European journal of radiology · 2026
Automated segmentation-based emergency CT radiomics for predicting functional outcome at discharge and in-hospital post-stroke pneumonia in primary brainstem hemorrhage.
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Li Yaguang, Wang Jiayi, Liang Yaoan, Liu Jianfeng, Jiang Wenhao, Zhou Wenshuo, Lei Licun, Wang Yong
Аннотация
BACKGROUND: Primary brainstem hemorrhage (PBSH) is associated with high mortality, disability, and uncertain early outcomes. Emergency non-contrast CT (NCCT) is the earliest imaging examination, but conventional assessment may not fully capture hematoma heterogeneity. This study aimed to develop an automated segmentation-based emergency NCCT radiomics framework for predicting poor functional outcome at discharge and in-hospital post-stroke pneumonia (PSP) in PBSH.
METHODS: This retrospective study included 372 patients with PBSH, comprising a training set (n = 182), an internal test set (n = 79), and a temporal internal validation set (n = 111). Hematomas were segmented using a three-dimensional U-Net. Manual and automated segmentation-based radiomics models were compared, and automated segmentation-derived radiomics features were integrated with conventional radiological predictors to construct combined models for poor functional outcome and PSP.
RESULTS: The automated segmentation-based radiomics model showed comparable performance to the manual segmentation-based model across all three datasets. The combined prognostic model achieved AUCs of 0.926, 0.923, and 0.858 in the training, internal test, and temporal internal validation sets, with higher AUCs and lower Brier scores than the conventional radiological model. The combined PSP model (AUCs: 0.828, 0.742, 0.681) showed no consistent improvement. SHAP identified hematoma classification as the most influential prognostic predictor.
CONCLUSIONS: Automated segmentation-based NCCT radiomics is feasible for early risk stratification in PBSH. The combined prognostic model showed good discrimination and potential complementary value beyond conventional CT assessment. The PSP model remains exploratory and requires further refinement and validation.
METHODS: This retrospective study included 372 patients with PBSH, comprising a training set (n = 182), an internal test set (n = 79), and a temporal internal validation set (n = 111). Hematomas were segmented using a three-dimensional U-Net. Manual and automated segmentation-based radiomics models were compared, and automated segmentation-derived radiomics features were integrated with conventional radiological predictors to construct combined models for poor functional outcome and PSP.
RESULTS: The automated segmentation-based radiomics model showed comparable performance to the manual segmentation-based model across all three datasets. The combined prognostic model achieved AUCs of 0.926, 0.923, and 0.858 in the training, internal test, and temporal internal validation sets, with higher AUCs and lower Brier scores than the conventional radiological model. The combined PSP model (AUCs: 0.828, 0.742, 0.681) showed no consistent improvement. SHAP identified hematoma classification as the most influential prognostic predictor.
CONCLUSIONS: Automated segmentation-based NCCT radiomics is feasible for early risk stratification in PBSH. The combined prognostic model showed good discrimination and potential complementary value beyond conventional CT assessment. The PSP model remains exploratory and requires further refinement and validation.