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

被引:15
作者
Salazar, Pascal [1 ]
Di Napoli, Mario [2 ]
Jafari, Mostafa [3 ]
Jafarli, Alibay [3 ]
Ziai, Wendy [4 ]
Petersen, Alexander [5 ]
Mayer, Stephan A. [6 ]
Bershad, Eric M. [7 ]
Damani, Rahul [7 ]
Divani, Afshin A. [3 ,8 ]
机构
[1] Vital Images, Minnetonka, MN USA
[2] San Camillo de Lellis Dist Gen Hosp, Dept Neurol, Rieti, Italy
[3] Univ Minnesota, Dept Neurol, MMC 295,420 Delaware St SE, Minneapolis, MN 55455 USA
[4] Johns Hopkins, Dept Neurol Neurosurg & Anesthesia Crit Care Med, Baltimore, MD USA
[5] Univ Calif Santa Barbara, Dept Stat & Appl Probabil, Santa Barbara, CA 93106 USA
[6] Henry Ford Hlth Syst, Dept Neurol, Detroit, MI USA
[7] Baylor Coll Med, Dept Neurol, Houston, TX 77030 USA
[8] Univ Minnesota, Dept Neurosurg, Minneapolis, MN 55455 USA
基金
美国国家科学基金会;
关键词
Intracerebral hemorrhage; Hematoma volume; Hematoma shape; Computed tomography; CT density; Radiologic predictors; Outcomes; COMPUTED-TOMOGRAPHY; VOLUME MEASUREMENT; STROKE; SHAPE; DENSITY; SCORE;
D O I
10.1007/s12028-019-00783-8
中图分类号
R4 [临床医学];
学科分类号
1002 ; 100602 ;
摘要
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.
引用
收藏
页码:539 / 549
页数:11
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