Diprose James P, Diprose William K, Chien Tuan-Yow, Wang Michael T M, McFetridge Andrew, Tarr Gregory P, Ghate Kaustubha, Beharry James, Hong JaeBeom, Wu Teddy, Campbell Doug, Barber P Alan
Independent Computer Scientist, Auckland, New Zealand.
Department of Medicine, Faculty of Medical and Health Sciences, University of Auckland, Auckland, New Zealand.
J Neurointerv Surg. 2025 Feb 14;17(3):266-271. doi: 10.1136/jnis-2023-021154.
Deep learning using clinical and imaging data may improve pre-treatment prognostication in ischemic stroke patients undergoing endovascular thrombectomy (EVT).
Deep learning models were trained and tested on baseline clinical and imaging (CT head and CT angiography) data to predict 3-month functional outcomes in stroke patients who underwent EVT. Classical machine learning models (logistic regression and random forest classifiers) were constructed to compare their performance with the deep learning models. An external validation dataset was used to validate the models. The MR PREDICTS prognostic tool was tested on the external validation set, and its performance was compared with the deep learning and classical machine learning models.
A total of 975 patients (550 men; mean±SD age 67.5±15.1 years) were studied with 778 patients in the model development cohort and 197 in the external validation cohort. The deep learning model trained on baseline CT and clinical data, and the logistic regression model (clinical data alone) demonstrated the strongest discriminative abilities for 3-month functional outcome and were comparable (AUC 0.811 vs 0.817, Q=0.82). Both models exhibited superior prognostic performance than the other deep learning (CT head alone, CT head, and CT angiography) and MR PREDICTS models (all Q<0.05).
The discriminative performance of deep learning for predicting functional independence was comparable to logistic regression. Future studies should focus on whether incorporating procedural and post-procedural data significantly improves model performance.
利用临床和影像数据的深度学习可能会改善接受血管内血栓切除术（EVT）的缺血性中风患者的治疗前预后。
基于基线临床和影像（头颅CT和CT血管造影）数据训练并测试深度学习模型，以预测接受EVT的中风患者3个月后的功能结局。构建经典机器学习模型（逻辑回归和随机森林分类器），并与深度学习模型比较其性能。使用外部验证数据集对模型进行验证。在外部验证集上测试MR PREDICTS预后工具，并将其性能与深度学习模型和经典机器学习模型进行比较。
共研究了975例患者（550例男性；平均±标准差年龄67.5±15.1岁），其中778例在模型开发队列中，197例在外部验证队列中。基于基线CT和临床数据训练的深度学习模型以及逻辑回归模型（仅临床数据）对3个月功能结局显示出最强的判别能力，且二者相当（AUC分别为0.811和0.817，Q=0.82）。这两种模型均表现出优于其他深度学习模型（仅头颅CT、头颅CT和CT血管造影）和MR PREDICTS模型的预后性能（所有Q<0.05）。
深度学习在预测功能独立性方面的判别性能与逻辑回归相当。未来的研究应关注纳入手术过程中和术后数据是否能显著改善模型性能。
