Published online Jan 28, 2026. doi: 10.4329/wjr.v18.i1.115504
Revised: November 17, 2025
Accepted: January 6, 2026
Published online: January 28, 2026
Processing time: 100 Days and 13.4 Hours
Spontaneous intracerebral hemorrhage (ICH) is a severe form of stroke with high early mortality, and hematoma enlargement (HE) occurs in roughly one-third of patients and strongly predicts poor outcomes. Quantitative image analysis using handcrafted radiomics and deep learning-derived features can capture hematoma and perihematomal edema (PHE) heterogeneity objectively that the combination of these approaches with clinical data may improve early prediction of HE and in-hospital mortality.
To evaluate and validate the predictive performance of hematoma- and PHE-derived features on non-contrast computed tomography via handcrafted radio
Of 322 patients with basal ganglia ICHs were included retrospectively between June 2018 and June 2020, and assigned into the training cohort (n = 225) and the testing cohort (n = 97). We extracted features on hematoma and PHE subregions via handcrafted radiomics analysis manually and deep learning analysis of pretrained convolutional neural networks via transfer learning automatically. Support vector machine was adopted as the classifier for prediction of HE and hospital mortality. The clinical-radiological integrated models for HE and hospital mortality were constructed on clinical data and radiological signatures generated from the radiological models with the optimal area under the receiver operating characteristics curve in the testing cohort.
The clinical-radiological model combining clinical information and hematoma- and PHE-derived computed tomography features for prediction of HE implied an area under the receiver operating characteristics curve of 0.828 with 95% confidence interval of 0.714 to 0.942 with accuracy of 72.89%, sensitivity of 70.00%, and specificity of 74.52% in the testing cohort. The model integrating clinical and radiological features showed great identification performance for predicting hospital mortality, demonstrating significant classification and discrimination abilities after validation.
Quantitative radiomics features from hematoma and PHE regions on non-contrast computed tomography images showed good performance for predicting HE and hospital mortality in patients with ICH.
Core Tip: In this work, we developed quantitative and easy-to-reach prediction tools for early hematoma enlargement in spontaneous intracerebral hemorrhage based on the radiological features from deep learning or handcrafted radiomics methods, and validated the predictive models in an independent cohort to assure their discriminative capacities. The artificial intelligence based computer aided diagnosis methods we used to predict hematoma enlargement in spontaneous intracerebral hemorrhage on computed tomography images would assist making decisions about whether clinicians should implement positive surgical intervention or not at early stage once admission.