Department of Medical Imaging, Xuzhou Medical University, Xuzhou, China,
Department of Medical Imaging, Jinling Hospital, Nanjing University School of Medicine, Nanjing, China.
Cerebrovasc Dis. 2022;51(5):647-654. doi: 10.1159/000522058. Epub 2022 Mar 8.
Carotid computed tomography angiography (CTA) is routinely used for evaluating the atherosclerotic process. Radiomics allows the extraction of imaging markers of lesion heterogeneity and spatial complexity. These quantitative features can be used as the input for machine learning (ML). Therefore, in this study, we aimed to evaluate the diagnostic performance of radiomics-based ML assessment of carotid CTA data to identify symptomatic patients with carotid artery atherosclerosis.
In this retrospective study, participants with carotid artery atherosclerosis who underwent carotid CTA and brain magnetic resonance imaging from May 2010 to December 2017 were studied. The participants were grouped into symptomatic and asymptomatic groups according to their recent symptoms (determination of ipsilateral ischemic stroke). Eight conventional plaque features and 2,107 radiomics parameters were extracted from carotid CTA images. A radiomics-based ML model was fitted on the training set, and the radiomics-based ML model and conventional assessment were compared using the area under the curve (AUC) to identify symptomatic participants.
After excluding participants with other stroke sources, 120 patients with 148 carotid arteries were analyzed. Of these 148 carotid arteries, 34 (22.97%) were classified into the symptomatic group. Plaque ulceration (odds ratio [OR] = 0.257; 95% confidence interval [CI], 0.094-0.698) and plaque enhancement (OR = 0.305; 95% CI, 0.094-0.988) were associated with the symptomatic status. Twenty radiomics parameters were chosen to be inputs in the radiomics-based ML model. In the identification of symptomatic participants, the discriminatory value of the radiomics-based ML model was significantly higher than that of the conventional assessment (AUC = 0.858 vs. AUC = 0.706, p = 0.021).
Radiomics-based ML analysis improves the discriminatory power of carotid CTA in the identification of recent ischemic symptoms in patients with carotid artery atherosclerosis.
颈动脉计算机断层血管造影（CTA）通常用于评估动脉粥样硬化过程。放射组学允许提取病变异质性和空间复杂性的成像标志物。这些定量特征可作为机器学习（ML）的输入。因此，在这项研究中，我们旨在评估基于放射组学的 ML 评估颈动脉 CTA 数据以识别颈动脉粥样硬化患者有症状的患者的诊断性能。
在这项回顾性研究中，研究了 2010 年 5 月至 2017 年 12 月期间接受颈动脉 CTA 和脑磁共振成像的颈动脉粥样硬化患者。根据最近的症状（同侧缺血性卒中的确定），将患者分为有症状和无症状组。从颈动脉 CTA 图像中提取了 8 个常规斑块特征和 2107 个放射组学参数。在训练集上拟合基于放射组学的 ML 模型，并使用曲线下面积（AUC）比较基于放射组学的 ML 模型和常规评估，以识别有症状的参与者。
排除其他卒中源的参与者后，对 120 名患者的 148 条颈动脉进行了分析。在这 148 条颈动脉中，34 条（22.97%）被归类为有症状组。斑块溃疡（优势比[OR] = 0.257；95%置信区间[CI]，0.094-0.698）和斑块强化（OR = 0.305；95%CI，0.094-0.988）与症状状态相关。选择 20 个放射组学参数作为放射组学 ML 模型的输入。在识别有症状的参与者方面，基于放射组学的 ML 模型的判别能力明显高于常规评估（AUC=0.858 与 AUC=0.706，p=0.021）。
基于放射组学的 ML 分析提高了颈动脉 CTA 在识别颈动脉粥样硬化患者近期缺血症状中的鉴别能力。
