The Prediction of Hematoma Growth in Acute Intracerebral Hemorrhage: From 2-Dimensional Shape to 3-Dimensional Morphology.

IF 2.2 3区医学 Q3 CLINICAL NEUROLOGY

Cerebrovascular Diseases Pub Date : 2025-02-18 DOI:10.1159/000544757

Wen-Song Yang, Yi-Qing Shen, Qing-Jun Liu, Yong-Bo Ma, Jun-Meng Huang, Qing-Yuan Wu, Jing Wang, Chao-Yi Huang, Li-Bo Zhao, Qi Li, Peng Xie

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引用次数: 0

Abstract

Introduction: The relationship between the 3-dimensional morphological features of hematoma and hematoma growth (HG) remains unclear. We aimed to quantitatively assess the predictive value of 3-dimensional hematoma morphology for HG among patients with intracerebral hemorrhage (ICH).

Methods: Our study comprised 312 consecutive ICH patients. Using semi-automated volumetric analysis software, we measured hematoma volumes and delineated the region of interest. We employed Python software to extract shape features and receiver operating characteristic curve analysis to assess the predictive performance of hematoma morphology for HG. p < 0.05 was considered statistically significant.

Results: Sphericity and SurfaceArea emerged as the most effective 3-dimensional hematoma morphological predictors for HG. Optimal cutoff points relating to HG were Sphericity ≤0.56 and SurfaceArea >55 cm2. We subsequently constructed the 3-dimensional morphology models, including the probability of hematoma morphology (PHM) and the probability of comprehensive model (PCM), to predict HG. The PHM model outperformed the irregular hematoma (p = 0.007), island sign (p = 0.032), and satellite sign (p < 0.001) in predictive accuracy for HG. Among all prediction models, the PCM presented the highest predictive value for active bleeding.

Conclusions: The Sphericity ≤0.56 and SurfaceArea >55 cm2 could represent the optimal threshold for HG prediction. PHM was considered a reliable 3-dimensional morphology model for HG prediction. PCM tended to be a better model for risk stratification of active bleeding in acute ICH patients.

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急性脑出血血肿生长的预测：从二维形态到三维形态。

血肿的三维形态特征与血肿生长（HG）之间的关系尚不清楚。我们的目的是定量评估三维血肿形态学对脑出血（ICH）患者HG的预测价值。方法：我们的研究包括312例连续的脑出血患者。使用半自动体积分析软件，我们测量血肿体积并划定感兴趣的区域。采用Python软件提取形状特征，采用受试者工作特征曲线分析评估血肿形态学对HG的预测效果，P值< 0.05为有统计学意义。结果：球度和表面积是HG最有效的三维血肿形态学预测指标，与HG相关的最佳截断点是球度≤0.56和表面积≤55 cm2。随后，我们构建了三维形态学模型，包括血肿形态学概率（PHM）和综合模型概率（PCM）来预测HG， PHM模型对HG的预测精度优于不规则血肿（p = 0.007）、岛状征象（p = 0.032）和卫星征象（p < 0.001），在所有预测模型中，PCM对活动性出血的预测价值最高。结论：球度≤0.56、表面积>55 cm2可作为HG预测的最佳阈值。PHM被认为是预测HG的可靠的三维形态学模型。PCM是急性脑出血患者活动性出血风险分层的较好模型。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Cerebrovascular Diseases 医学-临床神经学

CiteScore

4.50

自引率

0.00%

发文量

审稿时长

1 months

期刊介绍： A rapidly-growing field, stroke and cerebrovascular research is unique in that it involves a variety of specialties such as neurology, internal medicine, surgery, radiology, epidemiology, cardiology, hematology, psychology and rehabilitation. ''Cerebrovascular Diseases'' is an international forum which meets the growing need for sophisticated, up-to-date scientific information on clinical data, diagnostic testing, and therapeutic issues, dealing with all aspects of stroke and cerebrovascular diseases. It contains original contributions, reviews of selected topics and clinical investigative studies, recent meeting reports and work-in-progress as well as discussions on controversial issues. All aspects related to clinical advances are considered, while purely experimental work appears if directly relevant to clinical issues.