Kashkoush Ahmed, Winkelman Robert, Achey Rebecca, Davison Mark A, Kshettry Varun R, Moore Nina, Hassett Catherine E, Gomes Joao, Bain Mark
Department of Neurological Surgery, Cleveland Clinic Lerner College of Medicine of Case Western Reserve University, Cleveland , Ohio , USA.
Cerebrovascular Center, Neurological Institute, Cleveland Clinic, Cleveland , Ohio , USA.
Neurosurgery. 2025 Feb 7;97(2):481-488. doi: 10.1227/neu.0000000000003368.
Identifying patients with basal ganglia intracranial hemorrhage (ICH) at risk of hematoma expansion (HE) may better define selection criteria for early surgical evacuation. The aim of this study was to use automated radiographic feature extraction to improve risk stratification for basal ganglia ICH expansion.
A single-center retrospective review was performed to identify patients with basal ganglia ICH between 2013 and 2024. ICH volumes were automatically segmented from the initial noncontrast computed tomography (CT) of the head using a custom-trained convolutional neural network. Features were quantified from the segmented ICH including stereotactic location, normalized volumetric CT density (nv-CTD, measured as mean ICH CT density divided by the background parenchymal CT density), volume, orientation, and border irregularity. HE was defined as an increase in hemorrhage volume of 10 mL or at a rate of 1.7 mL/h.
A total of 108 patients (median age 55 years, 62% male) were included. HE occurred in 24 patients (22%) and was associated with shorter duration between symptom onset and initial CT (median 1 vs 3 hours, P = .006), a lower nv-CTD (median 2.0 vs 2.2, P = .011), and a positive spot sign (41% vs 5%, P < .001). nv-CTD was positively associated with time to presentation ( R2 = 0.13, P < .001) and was negatively associated with HE in spot-sign-negative patients (median 2.0 vs 2.1, P = .016). Multivariate logistic regression modeling using nv-CTD and spot sign as inputs demonstrated improved diagnostic accuracy compared with that of the spot sign alone (area under the receiver operating characteristic curve 0.80 vs 0.68, P = .008). The area under the receiver operating characteristic curve of nv-CTD alone was 0.67 (95% CI: 0.56-0.78), which was statistically similar to that of the spot sign alone (0.68, 95% CI: 0.54-0.82) ( P = .819).
nv-CTD is a measure of bgICH acuity and can augment spot-sign bgICH expansion risk stratification.
识别有血肿扩大（HE）风险的基底节区颅内出血（ICH）患者，可能有助于更好地确定早期手术清除血肿的选择标准。本研究的目的是利用自动放射学特征提取来改善基底节区ICH扩大的风险分层。
进行了一项单中心回顾性研究，以确定2013年至2024年间患有基底节区ICH的患者。使用经过定制训练的卷积神经网络，从头部的初始非增强计算机断层扫描（CT）中自动分割出ICH体积。从分割出的ICH中量化特征，包括立体定向位置、归一化体积CT密度（nv-CTD，计算方法为平均ICH CT密度除以背景实质CT密度）、体积、方向和边界不规则性。HE定义为出血量增加10 mL或增加速率为1.7 mL/h。
共纳入108例患者（中位年龄55岁，62%为男性）。24例患者（22%）发生了HE，且与症状发作至初始CT的时间较短（中位时间1小时对3小时，P = 0.006）、较低的nv-CTD（中位值2.0对2.2，P = 0.011）以及阳性斑点征（41%对5%，P < 0.001）相关。nv-CTD与就诊时间呈正相关（R2 = 0.13，P < 0.001），在斑点征阴性的患者中与HE呈负相关（中位值2.0对2.1，P = 0.016）。以nv-CTD和斑点征作为输入的多变量逻辑回归模型显示，与单独使用斑点征相比，诊断准确性有所提高（受试者操作特征曲线下面积为0.80对0.68，P = 0.008）。单独nv-CTD的受试者操作特征曲线下面积为0.67（95%CI：0.56 - 0.78），与单独斑点征的曲线下面积（0.68，95%CI：0.54 - 0.82）在统计学上相似（P = 0.819）。
nv-CTD是衡量基底节区ICH严重程度的指标，可增强斑点征对基底节区ICH扩大风险的分层能力。
