Fu Bowen, Qi Shouliang, Tao Lin, Xu Haibin, Kang Yan, Yao Yudong, Yang Benqiang, Duan Yang, Chen Huisheng
College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China.
Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, China.
Front Neurol. 2020 Dec 23;11:609747. doi: 10.3389/fneur.2020.609747. eCollection 2020.
Malignant cerebral edema (MCE) after an ischemic stroke results in a poor outcome or death. Early prediction of MCE helps to identify subjects that could benefit from a surgical decompressive craniectomy. Net water uptake (NWU) in an ischemic lesion is a predictor of MCE; however, CT perfusion and lesion segmentation are required. This paper proposes a new Image Patch-based Net Water Uptake (IP-NWU) procedure that only uses non-enhanced admission CT and does not need lesion segmentation. IP-NWU is calculated by comparing the density of ischemic and contralateral normal patches selected from the middle cerebral artery (MCA) area using standard reference images. We also compared IP-NWU with the Segmented Region-based NWU (SR-NWU) procedure in which segmented ischemic regions from follow-up CT images are overlaid onto admission images. Furthermore, IP-NWU and its combination with imaging features are used to construct predictive models of MCE with a radiomics approach. In total, 116 patients with an MCA infarction (39 with MCE and 77 without MCE) were included in the study. IP-NWU was significantly higher for patients with MCE than those without MCE ( < 0.05). IP-NWU can predict MCE with an AUC of 0.86. There was no significant difference between IP-NWU and SR-NWU, nor between their predictive efficacy for MCE. The inter-reader and interoperation agreement of IP-NWU was exceptional according to the Intraclass Correlation Coefficient (ICC) analysis (inter-reader: ICC = 0.92; interoperation: ICC = 0.95). By combining IP-NWU with imaging features through a random forest classifier, the radiomics model achieved the highest AUC (0.96). In summary, IP-NWU and radiomics models that combine IP-NWU with imaging features can precisely predict MCE using only admission non-enhanced CT images scanned within 24 h from onset.
缺血性中风后发生的恶性脑水肿（MCE）会导致预后不良或死亡。早期预测MCE有助于识别可能从外科减压颅骨切除术获益的患者。缺血性病变中的净吸水量（NWU）是MCE的一个预测指标；然而，这需要CT灌注和病变分割。本文提出了一种新的基于图像块的净吸水量（IP-NWU）方法，该方法仅使用平扫入院CT，且无需病变分割。IP-NWU通过使用标准参考图像比较从中脑动脉（MCA）区域选取的缺血性和对侧正常图像块的密度来计算。我们还将IP-NWU与基于分割区域的NWU（SR-NWU）方法进行了比较，后者是将随访CT图像中分割出的缺血区域叠加到入院图像上。此外，IP-NWU及其与影像特征的组合被用于通过放射组学方法构建MCE的预测模型。该研究共纳入了116例MCA梗死患者（39例发生MCE，77例未发生MCE）。发生MCE的患者的IP-NWU显著高于未发生MCE的患者（<0.05）。IP-NWU预测MCE的曲线下面积（AUC）为0.86。IP-NWU与SR-NWU之间以及它们对MCE的预测效能之间均无显著差异。根据组内相关系数（ICC）分析，IP-NWU的阅片者间和操作间一致性极佳（阅片者间：ICC = 0.92；操作间：ICC = 0.95）。通过随机森林分类器将IP-NWU与影像特征相结合，放射组学模型获得了最高的AUC（0.96）。总之，IP-NWU以及将IP-NWU与影像特征相结合的放射组学模型仅使用发病后24小时内扫描的入院平扫CT图像就能精确预测MCE。
