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Prediction of cerebral hemorrhagic transformation after thrombectomy using a deep learning of dual-energy CT

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Abstract

Objectives

To develop and validate a deep learning model for predicting hemorrhagic transformation after endovascular thrombectomy using dual-energy computed tomography (CT).

Materials and methods

This was a retrospective study from a prospective registry of acute ischemic stroke. Patients admitted between May 2019 and February 2023 who underwent endovascular thrombectomy for acute anterior circulation occlusions were enrolled. Hemorrhagic transformation was defined using follow-up magnetic resonance imaging or CT. The deep learning model was developed using post-thrombectomy dual-energy CT to predict hemorrhagic transformation within 72 h. Temporal validation was performed with patients who were admitted after July 2022. The deep learning model’s performance was compared with a logistic regression model developed from clinical variables using the area under the receiver operating characteristic curve (AUC).

Results

Total of 202 patients (mean age 71.4 years ± 14.5 [standard deviation], 92 men) were included, with 109 (54.0%) patients having hemorrhagic transformation. The deep learning model performed consistently well, showing an average AUC of 0.867 (95% confidence interval [CI], 0.815–0.902) upon five-fold cross validation and AUC of 0.911 (95% CI, 0.774–1.000) with the test dataset. The clinical variable model showed an AUC of 0.775 (95% CI, 0.709–0.842) on the training dataset (p < 0.01) and AUC of 0.634 (95% CI, 0.385–0.883) on the test dataset (p = 0.06).

Conclusion

A deep learning model was developed and validated for prediction of hemorrhagic transformation after endovascular thrombectomy in patients with acute stroke using dual-energy computed tomography.

Clinical relevance statement

This study demonstrates that a convolutional neural network (CNN) can be utilized on dual-energy computed tomography (DECT) for the accurate prediction of hemorrhagic transformation after thrombectomy. The CNN achieves high performance without the need for region of interest drawing.

Key Points

• Iodine leakage on dual-energy CT after thrombectomy may be from blood-brain barrier disruption.

• A convolutional neural network on post-thrombectomy dual-energy CT enables individualized prediction of hemorrhagic transformation.

• Iodine leakage is an important predictor of hemorrhagic transformation following thrombectomy for ischemic stroke.

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Abbreviations

AUC:

Area under the receiver operating characteristic curve

CNN:

Convolutional neural network

DECT:

Dual-energy computed tomography

DICOM:

Digital Imaging and Communications in Medicine

EVT:

Endovascular thrombectomy

MRI:

Magnetic resonance imaging

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Funding

The authors state that this work has not received any funding.

Author information

Authors and Affiliations

  1. Department of Radiology, Yonsei University College of Medicine, Seoul, Republic of Korea

    JoonNyung Heo, Byung Moon Kim, Dong Joon Kim, Seung-Koo Lee & Beomseok Sohn

  2. Department of Neurology, Yonsei University College of Medicine, Seoul, Republic of Korea

    JoonNyung Heo, Young Dae Kim & Hyo Suk Nam

  3. Bright Data LLC, Yongin, Republic of Korea

    Youngno Yoon

  4. Department of Neurosurgery, Yonsei University College of Medicine, Seoul, Republic of Korea

    Hyun Jin Han, Jung-jae Kim & Keun Young Park

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

    Beomseok Sohn

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  1. JoonNyung Heo
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Corresponding author

Correspondence to Beomseok Sohn.

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Guarantor

The scientific guarantor of this publication is Beomseok Sohn.

Conflict of interest

The authors of this manuscript declare relationships with the following companies:

Author Youngno Yoon declares the following conflict of interest statement. Although Youngno Yoon is the CEO of Bright Data, during the preparation of this research paper, Youngno Yoon solely participated as a researcher and did not utilize any resources or involvement from Bright Data. Youngno Yoon’s affiliation with Bright Data does not influence the objectivity, integrity, or impartiality of the research findings presented in this paper.

The other authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.

Statistics and biometry

Author JoonNyung Heo has significant statistical expertise.

Kyunghwa Han kindly provided her statistical advice for this manuscript.

Informed consent

Written informed consent was waived by the Institutional Review Board, owing to the retrospective nature of the study.

Ethical approval

Institutional Review Board approval was obtained. This study was approved by the Institutional Review Board of Yonsei University College of Medicine (approval number: 4-2022-0928), with a waiver of informed consent owing to the retrospective nature of the study.

Study subjects or cohorts overlap

No study subjects or cohorts have been previously reported.

Methodology

• retrospective

• observational

• performed at one institution

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J. Heo and Y. Yoon are co-first authors.

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Supplementary file 1 (PDF 206 KB)

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Heo, J., Yoon, Y., Han, H.J. et al. Prediction of cerebral hemorrhagic transformation after thrombectomy using a deep learning of dual-energy CT. Eur Radiol 34, 3840–3848 (2024). https://doi.org/10.1007/s00330-023-10432-6

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  • DOI: https://doi.org/10.1007/s00330-023-10432-6

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