Yamashita Koji, Hiwatashi Akio, Kondo Masatoshi, Togao Osamu, Kikuchi Kazufumi, Sugimori Hiroshi, Yoshiura Takashi, Honda Hiroshi
From the *Department of Clinical Radiology, Graduate School of Medical Sciences, Kyushu University; and Departments of †Medical Technology and ‡Emergency and Critical Care Center, Kyushu University Hospital, Fukuoka; §Department of Radiology, Kagoshima University Graduate School of Medical and Dental Sciences, Kagoshima, Japan.
J Comput Assist Tomogr. 2016 Jul-Aug;40(4):612-6. doi: 10.1097/RCT.0000000000000396.
The aim of the study was to evaluate the prognostic utility of computed tomography (CT) histogram analysis with an automated whole-brain extraction algorithm in patients with post-cardiac arrest syndrome (PCAS).
Computed tomography data from consecutive patients between January 2009 and February 2012 were obtained and retrospectively analyzed. All CT images were obtained using a 64-detector-row CT scanner with a slice thickness of 4.0 mm. A brain region was extracted from the whole-brain CT images using our original automated algorithm and used for the subsequent histogram analysis. The obtained histogram statistics (mean brain tissue CT value, kurtosis, and skewness), as well as clinical parameters, were compared between the good and poor outcome groups using the Student t test. In addition, receiver operating characteristic curve analysis was performed for the discrimination between the 2 groups for each parameter.
One hundred thirty-eight consecutive PCAS patients were enrolled. The patients were classified into good (n = 47) and poor (n = 91) outcome groups. The mean brain tissue CT value was significantly higher in the good outcome group than in the poor outcome group (P < 0.05). Kurtosis, skewness, and age were significantly lower in the good outcome group than in the poor outcome group (P < 0.0001, P < 0.05, and P < 0.05, respectively). The area-under-the-curve values for kurtosis, mean brain tissue CT value, skewness, and age were 0.751, 0.639, 0.623, and 0.626, respectively. A combination of the 4 parameters increased the diagnostic performance (area under the curve = 0.814).
Histogram analysis of whole-brain CT images with our automated extraction algorithm is useful for assessing the outcome of PCAS patients.
本研究旨在评估采用自动全脑提取算法的计算机断层扫描（CT）直方图分析在心脏骤停后综合征（PCAS）患者中的预后价值。
获取并回顾性分析2009年1月至2012年2月间连续患者的CT数据。所有CT图像均使用64排CT扫描仪获取，层厚为4.0mm。使用我们自主研发的自动算法从全脑CT图像中提取脑区，并用于后续的直方图分析。采用Student t检验比较预后良好组和预后不良组的直方图统计数据（平均脑组织CT值、峰度和偏度）以及临床参数。此外，对每个参数进行受试者工作特征曲线分析，以区分两组。
共纳入138例连续的PCAS患者。患者分为预后良好组（n = 47）和预后不良组（n = 91）。预后良好组的平均脑组织CT值显著高于预后不良组（P < 0.05）。预后良好组的峰度、偏度和年龄显著低于预后不良组（分别为P < 0.0001、P < 0.05和P < 0.05）。峰度、平均脑组织CT值、偏度和年龄的曲线下面积值分别为0.751、0.639、0.623和0.626。这4个参数的组合提高了诊断性能（曲线下面积 = 0.814）。
采用我们的自动提取算法对全脑CT图像进行直方图分析，有助于评估PCAS患者的预后。
