Han Na, Hu Wanjun, Ma Yurong, Zheng Yu, Yue Songhong, Ma Laiyang, Li Jie, Zhang Jing
Department of Magnetic Resonance, Lanzhou University Second Hospital, Lanzhou, China.
Gansu Province Clinical Research Center for Functional and Molecular Imaging, Lanzhou, China.
Front Neurol. 2024 Mar 13;15:1343423. doi: 10.3389/fneur.2024.1343423. eCollection 2024.
To accurately predict the risk of ischemic stroke, we established a radiomics model of carotid atherosclerotic plaque-based high-resolution vessel wall magnetic resonance imaging (HR-VWMRI) and combined it with clinical indicators.
In total, 127 patients were finally enrolled and randomly divided into training and test cohorts. HR-VWMRI three-dimensional T1-weighted imaging (T1WI) and contrast-enhanced T1WI (T1CE) were collected. A traditional model was built by recording and calculating radiographic features of the carotid plaques and patients' clinical indicators. After extracting radiomics features from T1WI and T1CE images, the least absolute shrinkage and selection operator (LASSO) algorithm was used to select the optimal features and construct the radiomics_T1WI model and the radiomics_T1CE model. The traditional and radiomics features were used to build combined models. The performance of all the models predicting ischemic stroke was evaluated in the training and test cohorts, respectively.
Body mass index (BMI) and intraplaque hemorrhage (IPH) were independently related to ischemic stroke and were used to build the traditional model, which achieved an area under the curve (AUC) of 0.79 versus 0.78 in the training and test cohorts, respectively. The AUC value of the radiomics_T1WI model is the lowest in the training and test cohorts, but the prediction performance is significantly improved when the model combines IPH and BMI. The AUC value of the combined_T1WI model was 0.78 and 0.81 in the training and test cohorts, respectively. In addition, in the training and test cohorts, the radiomics_T1CE model based on HR-VWMRI combined clinical characteristics, which is the combined_T1CE model, had the highest AUC value of 0.84 and 0.82, respectively.
Compared with other models, the radiomics_T1CE model based on HR-VWMRI combined clinical characteristics, which is a combined_T1CE model, can accurately predict the risk of ischemic stroke.
为准确预测缺血性中风风险，我们基于高分辨率血管壁磁共振成像（HR-VWMRI）建立了颈动脉粥样硬化斑块的放射组学模型，并将其与临床指标相结合。
最终纳入127例患者并随机分为训练组和测试组。采集HR-VWMRI三维T1加权成像（T1WI）和对比增强T1WI（T1CE）。通过记录和计算颈动脉斑块的影像学特征及患者临床指标建立传统模型。从T1WI和T1CE图像中提取放射组学特征后，采用最小绝对收缩和选择算子（LASSO）算法选择最佳特征并构建放射组学_T1WI模型和放射组学_T1CE模型。将传统特征和放射组学特征用于构建联合模型。分别在训练组和测试组中评估所有模型预测缺血性中风的性能。
体重指数（BMI）和斑块内出血（IPH）与缺血性中风独立相关，并用于构建传统模型，该模型在训练组和测试组中的曲线下面积（AUC）分别为0.79和0.78。放射组学_T1WI模型在训练组和测试组中的AUC值最低，但当该模型结合IPH和BMI时预测性能显著提高。联合_T1WI模型在训练组和测试组中的AUC值分别为0.78和0.81。此外，在训练组和测试组中，基于HR-VWMRI联合临床特征的放射组学_T1CE模型即联合_T1CE模型的AUC值最高，分别为0.84和0.82。
与其他模型相比，基于HR-VWMRI联合临床特征的放射组学_T1CE模型即联合_T1CE模型能够准确预测缺血性中风风险。
