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Prediction of cerebral hemorrhagic transformation after thrombectomy using a deep learning of dual-energy CT
DC Field | Value | Language |
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dc.contributor.author | 김동준 | - |
dc.contributor.author | 김병문 | - |
dc.contributor.author | 김영대 | - |
dc.contributor.author | 김정재 | - |
dc.contributor.author | 남효석 | - |
dc.contributor.author | 박근영 | - |
dc.contributor.author | 이승구 | - |
dc.contributor.author | 한현진 | - |
dc.date.accessioned | 2024-08-19T00:17:28Z | - |
dc.date.available | 2024-08-19T00:17:28Z | - |
dc.date.issued | 2024-06 | - |
dc.identifier.issn | 0938-7994 | - |
dc.identifier.uri | https://ir.ymlib.yonsei.ac.kr/handle/22282913/200280 | - |
dc.description.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. | - |
dc.description.statementOfResponsibility | restriction | - |
dc.language | English | - |
dc.publisher | Springer International | - |
dc.relation.isPartOf | EUROPEAN RADIOLOGY | - |
dc.rights | CC BY-NC-ND 2.0 KR | - |
dc.subject.MESH | Aged | - |
dc.subject.MESH | Aged, 80 and over | - |
dc.subject.MESH | Cerebral Hemorrhage* / diagnostic imaging | - |
dc.subject.MESH | Cerebral Hemorrhage* / etiology | - |
dc.subject.MESH | Cerebral Hemorrhage* / surgery | - |
dc.subject.MESH | Deep Learning* | - |
dc.subject.MESH | Female | - |
dc.subject.MESH | Humans | - |
dc.subject.MESH | Ischemic Stroke / diagnostic imaging | - |
dc.subject.MESH | Ischemic Stroke / surgery | - |
dc.subject.MESH | Male | - |
dc.subject.MESH | Middle Aged | - |
dc.subject.MESH | Predictive Value of Tests | - |
dc.subject.MESH | Radiography, Dual-Energy Scanned Projection / methods | - |
dc.subject.MESH | Retrospective Studies | - |
dc.subject.MESH | Thrombectomy* / methods | - |
dc.subject.MESH | Tomography, X-Ray Computed* / methods | - |
dc.title | Prediction of cerebral hemorrhagic transformation after thrombectomy using a deep learning of dual-energy CT | - |
dc.type | Article | - |
dc.contributor.college | College of Medicine (의과대학) | - |
dc.contributor.department | Dept. of Radiology (영상의학교실) | - |
dc.contributor.googleauthor | JoonNyung Heo | - |
dc.contributor.googleauthor | Youngno Yoon | - |
dc.contributor.googleauthor | Hyun Jin Han | - |
dc.contributor.googleauthor | Jung-Jae Kim | - |
dc.contributor.googleauthor | Keun Young Park | - |
dc.contributor.googleauthor | Byung Moon Kim | - |
dc.contributor.googleauthor | Dong Joon Kim | - |
dc.contributor.googleauthor | Young Dae Kim | - |
dc.contributor.googleauthor | Hyo Suk Nam | - |
dc.contributor.googleauthor | Seung-Koo Lee | - |
dc.contributor.googleauthor | Beomseok Sohn | - |
dc.identifier.doi | 10.1007/s00330-023-10432-6 | - |
dc.contributor.localId | A00410 | - |
dc.contributor.localId | A00498 | - |
dc.contributor.localId | A00702 | - |
dc.contributor.localId | A06249 | - |
dc.contributor.localId | A01273 | - |
dc.contributor.localId | A01442 | - |
dc.contributor.localId | A02912 | - |
dc.contributor.localId | A05067 | - |
dc.relation.journalcode | J00851 | - |
dc.identifier.eissn | 1432-1084 | - |
dc.identifier.pmid | 37950080 | - |
dc.identifier.url | https://link.springer.com/article/10.1007/s00330-023-10432-6 | - |
dc.subject.keyword | Cerebral hemorrhage | - |
dc.subject.keyword | Computed tomography | - |
dc.subject.keyword | Deep learning | - |
dc.subject.keyword | Ischemic stroke | - |
dc.subject.keyword | Mechanical thrombolysis | - |
dc.contributor.alternativeName | Kim, Dong Joon | - |
dc.contributor.affiliatedAuthor | 김동준 | - |
dc.contributor.affiliatedAuthor | 김병문 | - |
dc.contributor.affiliatedAuthor | 김영대 | - |
dc.contributor.affiliatedAuthor | 김정재 | - |
dc.contributor.affiliatedAuthor | 남효석 | - |
dc.contributor.affiliatedAuthor | 박근영 | - |
dc.contributor.affiliatedAuthor | 이승구 | - |
dc.contributor.affiliatedAuthor | 한현진 | - |
dc.citation.volume | 34 | - |
dc.citation.number | 6 | - |
dc.citation.startPage | 3840 | - |
dc.citation.endPage | 3848 | - |
dc.identifier.bibliographicCitation | EUROPEAN RADIOLOGY, Vol.34(6) : 3840-3848, 2024-06 | - |
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