A Machine Learning Approach to Predict Acute Ischemic Stroke Thrombectomy Reperfusion using Discriminative MR Image Features.
A Machine Learning Approach to Predict Acute Ischemic Stroke Thrombectomy Reperfusion using Discriminative MR Image Features.
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DOI：
10.1109/bhi50953.2021.9508597
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发表时间：
2021-07
期刊：
影响因子：
--
通讯作者：
Arnold, Corey
中科院分区：
文献类型：
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作者：
Zhang, Haoyue;Polson, Jennifer;Nael, Kambiz;Salamon, Noriko;Yoo, Bryan;Speier, William;Arnold, Corey
机械血栓切除术（MTB）是急性缺血性中风（AIS）患者的两种标准治疗选择之一。当前的临床指南指导使用治疗前成像来表征患者的脑血管血流，因为有许多因素可能是患者对治疗成功反应的基础。迫切需要利用入院时拍摄的治疗前成像来以自动化方式指导潜在的治疗途径。本研究的目的是开发并验证一种全自动机器学习算法，以预测 MTB 后最终改良的脑梗塞溶栓 (mTICI) 评分。根据 141 名患者的分段治疗前 MRI 扫描计算出总共 321 个放射组学特征。成功的再通定义为 mTICI 评分 >= 2c。本研究检查了不同的特征选择方法和分类模型。我们的最佳性能模型达到了 74.42±2.52% AUC、75.56±4.44% 敏感性和 76.75±4.55% 特异性，显示出使用治疗前 MRI 对再灌注质量的良好预测。结果表明，MR 图像可以为预测患者对 MTB 的反应提供信息，并且通过更大的队列进行进一步验证可以确定临床效用。
Mechanical thrombectomy (MTB) is one of the two standard treatment options for Acute Ischemic Stroke (AIS) patients. Current clinical guidelines instruct the use of pretreatment imaging to characterize a patient’s cerebrovascular flow, as there are many factors that may underlie a patient’s successful response to treatment. There is a critical need to leverage pretreatment imaging, taken at admission, to guide potential treatment avenues in an automated fashion. The aim of this study is to develop and validate a fully automated machine learning algorithm to predict the final modified thrombolysis in cerebral infarction (mTICI) score following MTB. A total 321 radiomics features were computed from segmented pretreatment MRI scans for 141 patients. Successful recanalization was defined as mTICI score >= 2c. Different feature selection methods and classification models were examined in this study. Our best performance model achieved 74.42±2.52% AUC, 75.56±4.44% sensitivity, and 76.75±4.55% specificity, showing a good prediction of reperfusion quality using pretreatment MRI. Results suggest that MR images can be informative to predicting patient response to MTB, and further validation with a larger cohort can determine the clinical utility.