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Exploration of Multiparameter Hematoma 3D Image Analysis for Predicting Outcome After Intracerebral Hemorrhage

机译:脑出血后预测结果预测结果的探索

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Background Rapid diagnosis and proper management of intracerebral hemorrhage (ICH) play a crucial role in the outcome. Prediction of the outcome with a high degree of accuracy based on admission data including imaging information can potentially influence clinical decision-making practice. Methods We conducted a retrospective multicenter study of consecutive ICH patients admitted between 2012-2017. Medical history, admission data, and initial head computed tomography (CT) scan were collected. CT scans were semiautomatically segmented for hematoma volume, hematoma density histograms, and sphericity index (SI). Discharge unfavorable outcomes were defined as death or severe disability (modified Rankin Scores 4-6). We compared (1) hematoma volume alone; (2) multiparameter imaging data including hematoma volume, location, density heterogeneity, SI, and midline shift; and (3) multiparameter imaging data with clinical information available on admission for ICH outcome prediction. Multivariate analysis and predictive modeling were used to determine the significance of hematoma characteristics on the outcome. Results We included 430 subjects in this analysis. Models using automated hematoma segmentation showed incremental predictive accuracies for in-hospital mortality using hematoma volume only: area under the curve (AUC): 0.85 [0.76-0.93], multiparameter imaging data (hematoma volume, location, CT density, SI, and midline shift): AUC: 0.91 [0.86-0.97], and multiparameter imaging data plus clinical information on admission (Glasgow Coma Scale (GCS) score and age): AUC: 0.94 [0.89-0.99]. Similarly, severe disability predictive accuracy varied from AUC: 0.84 [0.76-0.93] for volume-only model to AUC: 0.88 [0.80-0.95] for imaging data models and AUC: 0.92 [0.86-0.98] for imaging plus clinical predictors. Conclusions Multiparameter models combining imaging and admission clinical data show high accuracy for predicting discharge unfavorable outcome after ICH.
机译:背景技术脑出血(ICH)的快速诊断和适当管理在结果中发挥着至关重要的作用。基于包括成像信息的准入数据的高精度预测结果可以潜在地影响临床决策实践。方法我们对2012 - 2017年间的连续ICH患者进行了回顾性的多中心研究。收集了病史,准入数据和初始头部计算断层扫描(CT)扫描。对于血肿体积,血肿密度直方图和球形指数(Si),CT扫描是半仿形的。排放不利结果被定义为死亡或严重残疾(修改的Rankin评分4-6)。我们单独进行(1)血肿体积; (2)多道琼布成像数据,包括血肿体积,位置,密度异质性,Si和中线移位; (3)多游艇仪成像数据,临床信息可用于ICH结果预测。多变量分析和预测建模用于确定血肿特征对结果的重要性。结果我们在此分析中包括430个科目。使用自动血肿分割的模型显示使用血肿体积的医院死亡率的增量预测准确性:曲线下的面积(AUC):0.85 [0.76-0.93],多游戏成像数据(血肿体积,位置,CT密度,SI和中线转移):AUC:0.91 [0.86-0.97]和多道马摄像机数据加上入学临床信息(Glasgow Coma Scale(GCS)评分和年龄):AUC:0.94 [0.89-0.99]。类似地,对于AUC的尺寸模型,对AUC的次数模型而变化的严重残疾预测精度为AUC:0.88 [0.80-0.95],用于成像数据模型和AUC:0.92 [0.86-0.98],用于成像加上临床预测因子。结论Multiparameter模型结合成像和入学临床数据显示出高精度,用于预测ICH后的放电不利结果。

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