结合临床因素、CT 征象和放射组学,区分急性缺血性脑卒中患者机械血栓切除术后的高密度区。

Duchang Zhai, Yuanyuan Wu, Manman Cui, Yan Liu, Xiuzhi Zhou, Dongliang Hu, Yuancheng Wang, Shenghong Ju, Guohua Fan, Wu Cai
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引用次数: 0

摘要

背景和目的:临床上,机械取栓术(MT)后出血转化(HT)是一种常见的并发症。本研究旨在探讨临床因素、CT征象和放射组学在急性缺血性卒中伴大血管闭塞(AIS-LVO)患者机械取栓术后脑部高密度区(HDAs)鉴别诊断中的价值:回顾性纳入2015年12月至2023年6月期间Ⅰ中心符合条件的AIS-LVO患者共156例,按7:3的比例随机分为训练集(n=109)和内部验证集(n=47)。第Ⅱ中心的 63 名患者数据作为外部验证集。根据诊断标准,三个数据集中的患者被分为高血压组和非高血压组。利用Ⅰ号和Ⅱ号中心的临床和影像学数据,通过逻辑回归(LR)建立临床因素和CT征象模型、放射学模型和综合模型。采用受试者操作特征(ROC)分析法评估了三个数据集中每个模型的诊断效果:结果:根据多变量 LR 分析,临床血糖(Glu)和 CT 最大横截面积(Areamax)与 HDA 的性质相关(P < 0.05)。在三个模型中,组合模型的诊断效率最高,三个数据集的曲线下面积(AUC)值分别为0.895、0.882和0.820,明显高于放射组学模型(0.887、0.898、0.798)和临床因素与CT征象模型(0.831、0.744、0.684)的AUC值:基于放射组学的综合模型性能最佳,表明放射组学特征可作为影像生物标志物,帮助临床判断 MT 后 HDA 的性质:缩写:HDA = 高密度区;HT = 出血性转化;MT = 机械取栓术;AIS-LVO = 急性缺血性卒中伴大血管闭塞;LR = 逻辑回归;AUC = 曲线下面积;ICE = 碘对比剂外渗;DECT=双能量 CT;IOM=碘覆盖图;VNC=虚拟非对比;Glu=葡萄糖;LASSO=最小绝对收缩和选择算子;ICC=类内相关系数;ROC=接收器操作特征;DCA=决策曲线分析。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Combinations of Clinical Factors, CT Signs, Radiomics for Differentiating High-density Areas After Mechanical Thrombectomy in Patients with Acute Ischemic Stroke.

Background and purpose: Clinically, hemorrhagic transformation (HT) after mechanical thrombectomy (MT) is a common complication. This study is aim to investigate the value of clinical factors, CT signs, and radiomics in the differential diagnosis of high-density areas (HDAs) in the brain after MT in patients with acute ischemic stroke with large vessel occlusion (AIS-LVO).

Materials and methods: A total of 156 eligible patients with AIS-LVO in Center Ⅰ from December 2015 to June 2023 were retrospectively enrolled and randomly divided into training (n=109) and internal validation (n=47) sets at a ratio of 7:3. The data of 63 patients in Center Ⅱ were collected as an external validation set. According to the diagnostic criteria, the patients in the three datasets were divided into a HT group and a non-HT group. The clinical and imaging data from Centers Ⅰ and Ⅱ were used to construct a clinical factor and CT-sign model, a radiomic model and a combined model by logistic regression (LR). Receiver operating characteristic (ROC) analysis was used to evaluate the diagnostic efficacy of each model in the three datasets.

Results: Clinical blood glucose (Glu) and the maximum cross-sectional area (Areamax) on CT were associated with the nature of the HDA according to multivariate LR analyses (P < 0.05). Among the three models, the combined model had the highest diagnostic efficiency, with area under the curve (AUC) values of 0.895, 0.882, and 0.820 in the three datasets, which were significantly greater than the AUC values of the radiomic model (0.887, 0.898, 0.798) and clinical factor and CT sign model (0.831, 0.744, 0.684).

Conclusions: The combined model based on radiomics had the best performance, indicating that radiomic features can be used as imaging biomarkers to aid in the clinical judgment of the nature of HDA after MT.

Abbreviations: HDA =high-density area; HT =hemorrhagic transformation; MT =mechanical thrombectomy; AIS-LVO =acute ischemic stroke with large vessel occlusion; LR =logistic regression; AUC =area under the curve; ICE =iodine contrast extravasation; DECT =dual energy CT; IOM =iodine overlay map; VNC =virtual noncontrast; Glu =glucose; LASSO =least absolute shrinkage and selection operator; ICC =intraclass correlation coefficient; ROC =receiver operating characteristic; DCA =decision curve analysis.

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