Gerbasi Alessia, Konduri Praneeta, Tolhuisen Manon, Cavalcante Fabiano, Rinkel Leon, Kappelhof Manon, Wolff Lennard, Coutinho Jonathan M, Emmer Bart J, Costalat Vincent, Arquizan Caroline, Hofmeijer Jeannette, Uyttenboogaart Maarten, van Zwam Wim, Roos Yvo, Quaglini Silvana, Bellazzi Riccardo, Majoie Charles, Marquering Henk
Department of Electrical, Computer and Biomedical Engineering, University of Pavia, 27100 PV Pavia, Italy.
Department of Biomedical Engineering and Physics, Amsterdam UMC Location University of Amsterdam, 1105 AZ Amsterdam, The Netherlands.
J Cardiovasc Dev Dis. 2022 Dec 19;9(12):468. doi: 10.3390/jcdd9120468.
The biological pathways involved in lesion formation after an acute ischemic stroke (AIS) are poorly understood. Despite successful reperfusion treatment, up to two thirds of patients with large vessel occlusion remain functionally dependent. Imaging characteristics extracted from DWI and T2-FLAIR follow-up MR sequences could aid in providing a better understanding of the lesion constituents. We built a fully automated pipeline based on a tree ensemble machine learning model to predict poor long-term functional outcome in patients from the MR CLEAN-NO IV trial. Several feature sets were compared, considering only imaging, only clinical, or both types of features. Nested cross-validation with grid search and a feature selection procedure based on SHapley Additive exPlanations (SHAP) was used to train and validate the models. Considering features from both imaging modalities in combination with clinical characteristics led to the best prognostic model (AUC = 0.85, 95%CI [0.81, 0.89]). Moreover, SHAP values showed that imaging features from both sequences have a relevant impact on the final classification, with texture heterogeneity being the most predictive imaging biomarker. This study suggests the prognostic value of both DWI and T2-FLAIR follow-up sequences for AIS patients. If combined with clinical characteristics, they could lead to better understanding of lesion pathophysiology and improved long-term functional outcome prediction.
急性缺血性卒中（AIS）后病变形成所涉及的生物学途径目前尚不清楚。尽管进行了成功的再灌注治疗，但仍有多达三分之二的大血管闭塞患者存在功能依赖。从DWI和T2-FLAIR后续MR序列中提取的影像特征有助于更好地了解病变成分。我们基于树集成机器学习模型构建了一个全自动流程，以预测MR CLEAN-NO IV试验患者的长期功能预后不良。比较了几个特征集，分别考虑仅影像特征、仅临床特征或两种特征类型。使用基于网格搜索的嵌套交叉验证和基于SHapley加性解释（SHAP）的特征选择程序来训练和验证模型。结合两种影像模态的特征与临床特征可得到最佳预后模型（AUC = 0.85，95%CI [0.81, 0.89]）。此外，SHAP值表明两个序列的影像特征对最终分类有显著影响，其中纹理异质性是最具预测性的影像生物标志物。本研究表明DWI和T2-FLAIR后续序列对AIS患者具有预后价值。如果与临床特征相结合，它们可以更好地理解病变病理生理学并改善长期功能预后预测。
