Quantitative Shape Irregularity and Density Heterogeneity Predict Hematoma Expansion in Patients With Intracerebral Hemorrhage

Zeqiang Ji; Yunyi Hao; Bin Gao; Xiaojing Zhang; Yani Zhang; Jiaokun Jia; Xue Xia; Yuhao Guo; Sijia Li; Jianwei Wu; Kaijiang Kang; Xingquan Zhao

doi:10.1002/acn3.70141

Quantitative Shape Irregularity and Density Heterogeneity Predict Hematoma Expansion in Patients With Intracerebral Hemorrhage

Ann Clin Transl Neurol. 2025 Jul 14. doi: 10.1002/acn3.70141. Online ahead of print.

Authors

Zeqiang Ji^{1

2

3}, Yunyi Hao^{1

4}, Bin Gao^{1

2}, Xiaojing Zhang^{1

2}, Yani Zhang⁵, Jiaokun Jia^{1

2}, Xue Xia^{4

6}, Yuhao Guo⁷, Sijia Li^{1

2}, Jianwei Wu^{1

2}, Kaijiang Kang^{1

2}, Xingquan Zhao^{1

2

3}

Affiliations

¹ Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
² China National Clinical Research Center for Neurological Diseases, Beijing, China.
³ Beijing Stroke Association, Beijing, China.
⁴ Department of Clinical Epidemiology and Clinical Trial, Capital Medical University, Beijing, China.
⁵ Department of Internal Medicine, MedStar Washington Hospital Center, Washington, DC, USA.
⁶ Department of Epidemiology, Beijing Neurosurgical Institute, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
⁷ Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.

PMID: 40653902
DOI: 10.1002/acn3.70141

Abstract

Purpose: This study aimed to explore the association between quantitative shape irregularity and density heterogeneity of hematomas and hematoma expansion (HE) for intracerebral hemorrhage (ICH) patients.

Methods: This cohort study included patients arriving within 24 h of symptom onset between August 2021 and July 2022 as the derivation cohort and those between July 2023 and February 2024 as the external validation cohort. HE is defined as a hematoma increase of > 6 mL or > 33% from the baseline to the follow-up CT scan between 24 and 48 h. The least absolute shrinkage and selection operator (LASSO) regression was applied to select the traditional image signs to fit the logistic regression as Model 1. Afterwards, the surface regularity index (SRI) and density coefficient of variation (DCV) of hematoma were added to form Model 2. Finally, we used the SRI and DCV to replace the selected traditional image signs as Model 3. The performance and clinical utilities were evaluated and compared in the external validation cohort.

Result: The three models demonstrated good discrimination in both the derivation cohort and the validation cohort, with Model 2 and Model 3 showing significant improvements in area under the receiver operating characteristic curve (AUROC) and in clinical utility compared to Model 1 (Model 2 AUROC: 0.859 [95% CI: 0.802, 0.926] vs. Model 1 AUROC: 0.713 [95% CI: 0.625, 0.814], Delong test p < 0.001; Model 3 AUROC: 0.840 [95% CI: 0.776, 0.912] vs. Model 1 AUROC: 0.713 [95% CI: 0.625, 0.814], p = 0.006). The SRI and DCV can improve the prediction of HE based on traditional clinical indicators and imaging signs, also serving as possible alternatives to traditional imaging signs.

Conclusions: The SRI and DCV can serve as effective substitutes for traditional imaging signs in predicting hematoma expansion.

Keywords: hematoma auto‐segmenting; hematoma expansion; hematoma surface and density characteristics; intracerebral hemorrhage.

Abstract

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