TY  - JOUR
AU  - Ouyang, Jianlong
AU  - Xu, Wei
AU  - Luo, Xianting
AU  - Yin, Xiaoping
AU  - Zhou, Yuyuan
AU  - Chen, Liang
AU  - Yin, Jiayu
AU  - Chen, Zhiyin
PY  - 2026
TI  - Machine learning-based radiomics model for predicting hematoma enlargement in spontaneous intracerebral hemorrhage: A multicenter study
JO  - Brain Hemorrhages
SN  - 2589-238X
SP  - 215
EP  - 224
VL  - 7
IS  - 4
AB  - ObjectiveThis study aimed to establish and validate three predictive models: a clinical model (CM), a radiomics model (RM), and a combined clinical–radiomics model (CRM) for forecasting hematoma progression in patients with spontaneous intracerebral hemorrhage (sICH) within 24 h of admission.MethodsA total of 477 patients were included in the training cohort (TC), while 106 patients were assigned to the validation cohort (VC). For the RM, radiomics features were first extracted, and the Least Absolute Shrinkage and Selection Operator (LASSO) regression was applied to identify the most relevant predictors. Ten different machine-learning algorithms were evaluated to determine the optimal model. The CM was developed using univariate and multivariate logistic regression analyses to identify independent clinical risk factors for hematoma expansion. The CRM was then constructed by integrating both CM and RM features.ResultsNine radiomics features were ultimately selected, and the Extra Trees algorithm was applied to build the RM. Independent clinical predictors of hematoma expansion included diabetes, the presence of the whirlpool sign, CT attenuation value, and baseline hematoma volume. In the TC, the area under the receiver operating characteristic curve (AUC) values for the CRM, RM, and CM were 0.901, 0.892, and 0.821, respectively. In the VC, the corresponding AUC values were 0.797, 0.780, and 0.786.ConclusionThe CRM demonstrated superior predictive performance compared to the individual CM and RM, offering a reliable tool for early identification of hematoma enlargement in patients with sICH.FundingThis work is funded by the Science and Technology Bureau of Jiujiang (Grant No. S2024ZDYFN0059), Jiangxi Provincial Health Commission (Grant No. SKJP1220242116).
UR  - https://doi.org/10.1016/j.hest.2025.09.001
DO  - 10.1016/j.hest.2025.09.001