Densitometric analysis of brain computed tomography as a new prognostic factor in patients with acute subdural hematoma

Prognostic Factors of Mortality and Functional Outcome for Acute Subdural Hematoma: A Review Article

Acute subdural hematoma (ASDH) is the most frequent intracranial traumatic lesion requiring surgery in high-income countries. To date, uncertainty remains regarding the odds of mortality or functional outcome of patients with ASDH, regardless of whether they are operated on. This review aims to shed light on the clinical and radiologic factors associated with ASDH outcome. A scoping review was conducted on Medline database from inception to 2023. This review yielded 41 patient series. In the general population, specific clinical (admission Glasgow Coma Scale [GCS], abnormal pupil exam, time to surgery, decompressive craniectomy, raised postoperative intracranial pressure) and radiologic (ASDH thickness, midline shift, thickness/midline shift ratio, uncal herniation, and brain density difference) factors were associated with mortality (grade III). Other clinical (admission GCS, decompressive craniectomy) and radiologic (ASDH volume, thickness/midline shift ratio, uncal herniation, loss of basal cisterns, petechiae, and brain density difference) factors were associated with functional outcome (grade III). In the elderly, only postoperative GCS and midline shift on brain computed tomography were associated with mortality (grade III). Comorbidities, abnormal pupil examination, postoperative GCS, intensive care unit hospitalization, and midline shift were associated with functional outcome (grade III). Based on these factors, the SHE (Subdural Hematoma in the Elderly) and the RASH (Richmond Acute Subdural Hematoma) scores could be used in daily clinical practice. This review has underlined a few supplementary factors of prognostic interest in patients with ASDH, and highlighted two predictive scores that could be used in clinical practice to guide and assist clinicians in surgical indication.

Novel CT-based parameters assessing relative cross-sectional area to guide surgical management and predict clinical outcomes in patients with acute subdural hematoma

INTRODUCTION

Acute subdural hematoma (aSDH) is one of the most devastating entities secondary to traumatic brain injury (TBI). Even though radiological computed tomography (CT) findings, such as hematoma thickness (HT), midline shift (MLS), and MLS/HT ratio, have an important prognostic role, they suffer from important drawbacks. We hypothesized that relative cross-sectional area (rCSA) of specific brain regions would provide valuable information about brain compression and swelling, thus being a key determining factor governing the clinical course.

METHODS

We performed an 8-year retrospective analysis of patients with moderate to severe TBI with surgically evacuated, isolated, unilateral aSDH. We investigated the influence of aSDH rCSA and ipsilateral hemisphere rCSA along the supratentorial region on the subsequent operative technique employed for aSDH evacuation and patient's clinical outcomes (early death and Glasgow Outcome Scale [GOS] at discharge and after 1-year follow-up). Different conventional radiological variables were also assessed.

RESULTS

The study included 39 patients. Lower HT, MLS, hematoma volume, and aSDH rCSA showed a significant association with decompressive craniectomy (DC) procedure. Conversely, higher ipsilateral hemisphere rCSA along the dorso-ventral axis and, specifically, ipsilateral hemisphere rCSA at the high convexity level were predictors for DC. CT segmentation analysis exhibited a modest relationship with early death, which was limited to the basal supratentorial subregion, but could not predict long-term outcome.

CONCLUSION

rCSA is an objectifiable and reliable radiologic parameter available on admission CT that might provide valuable information to optimize surgical treatment.

Evaluation of the prognosis of acute subdural hematoma according to the density differences between gray and white matter

Objective

Acute subdural hematoma (ASDH) is a common neurological emergency, and its appearance on head-computed tomographic (CT) imaging helps guide clinical treatment. To provide a basis for clinical decision-making, we analyzed that the density difference between the gray and white matter of the CT image is associated with the prognosis of patients with ASDH.

Methods

We analyzed the data of 194 patients who had ASDH as a result of closed traumatic brain injury (TBI) between 2018 and 2021. The patients were subdivided into surgical and non-surgical groups, and the non-surgical group was further subdivided into "diffused [hematoma]" and "non-diffused" groups. The control group's CT scans were normal. The 3D Slicer software was used to quantitatively analyze the density of gray and white matter depicted in the CT images.

Results

Imaging evaluation showed that the median difference in density between the gray and white matter on the injured side was 4.12 HU (IQR, 3.91-4.22 HU; p < 0.001) and on the non-injured side was 4.07 HU (IQR, 3.90-4.19 HU; p < 0.001), and the hematoma needs to be surgically removed. The median density difference value of the gray and white matter on the injured side was 3.74 HU (IQR, 3.53-4.01 HU; p < 0.001) and on the non-injured side was 3.71 HU (IQR, 3.69-3.73 HU; p < 0.001), and the hematoma could diffuse in a short time.

Conclusion

Quantitative analysis of the density differences in the gray and white matter of the CT images can be used to evaluate the clinical prognosis of patients with ASDH.

Hemodynamic alterations following a cerebellar arteriovenous malformation resection: Case report and densitometric quantitative analysis from CT imaging

BACKGROUND

Cerebellar arteriovenous malformations (cAVMs) are rare and challenging lesions with an aggressive natural history. The mechanisms whereby a patient can worsen clinically after a supratentorial AVM resection include an acute alteration in cerebral hemodynamics, which is a known cause of postoperative hyperemia, edema and/or hemorrhage. These phenomena has not been described for cAVMS. Moreover, the underlying pathophysiology of edema and hemorrhage after AVM resection still remains controversial.

METHODS

We report a patient that presented an abrupt neurological deterioration after cAVM surgical resection. Emergent external ventricular drainage to treat incipient hydrocephalus only partially reverted the patient's deterioration. Consecutive post-surgery CT images revealed fourth ventricle compression secondary to cerebellar swelling that concurred with a new neurological deterioration. Densitometric analysis was performed in these CT images to reveal the nature of these changes as well as their evolution over time.

RESULTS

Importantly, we demonstrated a dynamic increase in the cerebellum mean density at the interval of Hounsfield values which correspond to hyperemia values. These changes were dynamic, and when hyperemia resolved and cerebellar density returned to basal levels, the fourth ventricle re-expanded and the patient neurologically recovered.

CONCLUSIONS

This study demonstrated the utility of quantitative CT image analysis in the context of hemodynamic alterations following cAVM resection. Densitometric CT analysis demonstrated that hyperemic changes, but not ischemic ones, were time-dependent and were responsible for swelling and hemorrhage that conditioned neurological status and patient's evolution.

Intracranial Densitometry-Augmented Machine Learning Enhances the Prognostic Value of Brain CT in Pediatric Patients With Traumatic Brain Injury: A Retrospective Pilot Study

Background: The inter- and intrarater variability of conventional computed tomography (CT) classification systems for evaluating the extent of ischemic-edematous insult following traumatic brain injury (TBI) may hinder the robustness of TBI prognostic models. Objective: This study aimed to employ fully automated quantitative densitometric CT parameters and a cutting-edge machine learning algorithm to construct a robust prognostic model for pediatric TBI. Methods: Fifty-eight pediatric patients with TBI who underwent brain CT were retrospectively analyzed. Intracranial densitometric information was derived from the supratentorial region as a distribution representing the proportion of Hounsfield units. Furthermore, a machine learning-based prognostic model based on gradient boosting (i.e., CatBoost) was constructed with leave-one-out cross-validation. At discharge, the outcome was assessed dichotomously with the Glasgow Outcome Scale (favorability: 1-3 vs. 4-5). In-hospital mortality, length of stay (>1 week), and need for surgery were further evaluated as alternative TBI outcome measures. Results: Densitometric parameters indicating reduced brain density due to subtle global ischemic changes were significantly different among the TBI outcome groups, except for need for surgery. The skewed intracranial densitometry of the unfavorable outcome became more distinguishable in the follow-up CT within 48 h. The prognostic model augmented by intracranial densitometric information achieved adequate AUCs for various outcome measures [favorability = 0.83 (95% CI: 0.72-0.94), in-hospital mortality = 0.91 (95% CI: 0.82-1.00), length of stay = 0.83 (95% CI: 0.72-0.94), and need for surgery = 0.71 (95% CI: 0.56-0.86)], and this model showed enhanced performance compared to the conventional CRASH-CT model. Conclusion: Densitometric parameters indicative of global ischemic changes during the acute phase of TBI are predictive of a worse outcome in pediatric patients. The robustness and predictive capacity of conventional TBI prognostic models might be significantly enhanced by incorporating densitometric parameters and machine learning techniques.