Background and Purpose- For patients with large vessel occlusion, neuroimaging biomarkers that evaluate the changes in brain tissue are important for determining the indications for mechanical thrombectomy. In this study, we applied deep learning to derive imaging features from pretreatment diffusion-weighted image data and evaluated the ability of these features in predicting clinical outcomes for patients with large vessel occlusion. Methods- This multicenter retrospective study included patients with anterior circulation large vessel occlusion treated with mechanical thrombectomy between 2013 and 2018. We designed a 2-output deep learning model based on convolutional neural networks (the convolutional neural network model). This model employed encoder-decoder architecture for the ischemic lesion segmentation, which automatically extracted high-level feature maps in its middle layers, and used its information to predict the clinical outcome. Its performance was internally validated with 5-fold cross-validation, externally validated, and the results compared with those from the standard neuroimaging biomarkers Alberta Stroke Program Early CT Score and ischemic core volume. The prediction target was a good clinical outcome, defined as a modified Rankin Scale score at 90-day follow-up of 0 to 2. Results- The derivation cohort included 250 patients, and the validation cohort included 74 patients. The convolutional neural network model showed the highest area under the receiver operating characteristic curve: 0.81±0.06 compared with 0.63±0.05 and 0.64±0.05 for the Alberta Stroke Program Early CT Score and ischemic core volume models, respectively. In the external validation, the area under the curve for the convolutional neural network model was significantly superior to those for the other 2 models. Conclusions- Compared with the standard neuroimaging biomarkers, our deep learning model derived a greater amount of prognostic information from pretreatment neuroimaging data. Although a confirmatory prospective evaluation is needed, the high-level imaging features derived by deep learning may offer an effective prognostic imaging biomarker.

中文翻译：

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深度学习派生的高级神经影像功能可预测大血管闭塞的临床结果。

背景和目的-对于具有大血管闭塞的患者，评估脑组织变化的神经影像生物标志物对于确定机械血栓切除术的适应症很重要。在这项研究中，我们应用深度学习从预处理扩散加权图像数据中得出成像特征，并评估了这些特征在预测大血管闭塞患者临床预后中的能力。方法-该多中心回顾性研究纳入了2013年至2018年间接受机械血栓切除术治疗的前循环大血管闭塞患者。我们设计了基于卷积神经网络的2输出深度学习模型（卷积神经网络模型）。该模型采用编解码器架构进行缺血性病变分割，它会自动提取其中间层的高级特征图，并使用其信息来预测临床结果。其性能在内部进行了5倍交叉验证，在外部进行了验证，其结果与标准神经影像生物标志物艾伯塔中风计划早期CT评分和局部缺血核心量的结果进行了比较。预测目标是良好的临床结局，定义为在90天后的0到2天随访中改良的Rankin量表评分。结果-派生队列包括250例患者，验证队列包括74例患者。卷积神经网络模型在接收器操作特征曲线下显示出最大的区域：0.81±0.06，而阿尔伯塔中风计划早期CT评分和缺血核心体积模型分别为0.63±0.05和0.64±0.05。在外部验证中，卷积神经网络模型的曲线下面积明显优于其他两个模型。结论-与标准的神经影像生物标记相比，我们的深度学习模型从预处理的神经影像数据中获得了大量的预后信息。尽管需要确定性的前瞻性评估，但通过深度学习获得的高级成像功能可能会提供有效的预后成像生物标志物。