# Automated DWI-FLAIR mismatch assessment in stroke using DWI only

## TL;DR

This study develops a deep-learning model to predict stroke mismatch using only one type of MRI scan, improving accuracy for patients with unknown stroke onset.

## Contribution

A novel deep-learning model for automated DWI-FLAIR mismatch assessment using only DWI data is developed and validated.

## Key findings

- The model achieved an AUC of 0.85 in the derivation cohort and 0.86 in the validation cohort.
- With an optimal cutoff, the model showed 70% sensitivity and 88% specificity in predicting mismatch.
- The model could help when visual rating is difficult or FLAIR imaging is unavailable.

## Abstract

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.

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.

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.

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.

Graphical abstract