This study aimed to develop an automated method to measure the gray-white matter ratio (GWR) from brain computed tomography (CT) scans of patients with out-of-hospital cardiac arrest (OHCA) and assess its significance in predicting early-stage neurological outcomes. Patients with OHCA who underwent brain CT imaging within 12 h of return of spontaneous circulation were enrolled in this retrospective study. The primary outcome endpoint measure was a favorable neurological outcome, defined as cerebral performance category 1 or 2 at hospital discharge. We proposed an automated method comprising image registration, K-means segmentation, segmentation refinement, and GWR calculation to measure the GWR for each CT scan. The K-means segmentation and segmentation refinement was employed to refine the segmentations within regions of interest (ROIs), consequently enhancing GWR calculation accuracy through more precise segmentations. Overall, 443 patients were divided into derivation N=265, 60% and validation N=178, 40% sets, based on age and sex. The ROI Hounsfield unit values derived from the automated method showed a strong correlation with those obtained from the manual method. Regarding outcome prediction, the automated method significantly outperformed the manual method in GWR calculation (AUC 0.79 vs. 0.70) across the entire dataset. The automated method also demonstrated superior performance across sensitivity, specificity, and positive and negative predictive values using the cutoff value determined from the derivation set. Moreover, GWR was an independent predictor of outcomes in logistic regression analysis. Incorporating the GWR with other clinical and resuscitation variables significantly enhanced the performance of prediction models compared to those without the GWR. Automated measurement of the GWR from non-contrast brain CT images offers valuable insights for predicting neurological outcomes during the early post-cardiac arrest period.

中文翻译：

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通过计算机断层扫描图像自动计算灰白质比率来预测心脏骤停的结果

本研究旨在开发一种自动化方法，通过院外心脏骤停 (OHCA) 患者的脑部计算机断层扫描 (CT) 扫描测量灰白质比 (GWR)，并评估其在预测早期神经功能障碍方面的意义。结果。在自主循环恢复后 12 小时内接受脑部 CT 成像的 OHCA 患者被纳入这项回顾性研究。主要结局指标是良好的神经系统结局，定义为出院时脑功能类别 1 或 2。我们提出了一种自动化方法，包括图像配准、K 均值分割、分割细化和 GWR 计算，以测量每次 CT 扫描的 GWR。采用 K 均值分割和分割细化来细化感兴趣区域 (ROI) 内的分割，从而通过更精确的分割来提高 GWR 计算精度。总体而言，443 名患者根据年龄和性别分为推导组 N=265（60%）和验证组 N=178（40%）。从自动化方法得出的 ROI Hounsfield 单位值与从手动方法获得的值显示出很强的相关性。关于结果预测，在整个数据集中，自动化方法在 GWR 计算中显着优于手动方法（AUC 0.79 与 0.70）。使用从推导集确定的截止值，自动化方法还证明了在敏感性、特异性以及阳性和阴性预测值方面的卓越性能。此外，GWR 是逻辑回归分析结果的独立预测因子。与没有 GWR 的预测模型相比，将 GWR 与其他临床和复苏变量相结合显着提高了预测模型的性能。从非对比脑 CT 图像中自动测量 GWR 为预测心脏骤停后早期的神经系统结果提供了宝贵的见解。