Xiong Xing, Wang Jia, Ke Jun, Hong Rong, Jiang Shu, Ye Jing, Hu Chunhong
Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, China.
Department of Radiology, Northern Jiangsu People's Hospital, Yangzhou, China.
Quant Imaging Med Surg. 2023 Feb 1;13(2):682-694. doi: 10.21037/qims-22-599. Epub 2023 Jan 2.
To evaluate the predictive value of radiomics features extracted from the thrombus on preoperative computed tomography images to identify successful recanalization after stent retrieve (SR) treatment in patients with acute ischemic stroke (AIS).
Two hundred fifty-six patients newly diagnosed AIS between March 2017 and September 2020 from two institutes, including the first affiliated hospital of Soochow university (institute I) and Northern Jiangsu People's hospital (institute II), were enrolled continuously and retrospectively. Patients with unsatisfactory image quality were excluded. The remaining patients of institute I were randomly divided into the training and internal validation cohorts at a ratio of 7 to 3, and patients of institute II were collected as the external validation cohort. After extraction and selection of the optimal radiomics features from training cohort, six machine learning (ML) classifiers including naïve Bayes (NB), random forest (RF), logistic regression (LR), linear support vector machine (L.SVM), radial SVM (R.SVM), and an artificial neural network (ANN) were developed to predict successful recanalization with SR treatment and compared. A combined model based on the optimal ML classifier was constructed using the optimal radiomics model and clinical-radiological risk variables. Finally, the performance of the model was selected based on the Matthews correlation coefficient (MCC) and the area under the receiver operating (AUC) and independently evaluated on the internal validation and external validation cohorts.
We automatically extracted 1,130 radiomics features from the voxel of interest (VOI) using PyRadiomics. The eight most relevant radiomics features were identified using Intraclass coefficient, single-factor logistic regression analysis, and least absolute shrinkage and selection operator algorithm in the training cohort. Among the six ML classifiers, the ANN classifier using thrombus radiomics features achieved the best prediction of early recanalization under SR with MCCs of 0.913, 0.693 and 0.505 in training, internal and external validation cohorts, respectively. Moreover, receiver operating characteristic curves showed that the combined model [AUC =0.860, 95% confidence interval (CI): 0.731-0.936; AUC =0.849, 95% CI: 0.759-0.831] was not significantly better than radiomics model based on the ANN classifier alone (AUC =0.873, 95% CI: 0.803-0.891; AUC =0.805, 95% CI: 0.864-0.971) (P>0.05, Delong test) in internal and external validation cohorts.
A radiomics model based on the ANN classifier has the ability to predict successful recanalization after SR in patients with AIS, thus allowing a potentially better selection of mechanical thrombectomy treatment.
评估从急性缺血性卒中（AIS）患者术前计算机断层扫描图像中提取的血栓放射组学特征对支架取栓（SR）治疗后成功再通的预测价值。
连续回顾性纳入2017年3月至2020年9月期间来自两所机构（苏州大学附属第一医院（机构I）和苏北人民医院（机构II））新诊断为AIS的256例患者。排除图像质量不佳的患者。机构I的其余患者按7:3的比例随机分为训练组和内部验证组，机构II的患者作为外部验证组。从训练组中提取并选择最佳放射组学特征后，开发了包括朴素贝叶斯（NB）、随机森林（RF）、逻辑回归（LR）、线性支持向量机（L.SVM）、径向支持向量机（R.SVM）和人工神经网络（ANN）在内的六种机器学习（ML）分类器，以预测SR治疗后的成功再通并进行比较。使用最佳放射组学模型和临床放射学风险变量构建基于最佳ML分类器的联合模型。最后，根据马修斯相关系数（MCC）和受试者工作特征曲线下面积（AUC）选择模型性能，并在内部验证组和外部验证组上进行独立评估。
我们使用PyRadiomics从感兴趣体素（VOI）中自动提取了1130个放射组学特征。在训练组中，使用组内系数、单因素逻辑回归分析和最小绝对收缩和选择算子算法确定了八个最相关的放射组学特征。在六种ML分类器中，使用血栓放射组学特征的ANN分类器在训练组、内部验证组和外部验证组中对SR后早期再通的预测效果最佳，MCC分别为0.913、0.693和0.505。此外，受试者工作特征曲线显示，联合模型[AUC =0.860，95%置信区间（CI）：0.731 - 0.936；AUC =0.849，95% CI：0.759 - 0.831]在内部和外部验证组中并不显著优于仅基于ANN分类器的放射组学模型（AUC =0.873，95% CI：0.803 - 0.891；AUC =0.805，95% CI：0.864 - 0.971）（P>0.05，德龙检验）。
基于ANN分类器的放射组学模型能够预测AIS患者SR治疗后的成功再通，从而可能更好地选择机械取栓治疗。
