Machine Learning (ML) has been proposed for tissue fate prediction after acute ischemic stroke (AIS), with the aim to help treatment decision and patient management. We compared three different ML models to the clinical method based on diffusion-perfusion thresholding for the voxel-based prediction of final infarct, using a large MRI dataset obtained in a cohort of AIS patients prior to recanalization treatment. Baseline MRI (MRI₀), including diffusion-weighted sequence (DWI) and Tmax maps from perfusion-weighted sequence, and 24-hr follow-up MRI (MRI_24h) were retrospectively collected in consecutive 394 patients AIS patients (median age = 70 years; final infarct volume = 28mL). Manually segmented DWI_24h lesion was considered the final infarct. Gradient Boosting, Random Forests and U-Net were trained using DWI, apparent diffusion coefficient (ADC) and Tmax maps on MRI₀ as inputs to predict final infarct. Tissue outcome predictions were compared to final infarct using Dice score. Gradient Boosting had significantly better predictive performance (median [IQR] Dice Score as for median age, maybe you can replace the comma with an equal sign for consistency 0.53 [0.29–0.68]) than U-Net (0.48 [0.18–0.68]), Random Forests (0.51 [0.27–0.66]), and clinical thresholding method (0.45 [0.25–0.62]) (P < 0.001). In this benchmark of ML models for tissue outcome prediction in AIS, Gradient Boosting outperformed other ML models and clinical thresholding method and is thus promising for future decision-making.

机器学习 (ML) 已被提议用于急性缺血性卒中 (AIS) 后的组织命运预测，旨在帮助治疗决策和患者管理。我们使用在再通治疗之前在一组 AIS 患者中获得的大型 MRI 数据集，将三种不同的 ML 模型与基于扩散灌注阈值的临床方法进行比较，以预测最终梗死的体素。在连续394_名AIS患者（中位年龄 = ₇₀年；最终梗死体积 = 28mL）。手动分段 DWI _24h病变被认为是最终的梗塞。梯度提升、随机森林和 U-Net 使用 DWI、表观扩散系数 (ADC) 和 MRI ₀上的 Tmax 图作为预测最终梗死的输入进行训练。使用 Dice 评分将组织结果预测与最终梗死进行比较。Gradient Boosting 比 U-Net (0.48 [0.18-0.68]) 具有显着更好的预测性能（中位数 [IQR] Dice Score 作为中位数年龄，也许你可以用等号替换逗号以获得​​一致性 0.53 [0.29–0.68]） ，随机森林（0.51 [0.27–0.66]）和临床阈值方法（0.45 [0.25–0.62]）（P < 0.001)。在这个用于 AIS 组织结果预测的 ML 模型基准测试中，梯度提升优于其他 ML 模型和临床阈值方法，因此有望用于未来的决策。