Prediction of Intraparenchymal Hemorrhage Progression and Neurologic Outcome in Traumatic Brain Injury Patients Using Radiomics Score and Clinical Parameters

Abstract

(1) Background: Radiomics analysis of spontaneous intracerebral hemorrhages on computed tomography (CT) images has been proven effective in predicting hematoma expansion and poor neurologic outcome. In contrast, there is limited evidence on its predictive abilities for traumatic intraparenchymal hemorrhage (IPH). (2) Methods: A retrospective analysis of 107 traumatic IPH patients was conducted. Among them, 45 patients (42.1%) showed hemorrhagic progression of contusion (HPC) and 51 patients (47.7%) had poor neurological outcome. The IPH on the initial CT was manually segmented for radiomics analysis. After feature extraction, selection and repeatability evaluation, several machine learning algorithms were used to derive radiomics scores (R-scores) for the prediction of HPC and poor neurologic outcome. (3) Results: The AUCs for R-scores alone to predict HPC and poor neurologic outcome were 0.76 and 0.81, respectively. Clinical parameters were used to build comparison models. For HPC prediction, variables including age, multiple IPH, subdural hemorrhage, Injury Severity Score (ISS), international normalized ratio (INR) and IPH volume taken together yielded an AUC of 0.74, which was significantly (p = 0.022) increased to 0.83 after incorporation of the R-score in a combined model. For poor neurologic outcome prediction, clinical variables of age, Glasgow Coma Scale, ISS, INR and IPH volume showed high predictability with an AUC of 0.92, and further incorporation of the R-score did not improve the AUC. (4) Conclusion: The results suggest that radiomics analysis of IPH lesions on initial CT images has the potential to predict HPC and poor neurologic outcome in traumatic IPH patients. The clinical and R-score combined model further improves the performance of HPC prediction.

Enhancing hospital course and outcome prediction in patients with traumatic brain injury: A machine learning study

“…Furthermore, there are studies describing models to predict the functional outcome or The Glasgow Outcome Scale-Extended, 5,[25][26][27][28][29] and more recent studies using images as inputs. 8,30,31 Furthermore, there are studies in the literature similar to ours that describe models for predicting in-hospital mortality, 29,[32][33][34][35] early mortality, [36][37][38] discharge position, 39,40 need for hospital admission, 6 emergency neurosurgery, 41 and length of hospital stay. 4 In addition to contributing to the body of knowledge by describing the efficacy of incorporating ML into patient care to predict multiple outcomes simultaneously in TBI patients, this study is unique since it has used blood biomarkers such as GFAP and UCH-L1, and non-contrast CT CDEs as input variables.…”

Diffusion Kurtosis Imaging and Radiomics in Diffuse Axonal Injury

“…Цель: оценить информативность параметров радиомики по данным диффузионно-куртозисной магнитно-резонансной томографии для выявления микроструктурных повреждений головного мозга у пациентов при диффузном аксональном повреждении и прогнозирования его исходов. Работ с использованием радиомики при черепно-мозговой травме крайне мало [8][9][10][13][14][15][16], и в основном они…”

The Application Value of CT Radiomics Combined with Clinical Parameters in Predicting the Duration of Invasive Mechanical Ventilation in Patients with Traumatic Brain Injury

“…At the same time, by complementing with other information such as clinical or laboratory data, radiomics improves diagnostic e ciency to an acceptable level for clinical practice. Radiomics has been widely applied in clinical research of the central nervous system, such as predicting hematoma expansion in hypertensive intracerebral hemorrhage, 9,10 predicting hematoma expansion in traumatic brain injury, 11,12 etiological classi cation of intracranial aneurysm rupture, 13 and etiological classi cation of intracranial hemorrhage. 14 Although radiomics has been widely used in central nervous system research, there have been no reported studies on predicting the duration of invasive mechanical ventilation.…”