IEEE Trans Med Imaging. 2019 Jul;38(7):1666-1676. doi: 10.1109/TMI.2019.2901445. Epub 2019 Feb 25.
Current clinical practice relies on clinical history to determine the time since stroke (TSS) onset. Imaging-based determination of acute stroke onset time could provide critical information to clinicians in deciding stroke treatment options, such as thrombolysis. The patients with unknown or unwitnessed TSS are usually excluded from thrombolysis, even if their symptoms began within the therapeutic window. In this paper, we demonstrate a machine learning approach for TSS classification using routinely acquired imaging sequences. We develop imaging features from the magnetic resonance (MR) images and train machine learning models to classify the TSS. We also propose a deep-learning model to extract hidden representations for the MR perfusion-weighted images and demonstrate classification improvement by incorporating these additional deep features. The cross-validation results show that our best classifier achieved an area under the curve of 0.765, with a sensitivity of 0.788 and a negative predictive value of 0.609, outperforming existing methods. We show that the features generated by our deep-learning algorithm correlate with the MR imaging features, and validate the robustness of the model on imaging parameter variations (e.g., year of imaging). This paper advances magnetic resonance imaging analysis one-step-closer to an operational decision support tool for stroke treatment guidance.
目前的临床实践依赖于临床病史来确定中风发病时间（TSS）。基于影像学的急性中风发病时间的确定可以为临床医生提供关键信息，以决定中风的治疗选择，如溶栓治疗。对于 TSS 未知或无法目击的患者，即使症状发生在治疗窗口内，通常也会被排除在溶栓治疗之外。在本文中，我们展示了一种使用常规获取的影像学序列对 TSS 进行分类的机器学习方法。我们从磁共振（MR）图像中提取影像学特征，并训练机器学习模型来对 TSS 进行分类。我们还提出了一种深度学习模型，用于提取 MR 灌注加权图像的隐藏表示，并通过整合这些额外的深度特征来证明分类的改进。交叉验证结果表明，我们最好的分类器的曲线下面积为 0.765，灵敏度为 0.788，阴性预测值为 0.609，优于现有的方法。我们表明，我们的深度学习算法生成的特征与 MR 影像学特征相关，并且模型在影像学参数变化（例如成像年份）上具有稳健性。本文将磁共振成像分析向前推进了一步，使其更接近一种用于中风治疗指导的操作性决策支持工具。
