Radiomics analysis for predicting malignant cerebral edema in patients undergoing endovascular treatment for acute ischemic stroke

Abstract

T he safety and efficacy of endovascular treatment (EVT) for anterior circulation large vessel occlusive stroke (LVOS) have been verified in randomized controlled trials. However, nearly 45% of the cases with LVOS cannot recover after EVT. 1,2 Recent evidence has revealed that malignant cerebral edema (MCE) commonly occurs after EVT, and the development of MCE may reduce the benefit-risk ratio of EVT. [3][4][5] MCE is a severe complication of stroke that occurs in approximately 10% of all patients with stroke. The prognosis for MCE is poor, with a mortality rate of 80% when treated conservatively. 6 Early decompressive surgery effectively lowers mortality and improves the clinical outcomes of patients with MCE. 7 The addition of decompressive craniectomy to the best medical therapy can reduce the mortality rate of MCE by 50%-75%. 8 Hence, the early identification of patients at a high risk of MCE is critical for therapeutic decision-making. The imaging predictors for MCE that have been studied so far primarily involve the characterization of the infarction size, neurovascular condition, and brain perfusion of the patient. 7 PURPOSERadiomics analysis is a promising image analysis technique. This study aims to extract a radiomics signature from baseline computed tomography (CT) to predict malignant cerebral edema (MCE) in patients with acute anterior circulation infarction after endovascular treatment (EVT). METHODSIn this retrospective study, 111 patients underwent EVT for acute ischemic stroke caused by middle cerebral artery (MCA) and/or internal carotid artery occlusion. The participants were randomly divided into two datasets: the training set (n = 77) and the test set (n = 34). The clinico-radiological profiles of all patients were collected, including cranial non-contrast-enhanced CT, CT angiography, and CT perfusion. The MCA territory on non-contrast-enhanced CT images was segmented, and the radiomics features associated with MCE were analyzed. The clinico-radiological parameters related to MCE were also identified. In addition, a routine visual radiological model based on radiological factors and a combined model comprising radiomics features and clinico-radiological factors were constructed to predict MCE. RESULTSThe areas under the curve (AUCs) of the radiomics signature for predicting MCE were 0.870 (P < 0.001) and 0.837 (P = 0.002) in the training and test sets, respectively. The AUCs of the routine visual radiological model were 0.808 (P < 0.001) and 0.813 (P = 0.005) in the training and test sets, respectively. The AUCs of the model combining the radiomics signature and clinico-radiological factors were 0.924 (P < 0.001) and 0.879 (P = 0.001) in the training and test sets, respectively. CONCLUSIONA CT image-based radiomics signature is a promising tool for predicting MCE in patients with acute anterior circulation infarction after EVT. For clinicians, it may assist in diagnostic decision-making.

An integrated nomogram combining clinical and radiomic features of hyperattenuated imaging markers to predict malignant cerebral edema following endovascular thrombectomy

“…Moreover, radiomics analysis has demonstrated superiority over previous methods in predicting the occurrence of MCE after EVT in those with AIS. Wen et al ( 23 ) developed a predictive model using radiomic features extracted from preoperative NCCT images before EVT. This model outperformed previous models and achieved AUC values of 0.870 and 0.837 for the training and test sets, respectively.…”