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

Zhihong Chen; Qingqing Li; Renyuan Li; Han Zhao; Zhaoqing Li; Ying Zhou; Renxiu Bian; Xinyu Jin; Min Lou; Ruiliang Bai

doi:10.21037/qims-21-33

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

Quant Imaging Med Surg. 2021 Sep;11(9):3978-3989. doi: 10.21037/qims-21-33.

Authors

Zhihong Chen¹, Qingqing Li², Renyuan Li^{3

4}, Han Zhao¹, Zhaoqing Li^{4

5}, Ying Zhou², Renxiu Bian³, Xinyu Jin¹, Min Lou², Ruiliang Bai^{3

4

5}

Affiliations

¹ Institute of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, China.
² Department of Neurology, The Second Affiliated Hospital of Zhejiang University, School of Medicine, Hangzhou, China.
³ Department of Physical Medicine and Rehabilitation, The Affiliated Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, China.
⁴ Interdisciplinary Institute of Neuroscience and Technology, School of Medicine, Zhejiang University, Hangzhou, China.
⁵ Key Laboratory of Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China.

Abstract

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.

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