Sanchez Sebastian, Veeturi Sricharan, Patel Tatsat, Ojeda Diego J, Sagues Elena, Miller Jacob M, Tutino Vincent M, Samaniego Edgar A
Department of Neurology, Yale University, New Haven, Connecticut, USA.
Canon Stroke and Vascular Research Center, University at Buffalo, Buffalo, NY, USA.
Interv Neuroradiol. 2024 Aug 30:15910199241275722. doi: 10.1177/15910199241275722.
High-resolution magnetic resonance imaging (HR-MRI) allows for detailed visualization of intracranial atherosclerotic plaques. Radiomics can be used as a tool for objective quantification of the plaque's characteristics. We analyzed the radiomics features (RFs) obtained from 7 T HR-MRI of patients with intracranial atherosclerotic disease (ICAD) to determine distinct characteristics of culprit and non-culprit plaques.
Patients with stroke due to ICAD underwent HR-MRI. Culprit plaques in the vascular territory of the stroke were identified. Degree of stenosis, area degree of stenosis and plaque burden were calculated. A three-dimensional segmentation of the plaque was performed, and RFs were obtained. A machine learning model for prediction and identification of culprit plaques using significantly different RFs was evaluated.
The study included 33 patients with ICAD as stroke etiology. Univariate analysis revealed 24 RFs in pre-contrast MRI, 21 in post-contrast MRI, 13 RFs that were different between pre and post contrast MRIs. Additionally, six shape-based RFs significantly differed from culprit and non-culprit plaques. The random forest model achieved an accuracy rate of 81% (88% sensitivity and 75% specificity) in identifying culprit plaques in the independent testing dataset. This model successfully identified the culprit plaques in all patients during the testing phase.
Symptomatic plaques had a distinct signature RFs compared to other plaques within the same subject. A machine learning model built with RFs successfully identified the symptomatic atherosclerotic plaques in most cases. Radiomics is a promising tool for stratification of plaques in patients with ICAD.
高分辨率磁共振成像（HR-MRI）能够详细显示颅内动脉粥样硬化斑块。放射组学可作为客观量化斑块特征的工具。我们分析了从颅内动脉粥样硬化疾病（ICAD）患者的7T HR-MRI中获得的放射组学特征（RFs），以确定罪犯斑块和非罪犯斑块的不同特征。
因ICAD导致中风的患者接受了HR-MRI检查。确定中风血管区域内的罪犯斑块。计算狭窄程度、面积狭窄程度和斑块负荷。对斑块进行三维分割，并获得RFs。评估了使用显著不同的RFs预测和识别罪犯斑块的机器学习模型。
该研究纳入了33例以ICAD为中风病因的患者。单变量分析显示，对比前MRI中有24个RFs，对比后MRI中有21个，对比前后MRI之间有13个RFs不同。此外，六个基于形状的RFs在罪犯斑块和非罪犯斑块之间有显著差异。随机森林模型在独立测试数据集中识别罪犯斑块的准确率为81%（敏感性为88%，特异性为75%）。该模型在测试阶段成功识别了所有患者的罪犯斑块。
与同一受试者体内的其他斑块相比，有症状的斑块具有独特的特征性RFs。用RFs建立的机器学习模型在大多数情况下成功识别了有症状的动脉粥样硬化斑块。放射组学是对ICAD患者的斑块进行分层的一种有前景的工具。
