Radiomics for predicting revised hematoma expansion with the inclusion of intraventricular hemorrhage growth in patients with supratentorial spontaneous intraparenchymal hematomas

• Published in: Annals of translational medicine 2022-01, Vol.10 (1), p.8-8
• Main Authors: Xia, Xiaona, Ren, Qingguo, Cui, Jiufa, Dong, Hao, Huang, Zhaodi, Jiang, Qingjun, Guan, Shuai, Huang, Chencui, Yin, Jihan, Xu, Jingxu, Liang, Kongming, Wang, Hao, Han, Kai, Meng, Xiangshui
• Format: Article
• Language: English
• Subjects: Original
• ISSN: 2305-5839; 2305-5839
• DOI: 10.21037/atm-21-6158

Previous radiomics analyses of hematoma expansion have been based on the traditional definition, which only focused on changes in intraparenchymal volume. However, the ability of radiomics-related models to predict revised hematoma expansion (RHE) with the inclusion of intraventricular hemorrhage expansion remains unclear. To develop and validate a noncontrast computed tomography (NCCT)-based clinical- semantic-radiomics nomogram to identify supratentorial spontaneous intracerebral hemorrhage (sICH) patients with RHE on admission. In this double-center retrospective study, data from 376 patients with sICH (training set: n=299; test set: n=77; external validation cohort: n=91) were reviewed. A radiomics model, a clinical-semantic model, and a combined model were then constructed based on the logistic regression machine learning approach. Radiomics features were extracted and selected by least absolute shrinkage and selection operator (LASSO) with 5-fold cross validation. Furthermore, the classical BRAIN scoring system was also constructed to predict RHE. Discriminative performance of the models was evaluated on the training and test set with area under the curve (AUC) and decision curve analysis (DCA). The addition of radiomics to clinical-semantic factors significantly improved the prediction performance of RHE compared with the clinical-semantic model alone in the training (AUC, 0.94 0.81, P