Exploration of Multiparameter Hematoma 3D Image Analysis for Predicting Outcome After Intracerebral Hemorrhage
Exploration of Multiparameter Hematoma 3D Image Analysis for Predicting Outcome After Intracerebral Hemorrhage
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DOI：
10.1007/s12028-019-00783-8
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发表时间：
2020-04-01
影响因子：
3.5
通讯作者：
Divani, Afshin A.
中科院分区：
文献类型：
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作者：
Salazar, Pascal;Di Napoli, Mario;Divani, Afshin A.
背景 脑出血 (ICH) 的快速诊断和正确处理对于治疗结果起着至关重要的作用。根据包括影像信息在内的入院数据对结果进行高精度预测可能会影响临床决策实践。方法 我们对 2012 年至 2017 年间连续收治的 ICH 患者进行了回顾性多中心研究。收集病史、入院数据和初始头部计算机断层扫描 (CT) 扫描。 CT 扫描针对血肿体积、血肿密度直方图和球形指数 (SI) 进行半自动分割。出院不良结果定义为死亡或严重残疾（改良Rankin评分4-6）。我们比较了（1）单独的血肿体积； (2) 多参数影像数据，包括血肿体积、位置、密度异质性、SI、中线移位； (3) 多参数成像数据以及入院时可用于 ICH 结果预测的临床信息。使用多变量分析和预测模型来确定血肿特征对结果的重要性。结果 我们在本次分析中纳入了 430 名受试者。使用自动血肿分割的模型仅使用血肿体积显示出院内死亡率的增量预测准确性：曲线下面积 (AUC)：0.85 [0.76-0.93]，多参数成像数据（血肿体积、位置、CT 密度、SI 和中线移位）：AUC：0.91 [0.86-0.97]，以及多参数成像数据加上入院时的临床信息（格拉斯哥昏迷）量表（GCS）评分和年龄）：AUC：0.94 [0.89-0.99]。同样，严重残疾预测准确性也各不相同，从仅体积模型的 AUC：0.84 [0.76-0.93] 到成像数据模型的 AUC：0.88 [0.80-0.95] 和成像加临床预测因子的 AUC：0.92 [0.86-0.98]。结论 结合影像学和入院临床数据的多参数模型在预测 ICH 出院不良结局方面显示出较高的准确性。
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.