Automated DWI-FLAIR mismatch assessment in stroke using DWI only Article Swipe
Joseph Benzakoun
,
Lauranne Scheldeman
,
Anke Wouters
,
Bastian Cheng
,
Martin Ebinger
,
Matthias Endres
,
Jochen B. Fiebach
,
Jens Fiehler
,
Ivana Galinović
,
Keith W. Muir
,
Norbert Nighoghossian
,
Salvador Pedraza
,
Josep Puig
,
Claus Z. Simonsen
,
Vincent Thijs
,
Götz Thomalla
,
Emilien Micard
,
Bailiang Chen
,
Bertrand Lapergue
,
Grégoire Boulouis
,
Alice Le Berre
,
Jean‐Claude Baron
,
Guillaume Turc
,
Wagih Ben Hassen
,
Olivier Naggara
,
Catherine Oppenheim
,
Robin Lemmens
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1177/23969873251362712
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1177/23969873251362712
Introduction: In Acute Ischemic Stroke (AIS), mismatch between Diffusion-Weighted Imaging (DWI) and Fluid-Attenuated Inversion-Recovery (FLAIR) helps identify patients who can benefit from thrombolysis when stroke onset time is unknown (15% of AIS). However, visual assessment has suboptimal observer agreement. Our study aims to develop and validate a Deep-Learning model for predicting DWI-FLAIR mismatch using solely DWI data. Patients and methods: This retrospective study included AIS patients from ETIS registry (derivation cohort, 2018–2024) and WAKE-UP trial (validation cohort, 2012–2017). DWI-FLAIR mismatch was rated visually. We trained a model to predict manually-labeled FLAIR visible areas (FVA) matching the DWI lesion on baseline and early follow-up MRIs, using only DWI as input. FVA-index was defined as the volume of predicted regions. Area under the ROC curve (AUC) and optimal FVA-index cutoff to predict DWI-FLAIR mismatch in the derivation cohort were computed. Validation was performed using baseline MRIs of the validation cohort. Results: The derivation cohort included 3605 MRIs in 2922 patients and the validation cohort 844 MRIs in 844 patients. FVA-index demonstrated strong predictive value for DWI-FLAIR mismatch in baseline MRIs from the derivation ( n = 2453, AUC = 0.85, 95%CI: 0.84–0.87) and validation cohort ( n = 844, AUC = 0.86, 95%CI: 0.84–0.89). With an optimal FVA-index cutoff at 0.5, we obtained a kappa of 0.54 (95%CI: 0.48–0.59), 70% sensitivity (378/537, 95%CI: 66–74%) and 88% specificity (269/307, 95%CI: 83–91%) in the validation cohort. Discussion and conclusion: The model accurately predicts DWI-FLAIR mismatch in AIS patients with unknown stroke onset. It could aid readers when visual rating is challenging, or FLAIR unavailable.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1177/23969873251362712
- https://journals.sagepub.com/doi/pdf/10.1177/23969873251362712
- OA Status
- bronze
- References
- 36
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4413776676
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4413776676Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1177/23969873251362712Digital Object Identifier
- Title
-
Automated DWI-FLAIR mismatch assessment in stroke using DWI onlyWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-08-28Full publication date if available
- Authors
-
Joseph Benzakoun, Lauranne Scheldeman, Anke Wouters, Bastian Cheng, Martin Ebinger, Matthias Endres, Jochen B. Fiebach, Jens Fiehler, Ivana Galinović, Keith W. Muir, Norbert Nighoghossian, Salvador Pedraza, Josep Puig, Claus Z. Simonsen, Vincent Thijs, Götz Thomalla, Emilien Micard, Bailiang Chen, Bertrand Lapergue, Grégoire Boulouis, Alice Le Berre, Jean‐Claude Baron, Guillaume Turc, Wagih Ben Hassen, Olivier Naggara, Catherine Oppenheim, Robin LemmensList of authors in order
- Landing page
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https://doi.org/10.1177/23969873251362712Publisher landing page
- PDF URL
-
https://journals.sagepub.com/doi/pdf/10.1177/23969873251362712Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
bronzeOpen access status per OpenAlex
- OA URL
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https://journals.sagepub.com/doi/pdf/10.1177/23969873251362712Direct OA link when available
- Concepts
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Fluid-attenuated inversion recovery, Stroke (engine), Medicine, Artificial intelligence, Computer science, Nuclear medicine, Radiology, Medical physics, Magnetic resonance imaging, Engineering, Mechanical engineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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36Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| publication_date | 2025-08-28 |
| publication_year | 2025 |
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| abstract_inverted_index.70% | 217 |
| abstract_inverted_index.844 | 162, 165 |
| abstract_inverted_index.88% | 223 |
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| abstract_inverted_index.AUC | 185, 197 |
| abstract_inverted_index.DWI | 55, 96, 106 |
| abstract_inverted_index.Our | 39 |
| abstract_inverted_index.ROC | 121 |
| abstract_inverted_index.The | 149, 235 |
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| abstract_inverted_index.844, | 196 |
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| abstract_inverted_index.when | 23, 252 |
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| abstract_inverted_index.(AUC) | 123 |
| abstract_inverted_index.(DWI) | 10 |
| abstract_inverted_index.(FVA) | 93 |
| abstract_inverted_index.0.85, | 187 |
| abstract_inverted_index.0.86, | 199 |
| abstract_inverted_index.2453, | 184 |
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| abstract_inverted_index.visual | 33, 253 |
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| abstract_inverted_index.WAKE-UP | 73 |
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| abstract_inverted_index.cohort, | 70, 76 |
| abstract_inverted_index.cohort. | 147, 231 |
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| abstract_inverted_index.develop | 43 |
| abstract_inverted_index.optimal | 125, 204 |
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| abstract_inverted_index.readers | 251 |
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| abstract_inverted_index.unknown | 28, 245 |
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| abstract_inverted_index.Ischemic | 3 |
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| abstract_inverted_index.(378/537, | 219 |
| abstract_inverted_index.66–74%) | 221 |
| abstract_inverted_index.83–91%) | 227 |
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| corresponding_institution_ids | https://openalex.org/I154526488, https://openalex.org/I204730241, https://openalex.org/I4210130152, https://openalex.org/I99464096 |
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