Joseph Benzakoun, Lauranne Scheldeman, Anke Wouters, Bastian Cheng, Martin Ebinger, Matthias Endres, Jochen B Fiebach, Jens Fiehler, Ivana Galinovic, 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
{"title":"自动DWI- flair错配评估中风仅使用DWI。","authors":"Joseph Benzakoun, Lauranne Scheldeman, Anke Wouters, Bastian Cheng, Martin Ebinger, Matthias Endres, Jochen B Fiebach, Jens Fiehler, Ivana Galinovic, 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","doi":"10.1177/23969873251362712","DOIUrl":null,"url":null,"abstract":"<p><strong>Introduction: </strong>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.</p><p><strong>Patients and methods: </strong>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.</p><p><strong>Results: </strong>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 (<i>n</i> = 2453, AUC = 0.85, 95%CI: 0.84-0.87) and validation cohort (<i>n</i> = 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.</p><p><strong>Discussion and conclusion: </strong>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.</p>","PeriodicalId":46821,"journal":{"name":"European Stroke Journal","volume":" ","pages":"23969873251362712"},"PeriodicalIF":4.5000,"publicationDate":"2025-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12394211/pdf/","citationCount":"0","resultStr":"{\"title\":\"Automated DWI-FLAIR mismatch assessment in stroke using DWI only.\",\"authors\":\"Joseph Benzakoun, Lauranne Scheldeman, Anke Wouters, Bastian Cheng, Martin Ebinger, Matthias Endres, Jochen B Fiebach, Jens Fiehler, Ivana Galinovic, 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\",\"doi\":\"10.1177/23969873251362712\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Introduction: </strong>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.</p><p><strong>Patients and methods: </strong>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.</p><p><strong>Results: </strong>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 (<i>n</i> = 2453, AUC = 0.85, 95%CI: 0.84-0.87) and validation cohort (<i>n</i> = 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.</p><p><strong>Discussion and conclusion: </strong>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.</p>\",\"PeriodicalId\":46821,\"journal\":{\"name\":\"European Stroke Journal\",\"volume\":\" \",\"pages\":\"23969873251362712\"},\"PeriodicalIF\":4.5000,\"publicationDate\":\"2025-08-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12394211/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"European Stroke Journal\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1177/23969873251362712\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"CLINICAL NEUROLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"European Stroke Journal","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1177/23969873251362712","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CLINICAL NEUROLOGY","Score":null,"Total":0}
Automated DWI-FLAIR mismatch assessment in stroke using DWI only.
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.
期刊介绍：
Launched in 2016 the European Stroke Journal (ESJ) is the official journal of the European Stroke Organisation (ESO), a professional non-profit organization with over 1,400 individual members, and affiliations to numerous related national and international societies. ESJ covers clinical stroke research from all fields, including clinical trials, epidemiology, primary and secondary prevention, diagnosis, acute and post-acute management, guidelines, translation of experimental findings into clinical practice, rehabilitation, organisation of stroke care, and societal impact. It is open to authors from all relevant medical and health professions. Article types include review articles, original research, protocols, guidelines, editorials and letters to the Editor. Through ESJ, authors and researchers have gained a new platform for the rapid and professional publication of peer reviewed scientific material of the highest standards; publication in ESJ is highly competitive. The journal and its editorial team has developed excellent cooperation with sister organisations such as the World Stroke Organisation and the International Journal of Stroke, and the American Heart Organization/American Stroke Association and the journal Stroke. ESJ is fully peer-reviewed and is a member of the Committee on Publication Ethics (COPE). Issues are published 4 times a year (March, June, September and December) and articles are published OnlineFirst prior to issue publication.
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
