite sign number. The discrimination performance of the proposed model was evaluated by ROC analysis, and the predictive accuracy was assessed via calibration curve. Decision curve analysis and Kaplan-Meier survival analysis was performed to evaluate the clinical value of the model.
Results Four optimal features were ultimately selected and contributed to the Radscore construction. Positive correlation was observed between the satellite sign number and Radscore (Pearson's r: 0.451). The nomogram model showed the best performance with high AUCs in both training cohort (0.881, Sensitivity: 0.973, Specificity: 0.787) and external validation cohort (0.857, Sensitivity: 0.950, Specificity: 0.766). The calibration curve, DCA and KM analysis indicated the high accuracy and clinical usefulness of the nomogram model for hematoma expansion prediction.
Conclusions A nomogram model of integrated radiomic signature and the satellite sign number based on noncontrast CT images could serve as a reliable and convenient measurement of hematoma expansion prediction.