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A Deep Learning-Based Automatic Collateral Assessment in Patients with Acute Ischemic Stroke

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Abstract

This study aimed to develop a supervised deep learning (DL) model for grading collateral status from dynamic susceptibility contrast magnetic resonance perfusion (DSC-MRP) images from patients with large vessel occlusion (LVO) acute ischemic stroke (AIS) and compare its performance against experts’ manual grading. Among consecutive LVO-AIS at three medical center sites, DSC-MRP data were processed to generate collateral flow maps consisting of arterial, capillary, and venous phases. With the use of expert readings as a reference, a DL model was developed to analyze collateral status with output classified into good and poor grades. The resulting model was externally validated in a later-collected population from one medical center site. The model was trained on 255 patients and externally validated on 72 patients. In the all-site internal validation population, DL grading of good collateral probability yielded a c statistic of 0.91; in the external validation population, the c statistic was 0.85. In the external validation population, there was moderate agreement between the experts’ grades and DL grades (kappa = 0.53, 95% CI = 0.32–0.73, p < 0.0001). Day 7 infarct growth volume was higher in DL-graded poor collateral group than good collateral group patients (median volume [26 mL vs. 6 mL], p = 0.01) in patients with successful reperfusion (modified treatment in cerebral infarction (mTICI) = 2b–3). In all patients with a 90-day modified Rankin Scale (mRS) score, there was a shift to more favorable outcomes in the good collateral group, with a common odds ratio of 2.99 (95% CI = 1.89–4.76, p < 0.0001). The DL-based collateral grading was in good agreement with expert manual grading in both development and validation populations. After exclusion of patients with large infarct volume, early reperfusion is more likely to benefit patients with the poor collateral flow, and the DL method has the potential to aid the assessment of collateral status.

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Authors and Affiliations

  1. Division of Digital Healthcare, College of Software and Digital Healthcare Convergence, Yonsei University, Wonju, Republic of Korea

    Yoon-Chul Kim

  2. Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-Ro, Gangnam-gu, Seoul, 135-710, Republic of Korea

    Jong-Won Chung, Oh Young Bang, Mihee Hong, Woo-Keun Seo & Gyeong-Moon Kim

  3. Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea

    Eung Yeop Kim

  4. Department of Neurology, Ajou University Hospital, Ajou University School of Medicine, Suwon, Republic of Korea

    Jin Soo Lee & Ji Man Hong

  5. Department of Neurology, UCLA Stroke Center, University of California, Los Angeles, CA, USA

    David S. Liebeskind & Jeffrey L. Saver

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  1. Yoon-Chul Kim
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  2. Jong-Won Chung
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  3. Oh Young Bang
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  4. Mihee Hong
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  7. Eung Yeop Kim
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  11. Jeffrey L. Saver
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Correspondence to Oh Young Bang.

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Kim, YC., Chung, JW., Bang, O.Y. et al. A Deep Learning-Based Automatic Collateral Assessment in Patients with Acute Ischemic Stroke. Transl. Stroke Res. 14, 66–72 (2023). https://doi.org/10.1007/s12975-022-01036-1

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  • DOI: https://doi.org/10.1007/s12975-022-01036-1

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