Automated Quantification of Reduced Sulcal Volume Identifies Early Brain Injury After Aneurysmal Subarachnoid Hemorrhage.

BACKGROUND AND PURPOSE: Early brain injury may be a more significant contributor to poor outcome after aneurysmal subarachnoid hemorrhage (aSAH) than vasospasm and delayed cerebral ischemia. However, studying this process has been hampered by lack of a means of quantifying the spectrum of injury. Global cerebral edema (GCE) is the most widely accepted manifestation of early brain injury but is currently assessed only through subjective, qualitative or semi-quantitative means. Selective sulcal volume (SSV), the CSF volume above the lateral ventricles, has been proposed as a quantitative biomarker of GCE, but is time-consuming to measure manually. Here we implement an automated algorithm to extract SSV and evaluate the age-dependent relationship of reduced SSV on early outcomes after aSAH. METHODS: We selected all adults with aSAH admitted to a single institution with imaging within 72 hours of ictus. Scans were assessed for qualitative presence of GCE. SSV was automatically segmented from serial CTs using a deep learning-based approach. Early SSV was the lowest SSV from all early scans. Modified Rankin Scale score of 4 to 6 at hospital discharge was classified as a poor outcome. RESULTS: Two hundred forty-four patients with aSAH were included. Sixty-five (27%) had GCE on admission while 24 developed it subsequently within 72 hours. Median SSV on admission was 10.7 mL but frequently decreased, with minimum early SSV being 3.0 mL (interquartile range, 0.3-11.9). Early SSV below 5 mL was highly predictive of qualitative GCE (area under receiver-operating-characteristic curve, 0.90). Reduced early SSV was an independent predictor of poor outcome, with a stronger effect in younger patients. CONCLUSIONS: Automated assessment of SSV provides an objective biomarker of GCE that can be leveraged to quantify early brain injury and dissect its impact on outcomes after aSAH. Such quantitative analysis suggests that GCE may be more impactful to younger patients with SAH.

Automated Quantification of Compartmental Blood Volumes Enables Prediction of Delayed Cerebral Ischemia and Outcomes After Aneurysmal Subarachnoid Hemorrhage.

OBJECTIVE: The role of hemorrhage volume in risk of vasospasm, delayed cerebral ischemia (DCI), and poor outcomes after aneurysmal subarachnoid hemorrhage (SAH) is well established. However, the relative contribution of blood within individual compartments is unclear. We present an automated technique for measuring not only total but also volumes of blood in each major compartment after SAH. METHODS: We trained convolutional neural networks to identify compartmental blood (cisterns, sulci, and ventricles) from baseline computed tomography scans of patients with SAH. We compared automated blood volumes against traditional markers of bleeding (modified Fisher score [mFS], Hijdra sum score [HSS]) in 190 SAH patients for prediction of vasospasm, DCI, and functional status (modified Rankin Scale) at hospital discharge. RESULTS: Combined cisternal and sulcal volume was better correlated with mFS and HSS than cisternal volume alone (ρ = 0.63 vs. 0.58 and 0.75 vs. 0.70, P < 0.001). Only blood volume in combined cisternal plus sulcal compartments was independently associated with DCI (OR 1.023 per mL, 95% CI 1.002-1.048), after adjusting for clinical factors while ventricular blood volume was not. Total and specifically sulcal blood volume was strongly associated with poor outcome (OR 1.03 per mL, 1.01-1.06, P = 0.006 and OR 1.04, 1.00-1.08 for sulcal) as was HSS (OR 1.06 per point, 1.00-1.12, P = 0.04), while mFS was not (P = 0.24). CONCLUSIONS: An automated imaging algorithm can measure the volume of bleeding after SAH within individual compartments, demonstrating cisternal plus sulcal (and not ventricular) blood contributes to risk of DCI/vasospasm. Automated blood volume was independently associated with outcome, while qualitative grading was not.