From the Calgary Stroke Program, Departments of Clinical Neurosciences (W.Q., H.K., E.T., J.M.O., M.G., M.D.H., A.M.D., B.K.M.), Radiology (M.G., M.D.H., A.M.D., B.K.M.), and Community Health Sciences (M.D.H., B.K.M.), University of Calgary, 239 Strathridge Pl SW, Calgary, AB, Canada T3H 4J2; Hotchkiss Brain Institute, Calgary, Alberta, Canada (M.G., M.D.H., A.M.D., B.K.M.), Department of Neurology, Keimyung University, Daegu, South Korea (S.I.S.); and Division of Neuroradiology, Clinic of Radiology and Nuclear Medicine, University Hospital Basel, University of Basel, Basel, Switzerland (J.M.O.).
Radiology. 2020 Mar;294(3):638-644. doi: 10.1148/radiol.2020191193. Epub 2020 Jan 28.
Background Identifying the presence and extent of infarcted brain tissue at baseline plays a crucial role in the treatment of patients with acute ischemic stroke (AIS). Patients with extensive infarction are unlikely to benefit from thrombolysis or thrombectomy procedures. Purpose To develop an automated approach to detect and quantitate infarction by using non-contrast-enhanced CT scans in patients with AIS. Materials and Methods Non-contrast-enhanced CT images in patients with AIS (<6 hours from symptom onset to CT) who also underwent diffusion-weighted (DW) MRI within 1 hour after AIS were obtained from May 2004 to July 2009 and were included in this retrospective study. Ischemic lesions manually contoured on DW MRI scans were used as the reference standard. An automatic segmentation approach involving machine learning (ML) was developed to detect infarction. Randomly selected nonenhanced CT images from 157 patients with the lesion labels manually contoured on DW MRI scans were used to train and validate the ML model; the remaining 100 patients independent of the derivation cohort were used for testing. The ML algorithm was quantitatively compared with the reference standard (DW MRI) by using Bland-Altman plots and Pearson correlation. Results In 100 patients in the testing data set (median age, 69 years; interquartile range [IQR]: 59-76 years; 59 men), baseline non-contrast-enhanced CT was performed within a median time of 48 minutes from symptom onset (IQR, 27-93 minutes); baseline MRI was performed a median of 38 minutes (IQR, 24-48 minutes) later. The algorithm-detected lesion volume correlated with the reference standard of expert-contoured lesion volume in acute DW MRI scans ( = 0.76, < .001). The mean difference between the algorithm-segmented volume (median, 15 mL; IQR, 9-38 mL) and the DW MRI volume (median, 19 mL; IQR, 5-43 mL) was 11 mL ( = .89). Conclusion A machine learning approach for segmentation of infarction on non-contrast-enhanced CT images in patients with acute ischemic stroke showed good agreement with stroke volume on diffusion-weighted MRI scans. © RSNA, 2020 See also the editorial by Nael in this issue.
背景 在急性缺血性脑卒中(AIS)患者的治疗中,确定基线时存在的和梗死范围的大小起着至关重要的作用。广泛梗死的患者不太可能从溶栓或取栓治疗中获益。目的 开发一种自动方法,利用 AIS 患者的非增强 CT 扫描来检测和定量梗死。材料与方法 本回顾性研究纳入了 2004 年 5 月至 2009 年 7 月期间发病 6 小时内接受 CT 检查且发病后 1 小时内行弥散加权(DW)MRI 检查的 AIS 患者的非增强 CT 图像。在 DW MRI 扫描上手动勾画的缺血性病变为参考标准。开发了一种涉及机器学习(ML)的自动分割方法来检测梗死。从 157 例具有 DW MRI 扫描上手动勾画病变标签的患者中随机选择非增强 CT 图像,用于训练和验证 ML 模型;其余 100 例患者来自与推导队列无关的独立数据集,用于测试。通过 Bland-Altman 图和 Pearson 相关来比较 ML 算法与参考标准(DW MRI)。结果 在 100 例测试数据集患者(中位年龄,69 岁;四分位距 [IQR]:5976 岁;59 例男性)中,基线非增强 CT 从发病开始中位时间 48 分钟内(IQR,2793 分钟)完成;基线 MRI 则在其后中位时间 38 分钟内(IQR,2448 分钟)完成。算法检测到的病变体积与急性 DW MRI 扫描中专家勾画的病变体积参考标准高度相关( = 0.76, <.001)。算法分割的体积(中位数,15 mL;IQR,938 mL)与 DW MRI 体积(中位数,19 mL;IQR,5~43 mL)之间的平均差值为 11 mL( =.89)。结论 用于 AIS 患者急性非增强 CT 图像中梗死分割的机器学习方法与弥散加权 MRI 扫描上的卒中容积具有良好的一致性。© RSNA,2020 本期亦见 Nael 的述评。