Li Ran, Zhou Pengyu, Chen Xinyue, Mossa-Basha Mahmud, Zhu Chengcheng, Wang Yuting
Department of Radiology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China.
Computed Tomography Angiography Collaboration, Siemens Healthineers, Chengdu, China.
Front Neurol. 2022 Apr 11;13:876238. doi: 10.3389/fneur.2022.876238. eCollection 2022.
Identifying unruptured intracranial aneurysm instability is crucial for therapeutic decision-making. This study aims to evaluate the role of Radiomics and traditional morphological features in identifying aneurysm instability by constructing and comparing multiple models.
A total of 227 patients with 254 intracranial aneurysms evaluated by CTA were included. Aneurysms were divided into unstable and stable groups using comprehensive criteria: the unstable group was defined as aneurysms with near-term rupture, growth during follow-up, or caused compressive symptoms; those without the aforementioned conditions were grouped as stable aneurysms. Aneurysms were randomly divided into training and test sets at a 1:1 ratio. Radiomics and traditional morphological features (maximum diameter, irregular shape, aspect ratio, size ratio, location, etc.) were extracted. Three basic models and two integrated models were constructed after corresponding statistical analysis. Model A used traditional morphological parameters. Model B used Radiomics features. Model C used the Radiomics features related to aneurysm morphology. Furthermore, integrated models of traditional and Radiomics features were built (model A+B, model A+C). The area under curves (AUC) of each model was calculated and compared.
There were 31 (13.7%) patients harboring 36 (14.2%) unstable aneurysms, 15 of which ruptured post-imaging, 16 with growth on serial imaging, and 5 with compressive symptoms, respectively. Four traditional morphological features, six Radiomics features, and three Radiomics-derived morphological features were identified. The classification of aneurysm stability was as follows: the AUC of the training set and test set in models A, B, and C are 0.888 (95% CI 0.808-0.967) and 0.818 (95% CI 0.705-0.932), 0.865 (95% CI 0.777-0.952) and 0.739 (95% CI 0.636-0.841), 0.605(95% CI 0.470-0.740) and 0.552 (95% CI 0.401-0.703), respectively. The AUC of integrated Model A+B was numerically slightly higher than any single model, whereas Model A+C was not.
A radiomics and traditional morphology integrated model seems to be an effective tool for identifying intracranial aneurysm instability, whereas the use of Radiomics-derived morphological features alone is not recommended. Radiomics-based models were not superior to the traditional morphological features model.
识别未破裂颅内动脉瘤的不稳定性对于治疗决策至关重要。本研究旨在通过构建和比较多个模型来评估影像组学和传统形态学特征在识别动脉瘤不稳定性中的作用。
纳入227例经CTA评估的254个颅内动脉瘤患者。采用综合标准将动脉瘤分为不稳定组和稳定组：不稳定组定义为近期破裂、随访期间生长或引起压迫症状的动脉瘤；无上述情况的动脉瘤归为稳定动脉瘤。动脉瘤以1:1的比例随机分为训练集和测试集。提取影像组学和传统形态学特征（最大直径、不规则形状、纵横比、大小比、位置等）。经过相应的统计分析后构建了三个基本模型和两个综合模型。模型A使用传统形态学参数。模型B使用影像组学特征。模型C使用与动脉瘤形态相关的影像组学特征。此外，构建了传统和影像组学特征的综合模型（模型A+B、模型A+C）。计算并比较每个模型的曲线下面积（AUC）。
31例（13.7%）患者有36个（14.2%）不稳定动脉瘤，其中15个在成像后破裂，16个在系列成像中生长，5个有压迫症状。确定了四个传统形态学特征、六个影像组学特征和三个影像组学衍生的形态学特征。动脉瘤稳定性的分类如下：模型A、B和C训练集和测试集的AUC分别为0.888（95%CI 0.808-0.967）和0.818（95%CI 0.705-0.932）、0.865（95%CI 0.777-0.952）和0.739（95%CI 0.636-0.841）、0.605（95%CI 0.470-0.740）和0.552（95%CI 0.401-0.703）。综合模型A+B的AUC在数值上略高于任何单一模型，而模型A+C则不然。
影像组学和传统形态学综合模型似乎是识别颅内动脉瘤不稳定性的有效工具，而不建议单独使用影像组学衍生的形态学特征。基于影像组学的模型并不优于传统形态学特征模型。
