Ensemble learning accurately predicts the potential benefits of thrombolytic therapy in acute ischemic stroke

Quantitative Imaging in Medicine and Surgery

article

Zhihong Chen¹  Ruiliang Bai²  Qingqing Li⁵  Renyuan Li²  Han Zhao¹  Zhaoqing Li³  Ying Zhou⁵  Renxiu Bian²  Xinyu Jin¹  Min Lou⁵ 

[1] Institute of Information Science and Electronic Engineering, Zhejiang University;Department of Physical Medicine and Rehabilitation, The Affiliated Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University;Interdisciplinary Institute of Neuroscience and Technology, School of Medicine, Zhejiang University;Key Laboratory of Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University;Department of Neurology, The Second Affiliated Hospital of Zhejiang University, School of Medicine

关键词: Infarct volume prediction;    thrombolytic therapy;    recanalization;    ensemble learning;    convolutional neural networks;    computer-aided diagnosis;   

DOI  :  10.21037/qims-21-33

学科分类：外科医学

来源: AME Publications

【 摘 要 】

Background: Finding methods to accurately predict the final infarct volumes for acute ischemic stroke patients with full or no recanalization would significantly help to evaluate the potential benefits of thrombolytic therapy. We proposed such a method by constructing a model of ensemble deep learning and machine learning using diffusion-weighted imaging (DWI) only. Methods: The proposed prediction model (named AUNet) combines an adaptive linear ensemble model (ALEM) of machine learning and a deep U-Net network with an accelerated non-local module (U-NL-Net) to learn voxel-wise and spatial features, respectively. Of 40 patients with acute ischemic stroke who received thrombolytic therapy, 17 were fully recanalized, 14 were not recanalized, and nine were partially recanalized. The AUNet was separately trained for full recanalization conditions (AUNetR) and no recanalization (AUNetN) as the best and worst outcomes of thrombolysis, respectively. Results: AUNet performed significantly better in predicting the final infarct volumes in both the recanalization and non-recanalization conditions [area under the receiver operating characteristic curve (AUC) =0.898±0.022, recanalization; AUC =0.875±0.036, non-recanalization: Matthew’s correlation coefficient (MCC) =0.863±0.033, recanalization; MCC =0.851±0.025, non-recanalization] than the fixed-thresholding method (AUC =0.776±0.021, P<0.0001, recanalization; AUC =0.692±0.023, P<0.0001, non-recanalization: MCC =0.742±0.035, recanalization; MCC =0.671±0.024, non-recanalization), the logistic regression method (AUC =0.797±0.023, P<0.003, recanalization; AUC =0.751±0.030, P<0.003, non-recanalization: MCC =0.762±0.035, recanalization; MCC =0.730±0.031, non-recanalization), and a recently developed convolutional neural network (AUC =0.814±0.013, P<0.003, recanalization; AUC =0.781±0.027, P<0.003, non-recanalization: MCC =792±0.022, recanalization; MCC =0.758±0.016, non-recanalization). The potential benefit of thrombolysis calculated from AUNetR and AUNetN showed large individual differences (from 12.81% to 239.73%) Conclusions: AUNet improved predictive accuracy over current state-of-the-art methods. More importantly, the accurate prediction of infarct volumes under different recanalization conditions may provide benefitial information for physicians in selecting suitable patients for thrombolytic therapy.

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