Quantitative CT Improves Outcome Prediction in Acute Traumatic Brain Injury

Abstract

The admission noncontrast head computed tomography (CT) scan has been demonstrated to be one of several key early clinical and imaging features in the challenging problem of prediction of long-term outcome after acute traumatic brain injury (TBI). In this study, we employ two novel approaches to the problem of imaging classification and outcome prediction in acute TBI. First, we employ the novel technique of quantitative CT (qCT) image analysis to provide more objective, reproducible measures of the abnormal features of the admission head CT in acute TBI. We show that the incorporation of quantitative, rather than qualitative, CT features results in a significant improvement in prediction of the 6-month Extended Glasgow Outcome Scale (GOS-E) score over a wide spectrum of injury severity. Second, we employ principal components analysis (PCA) to demonstrate the interdependence of certain predictive variables. Relatively few prior studies of outcome prediction in acute TBI have used a multivariate approach that explicitly takes into account the potential covariance among clinical and CT predictive variables. We demonstrate that several predictors, including midline shift, cistern effacement, subdural hematoma volume, and Glasgow Coma Scale (GCS) score are related to one another. Rather than being independent features, their importance may be related to their status as surrogate measures of a more fundamental underlying clinical feature, such as the severity of intracranial mass effect. We believe that objective computational tools and data-driven analytical methods hold great promise for neurotrauma research, and may ultimately have a role in image analysis for clinical care.

Association of a common genetic variant within ANKK1 with six-month cognitive performance after traumatic brain injury

“…Increased precision in characterizing regional pathophysiology will enable more objective control of injury type and severity in order to distill the specific mechanism by which ANKK1 modulates working memory, as a subset of the disparate patterns of cognitive impairment observed in the current TBI classification system of mild, moderate and severe. In a broader sense, further development of classification approaches based on quantitative morphometry [70], in conjunction with appropriate computational methods [71] and data integration processes [72], will aid in deconstructing the contribution of genetic modulation to multidimensional domains of outcome after TBI.…”

Quantitative analysis of computed tomography images and early detection of cerebral edema for pediatric traumatic brain injury patients: retrospective study

“…If the images from the initial CT examinations of those eight subjects share a similar density distribution with the rest of the severe edema subjects, the early detection of cerebral edema would be possible. The basis for this assumption is that the initial non-enhanced brain CT images possess significant prognostic value regarding TBI [47]. Tests on subjects revealed that HU 24 17 was able to differentiate the two groups successfully (Figures 4 and 6).…”

“…Quantitative CT (QCT) methods have beeen mainly used in other specialties, such as cardiology and pulmonology [36][37][38][39][40][41][42][43]. Recent neuroradiological studies, which used densitometry analysis, focused on finding tumors or regional pathological changes [44][45][46][47][48]. Only a handful of studies used densitometric methods for whole brain images of abnormal brains [49][50][51][52].…”

Automated prognosis analysis for traumatic brain injury CT images

“…Yuh et al [12] studied the correlation between quantified radiographical features and GOS score, as well as the correlation between the major components and GOS score after principle component analysis (PCA). The study focused more the interdependence of the features rather than GOS prediction.…”