Noncontrast CT-based deep learning for predicting intracerebral hemorrhage expansion incorporating growth of intraventricular hemorrhage.
Journal:
Scientific reports
Published Date:
Aug 31, 2025
Abstract
Intracerebral hemorrhage (ICH) is a severe form of stroke with high mortality and disability, where early hematoma expansion (HE) critically influences prognosis. Previous studies suggest that revised hematoma expansion (rHE), defined to include intraventricular hemorrhage (IVH) growth, provides improved prognostic accuracy. Therefore, this study aimed to develop a deep learning model based on noncontrast CT (NCCT) to predict high-risk rHE in ICH patients, enabling timely intervention. A retrospective dataset of 775 spontaneous ICH patients with baseline and follow-up CT scans was collected from two centers and split into training (n = 389), internal-testing (n = 167), and external-testing (n = 219) cohorts. 2D/3D convolutional neural network (CNN) models based on ResNet-101, ResNet-152, DenseNet-121, and DenseNet-201 were separately developed using baseline NCCT images, and the activation areas of the optimal deep learning model were visualized using gradient-weighted class activation mapping (Grad-CAM). Two baseline logistic regression clinical models based on the BRAIN score and independent clinical-radiologic predictors were also developed, along with combined-logistic and combined-SVM models incorporating handcrafted radiomics features and clinical-radiologic factors. Model performance was assessed using the area under the receiver operating characteristic curve (AUC). The 2D-ResNet-101 model outperformed others, with an AUC of 0.777 (95%CI, 0.716-0.830) in the external-testing set, surpassing the baseline clinical-radiologic model and the BRAIN score (AUC increase of 0.087, p = 0.022; 0.119, p = 0.003). Compared to the combined-logistic and combined-SVM models, AUC increased by 0.083 (p = 0.029) and 0.074 (p < 0.058), respectively. The deep learning model can identify ICH patients with high-risk rHE with favorable predictive performance than traditional baseline models based on clinical-radiologic variables and radiomics features.
