标题： 大血管闭塞卒中患者的预后预测和个体化治疗效果估计 显示英文标题

标题： Outcome prediction and individualized treatment effect estimation in patients with large vessel occlusion stroke

摘要： 机械血栓切除术由于大血管闭塞（LVO）患者已成为标准治疗方法。 然而，只有50%的治疗成功的患者表现出良好的预后。 我们开发并评估了可解释的深度学习模型，使用来自随机临床试验的449例LVO卒中患者的资料，以预测改良Rankin量表评分的功能结果以及个体化治疗效果（ITEs）。 除了临床变量外，我们还考虑了非对比CT（NCCT）和血管造影（CTA）扫描，这些扫描通过新的基础模型进行整合，以利用先进的影像信息。 临床变量对于二元功能结果预测具有良好的预测能力（AUC为0.719 [0.666, 0.774]），当添加CTA影像时，预测能力略有提高（AUC为0.737 [0.687, 0.795]）。 将NCCT扫描或NCCT和CTA扫描的组合添加到临床特征中并未带来改善。 功能结果最重要的临床预测因素是卒中前残疾。 虽然估计的ITEs与平均治疗效果校准良好，但根据所有模型中的C-for-Benefit统计量约为0.55，表明区分能力有限。 总之，这些模型使我们能够联合整合CT影像和临床特征，同时实现了最先进的预测性能和ITE估计。 然而，需要进一步的研究来特别改进ITE估计。 显示英文摘要

摘要： Mechanical thrombectomy has become the standard of care in patients with stroke due to large vessel occlusion (LVO). However, only 50% of successfully treated patients show a favorable outcome. We developed and evaluated interpretable deep learning models to predict functional outcomes in terms of the modified Rankin Scale score alongside individualized treatment effects (ITEs) using data of 449 LVO stroke patients from a randomized clinical trial. Besides clinical variables, we considered non-contrast CT (NCCT) and angiography (CTA) scans which were integrated using novel foundation models to make use of advanced imaging information. Clinical variables had a good predictive power for binary functional outcome prediction (AUC of 0.719 [0.666, 0.774]) which could slightly be improved when adding CTA imaging (AUC of 0.737 [0.687, 0.795]). Adding NCCT scans or a combination of NCCT and CTA scans to clinical features yielded no improvement. The most important clinical predictor for functional outcome was pre-stroke disability. While estimated ITEs were well calibrated to the average treatment effect, discriminatory ability was limited indicated by a C-for-Benefit statistic of around 0.55 in all models. In summary, the models allowed us to jointly integrate CT imaging and clinical features while achieving state-of-the-art prediction performance and ITE estimates. Yet, further research is needed to particularly improve ITE estimation.