Li Linna, Li Mingyang, Chen Zhongping, Lu Fei, Zhao Min, Zhang Huimao, Tong Dan
Department of Radiology, The First Hospital of Jilin University, Changchun, Jilin, China.
Pharmaceutical Diagnostics, GE Healthcare, Beijing, China.
Front Neurol. 2023 Jan 13;13:1037204. doi: 10.3389/fneur.2022.1037204. eCollection 2022.
The purpose of this study was to evaluate the prognostic value of radiomics-based hyperdense middle cerebral artery sign (HMCAS) for patients with acute ischemic stroke (AIS) after mechanical thrombectomy (MT) and to establish prediction models to identify patients who may benefit more from MT.
In this retrospective study, a total of 102 consecutive patients who presented with HMCAS on non-contrast computed tomography (NCCT) at admission and underwent MT in our hospital between January 2019 and December 2020 were recruited. Among them, 46 experienced favorable outcomes (modified Rankin Scale [mRS] ≤ 2) at 3 months of follow-up. All patients were categorized into two sets, namely, the training set ( = 81) and the test set ( = 21). Radiomics features (RFs) and clinical features (CFs) in the training set were selected by statistical analysis to create models. The models' discriminative ability was quantified using the area under the curve (AUC) and confirmed by decision curve analyses.
The prediction model established using CFs before MT includes baseline National Institutes of Health Stroke Scale (NIHSS) and neutrophil-to-lymphocyte ratio (NLR) [AUC [95% confidence interval (CI)] = 0.596 (0.312-0.881)]. A total of 1,389 RFs were extracted from each hyperdense territory and 8 RFs were left to build the radiomics model [RM; AUC (95%CI) = 0.798 (0.598-0.998)]. The model using preoperative CFs and RFs showed good performance [AUC (95%CI) = 0.817 (0.625-1.000)]. The models using post-operative CFs alone [AUC (95%CI) = 0.856 (0.685-1.000)] or post-operative CFs with RFs [AUC (95%CI) = 0.894 (0.757-1.000)] also showed good discrimination.
The radiomics-based HMCAS might be a promising tool to predict the prognoses of patients with AIS after MT.
本研究旨在评估基于影像组学的大脑中动脉高密度征(HMCAS)对急性缺血性卒中(AIS)患者机械取栓(MT)后预后的预测价值,并建立预测模型以识别可能从MT中获益更多的患者。
在这项回顾性研究中,共纳入了2019年1月至2020年12月期间在我院入院时非增强计算机断层扫描(NCCT)上出现HMCAS并接受MT的102例连续患者。其中,46例在随访3个月时获得了良好预后(改良Rankin量表[mRS]≤2)。所有患者被分为两组,即训练集(=81)和测试集(=21)。通过统计分析选择训练集中的影像组学特征(RFs)和临床特征(CFs)来创建模型。使用曲线下面积(AUC)对模型的鉴别能力进行量化,并通过决策曲线分析进行验证。
使用MT前CFs建立的预测模型包括基线美国国立卫生研究院卒中量表(NIHSS)和中性粒细胞与淋巴细胞比值(NLR)[AUC[95%置信区间(CI)]=0.596(0.312-0.881)]。从每个高密度区域提取了总共1389个RFs,留下8个RFs构建影像组学模型[RM;AUC(95%CI)=0.798(0.598-0.998)]。使用术前CFs和RFs的模型表现良好[AUC(95%CI)=0.817(0.625-1.000)]。仅使用术后CFs的模型[AUC(95%CI)=0.856(0.685-1.000)]或术后CFs与RFs的模型[AUC(95%CI)=0.894(0.757-1.000)]也显示出良好的鉴别能力。
基于影像组学的HMCAS可能是预测AIS患者MT后预后的一种有前景的工具。