Combined Radiomics Model for Prediction of Hematoma Progression and Clinical Outcome of Cerebral Contusions in Traumatic Brain Injury

Predicting Hematoma Expansion and Prognosis in Cerebral Contusions: A Radiomics-Clinical Approach

Hemorrhagic progression of contusion (HPC) often occurs early in cerebral contusions (CC) patients, significantly impacting their prognosis. It is vital to promptly assess HPC and predict outcomes for effective tailored interventions, thereby enhancing prognosis in CC patients. We utilized the Attention-3DUNet neural network to semi-automatically segment hematomas from computed tomography (CT) images of 452 CC patients, incorporating 695 hematomas. Subsequently, 1502 radiomic features were extracted from 358 hematomas in 261 patients. After a selection process, these features were used to calculate the radiomic signature (Radscore). The Radscore, along with clinical features such as medical history, physical examinations, laboratory results, and radiological findings, was employed to develop predictive models. For prognosis (discharge Glasgow Outcome Scale score), radiomic features of each hematoma were augmented and fused for correlation. We employed various machine learning methodologies to create both a combined model, integrating radiomics and clinical features, and a clinical-only model. Nomograms based on logistic regression were constructed to visually represent the predictive procedure, and external validation was performed on 170 patients from three additional centers. The results showed that for HPC, the combined model, incorporating hemoglobin levels, Rotterdam CT score of 3, multi-hematoma fuzzy sign, concurrent subdural hemorrhage, international normalized ratio, and Radscore, achieved area under the receiver operating characteristic curve (AUC) values of 0.848 and 0.836 in the test and external validation cohorts, respectively. The clinical model predicting prognosis, utilizing age, Abbreviated Injury Scale for the head, Glasgow Coma Scale Motor component, Glasgow Coma Scale Verbal component, albumin, and Radscore, attained AUC values of 0.846 and 0.803 in the test and external validation cohorts, respectively. Selected radiomic features indicated that irregularly shaped and highly heterogeneous hematomas increased the likelihood of HPC, while larger weighted axial lengths and lower densities of hematomas were associated with a higher risk of poor prognosis. Predictive models that combine radiomic and clinical features exhibit robust performance in forecasting HPC and the risk of poor prognosis in CC patients. Radiomic features complement clinical features in predicting HPC, although their ability to enhance the predictive accuracy of the clinical model for adverse prognosis is limited.

Whole-brain traumatic controlled cortical impact to the left frontal lobe: Magnetic resonance image-based texture analysis

This research assesses the capability of texture analysis (TA) derived from high-resolution (HR) T2-weighted magnetic resonance imaging to identify primary sequelae following 1-5 hours of controlled cortical impact mild or severe traumatic brain injury (TBI) to the left frontal cortex (focal impact) and secondary (diffuse) sequelae in the right frontal cortex, bilateral corpus callosum, and hippocampus in rats. The TA technique comprised first-order (histogram-based) and second-order statistics (including gray-level co-occurrence matrix, gray-level run length matrix, and neighborhood gray-level difference matrix). Edema in the left frontal impact region developed within 1 hour and continued throughout the 5-hour assessments. The TA features from HR images confirmed the focal injury. There was no significant difference among radiomics features between the left and right corpus callosum or hippocampus from 1 to 5 hours following a mild or severe impact. The adjacent corpus callosum region and the distal hippocampus region (s), showed no diffuse injury 1-5 hours after mild or severe TBI. These results suggest that combining HR images with TA may enhance detection of early primary and secondary sequelae following TBI.

A Clinical-Radiomics Based Nomogram to Predict Progressive Intraparenchymal Hemorrhage in Mild to Moderate Traumatic Injury Patients

PURPOSE

To develop a non-contrast computed tomography(NCCT)based radiomics model for predicting intraparenchymal hemorrhage progression in patients with mild to moderate traumatic brain injury(TBI).

METHODS

We retrospectively analyzed 166 mild to moderate TBI patients with intraparenchymal hemorrhage from January 2018 to December 2021. The enrolled patients were divided into training cohort and test cohort with a ratio of 6:4. Uni- and multivariable logistic regression analyses were implemented to screen clinical-radiological factors and to establish a clinical-radiological model. Model performance was evaluated by the area under the receiver operating characteristic curve (AUC), the calibration curve, the decision curve analysis, sensitivity, and specificity.

RESULTS

Eleven radiomics features, presence with SDH, and D-dimer > 5 mg/l were selected to construct the combined clinical-radiomic model for the prediction of TICH in mild to moderate TBI patients. The AUC of the combined model was 0.81(95% confidence interval (CI), 0.72 to 0.90) in the training cohort and 0.88 (95% CI 0.79 to 0.96) in the test cohort, which were superior to the clinical model alone (AUC_training = 0.72, AUC_test = 0.74). The calibration curve demonstrated that the radiomics nomogram had a good agreement between prediction and observation. Decision curve analysis confirmed clinically useful.

CONCLUSIONS

The combined clinical-radiomic model that incorporates the radiomics score and clinical risk factors can serve as a reliable and powerful tool for Predicting intraparenchymal hemorrhage progression for patients with mild to moderate TBI.

Non-Invasive Assessment of Intracranial Hypertension in Patients with Traumatic Brain Injury Using Computed Tomography Radiomic Features: A Pilot Study

This study aimed to assess intracranial hypertension in patients with traumatic brain injury non-invasively using computed tomography (CT) radiomic features. Fifty patients from the primary cohort were enrolled in this study. The clinical data, pre-operative cranial CT images, and initial intracranial pressure readings were collected and used to develop a prediction model. Data of 20 patients from another hospital were used to validate the model. Clinical features including age, sex, midline shift, basilar cistern status, and ventriculocranial ratio were measured. Radiomic features-i.e., 18 first-order and 40 second-order features- were extracted from the CT images. LASSO method was used for features filtration. Multi-variate logistic regression was used to develop three prediction models with clinical (CF model), first-order (FO model), and second-order features (SO model). The SO model achieved the most robust ability to predict intracranial hypertension. Internal validation showed that the C-statistic of the model was 0.811 (95% confidence interval [CI]: 0.691-0.931) with the bootstrapping method. The Hosmer Lemeshow test and calibration curve also showed that the SO model had excellent performance. The external validation results showed a good discrimination with an area under the curve of 0.725 (95% CI: 0.500-0.951). Although the FO model was inferior to the SO model, it had better prediction ability than the CF model. The study shows that the radiomic features analysis, especially second-order features, can be used to evaluate intracranial hypertension non-invasively compared with conventional clinical features, given its potential for clinical practice and further research.

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

(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.

Preventive strategies for feeding intolerance among patients with severe traumatic brain injury: A cross-sectional survey

Objectives

This study aimed to investigate the application status of preventive measures for feeding intolerance in patients with severe traumatic brain injury (STBI) in China and analysis the differences and their causes.

Methods

A cross-sectional survey was conducted. From December 2019 to January 2020, ICU nurses and physicians of 89 hospitals in China were surveyed by using a questionnaire on preventive strategies for feeding intolerance in patients with STBI. The questionnaire included two parts: the general information of participants (10 items) and application of preventive measures for feeding intolerance in STBI patients (18 items).

Results

Totally 996 nurses and physicians completed the questionnaire. Among various methods, gastrointestinal symptoms(85.0%) and injury severity (71.4%) were mostly used to assess gastrointestinal functions and risk of feeding intolerance among STBI patients, respectively. Initiating enteral nutrition (EN) within 24–48 h (61.5%), nasogastric tubes (91.2%), 30°–45° of head-of-bed elevation (89.5%), continuous feeding by pump (72.9%), EN solution temperature of 38–40 °C (65.5%), <500 ml initial volume of EN solution (50.0%), monitoring gastric residual volume with a syringe (93.7%), and assessing gastric residual volume every 4 h (51.5%) were mostly applied for EN delivery among STBI patients. Prokinetic agents (73.3%), enema (73.6%), probiotics (79.0%), antacid agents (84.1%), and non-nutritional preparations as initial EN formula (65.6%) were commonly used for preventing feeding intolerance among STBI patients.

Conclusions

The survey showed that nurses and clinicians in China have a positive attitude towards preventive strategies for feeding intolerance. However, some effective new technologies and methods have not been timely applied in clinical practice. We suggest that managers, researchers, clinicians, nurses, and other health professionals should collaborate to explore effective and standard preventive strategies for feeding intolerance among patients with STBI.

A Clinical Predictive Nomogram for Traumatic Brain Parenchyma Hematoma Progression

INTRODUCTION

Acute traumatic intraparenchymal hematoma (tICH) expansion is a major cause of clinical deterioration after brain contusion. Here, an accurate prediction tool for acute tICH expansion is proposed.

METHODS

A multicenter hospital-based study for multivariable prediction model was conducted among patients (889 patients in a development dataset and 264 individuals in an external validation dataset) with initial and follow-up computed tomography (CT) imaging for tICH volume evaluation. Semi-automated software was employed to assess tICH expansion. Two multivariate predictive models for acute tICH expansion were developed and externally validated.

RESULTS

A total of 198 (22.27%) individuals had remarkable acute tICH expansion. The novel Traumatic Parenchymatous Hematoma Expansion Aid (TPHEA) model retained several variables, including age, coagulopathy, baseline tICH volume, time to baseline CT time, subdural hemorrhage, a novel imaging marker of multihematoma fuzzy sign, and an inflammatory index of monocyte-to-lymphocyte ratio. Compared with multihematoma fuzzy sign, monocyte-to-lymphocyte ratio, and the basic model, the TPHEA model exhibited optimal discrimination, calibration, and clinical net benefits for patients with acute tICH expansion. A TPHEA nomogram was subsequently introduced from this model to facilitate clinical application. In an external dataset, this device showed good predicting performance for acute tICH expansion.

CONCLUSIONS

The main predictive factors in the TPHEA nomogram are the monocyte-to-lymphocyte ratio, baseline tICH volume, and multihematoma fuzzy sign. This user-friendly tool can estimate acute tICH expansion and optimize personalized treatments for individuals with brain contusion.