Prediction of tissue outcome in acute ischemic stroke based on single-phase CT angiography at admission
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Prediction of tissue outcome in acute ischemic stroke based on single-phase CT angiography at admission. / Palsson, Frosti; Forkert, Nils D; Meyer, Lukas; Broocks, Gabriel; Flottmann, Fabian; Maros, Máté E; Bechstein, Matthias; Winkelmeier, Laurens; Schlemm, Eckhard; Fiehler, Jens; Gellißen, Susanne; Kniep, Helge C.
In: FRONT NEUROL, Vol. 15, 19.03.2024, p. 1330497.Research output: SCORING: Contribution to journal › SCORING: Journal article › Research › peer-review
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TY - JOUR
T1 - Prediction of tissue outcome in acute ischemic stroke based on single-phase CT angiography at admission
AU - Palsson, Frosti
AU - Forkert, Nils D
AU - Meyer, Lukas
AU - Broocks, Gabriel
AU - Flottmann, Fabian
AU - Maros, Máté E
AU - Bechstein, Matthias
AU - Winkelmeier, Laurens
AU - Schlemm, Eckhard
AU - Fiehler, Jens
AU - Gellißen, Susanne
AU - Kniep, Helge C
N1 - Copyright © 2024 Palsson, Forkert, Meyer, Broocks, Flottmann, Maros, Bechstein, Winkelmeier, Schlemm, Fiehler, Gellißen and Kniep.
PY - 2024/3/19
Y1 - 2024/3/19
N2 - INTRODUCTION: In acute ischemic stroke, prediction of the tissue outcome after reperfusion can be used to identify patients that might benefit from mechanical thrombectomy (MT). The aim of this work was to develop a deep learning model that can predict the follow-up infarct location and extent exclusively based on acute single-phase computed tomography angiography (CTA) datasets. In comparison to CT perfusion (CTP), CTA imaging is more widely available, less prone to artifacts, and the established standard of care in acute stroke imaging protocols. Furthermore, recent RCTs have shown that also patients with large established infarctions benefit from MT, which might not have been selected for MT based on CTP core/penumbra mismatch analysis.METHODS: All patients with acute large vessel occlusion of the anterior circulation treated at our institution between 12/2015 and 12/2020 were screened (N = 404) and 238 patients undergoing MT with successful reperfusion were included for final analysis. Ground truth infarct lesions were segmented on 24 h follow-up CT scans. Pre-processed CTA images were used as input for a U-Net-based convolutional neural network trained for lesion prediction, enhanced with a spatial and channel-wise squeeze-and-excitation block. Post-processing was applied to remove small predicted lesion components. The model was evaluated using a 5-fold cross-validation and a separate test set with Dice similarity coefficient (DSC) as the primary metric and average volume error as the secondary metric.RESULTS: The mean ± standard deviation test set DSC over all folds after post-processing was 0.35 ± 0.2 and the mean test set average volume error was 11.5 mL. The performance was relatively uniform across models with the best model according to the DSC achieved a score of 0.37 ± 0.2 after post-processing and the best model in terms of average volume error yielded 3.9 mL.CONCLUSION: 24 h follow-up infarct prediction using acute CTA imaging exclusively is feasible with DSC measures comparable to results of CTP-based algorithms reported in other studies. The proposed method might pave the way to a wider acceptance, feasibility, and applicability of follow-up infarct prediction based on artificial intelligence.
AB - INTRODUCTION: In acute ischemic stroke, prediction of the tissue outcome after reperfusion can be used to identify patients that might benefit from mechanical thrombectomy (MT). The aim of this work was to develop a deep learning model that can predict the follow-up infarct location and extent exclusively based on acute single-phase computed tomography angiography (CTA) datasets. In comparison to CT perfusion (CTP), CTA imaging is more widely available, less prone to artifacts, and the established standard of care in acute stroke imaging protocols. Furthermore, recent RCTs have shown that also patients with large established infarctions benefit from MT, which might not have been selected for MT based on CTP core/penumbra mismatch analysis.METHODS: All patients with acute large vessel occlusion of the anterior circulation treated at our institution between 12/2015 and 12/2020 were screened (N = 404) and 238 patients undergoing MT with successful reperfusion were included for final analysis. Ground truth infarct lesions were segmented on 24 h follow-up CT scans. Pre-processed CTA images were used as input for a U-Net-based convolutional neural network trained for lesion prediction, enhanced with a spatial and channel-wise squeeze-and-excitation block. Post-processing was applied to remove small predicted lesion components. The model was evaluated using a 5-fold cross-validation and a separate test set with Dice similarity coefficient (DSC) as the primary metric and average volume error as the secondary metric.RESULTS: The mean ± standard deviation test set DSC over all folds after post-processing was 0.35 ± 0.2 and the mean test set average volume error was 11.5 mL. The performance was relatively uniform across models with the best model according to the DSC achieved a score of 0.37 ± 0.2 after post-processing and the best model in terms of average volume error yielded 3.9 mL.CONCLUSION: 24 h follow-up infarct prediction using acute CTA imaging exclusively is feasible with DSC measures comparable to results of CTP-based algorithms reported in other studies. The proposed method might pave the way to a wider acceptance, feasibility, and applicability of follow-up infarct prediction based on artificial intelligence.
U2 - 10.3389/fneur.2024.1330497
DO - 10.3389/fneur.2024.1330497
M3 - SCORING: Journal article
C2 - 38566856
VL - 15
SP - 1330497
JO - FRONT NEUROL
JF - FRONT NEUROL
SN - 1664-2295
ER -