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- W3136288632 abstract "Objective: We previously developed two separate deep learning (DL) models to segment the ischemic core and critically hypoperfused tissue on baseline imaging of acute ischemic stroke patients. We aimed to validate the models in an external, multi-center randomized clinical trial (DEFUSE3) and compare with the current clinical standard. Methods: The DL models were previously trained in a separate dataset in which follow-up MRI, obtained at 3-7 days, was used as the reference for critically hypoperfused tissue in patients who did not reperfuse and as the reference for the ischemic core in patients who did reperfuse. For validation, we included DEFUSE3 patients with adequate quality baseline MR perfusion and a 24-hour follow-up DWI scan. The 24-hour DWI lesion served as the reference for ischemic core in patients in the thrombectomy arm and for critically hypoperfused tissue for patients in the medical arm. RAPID was used to generate perfusion maps (Tmax, cerebral blood flow, cerebral blood volume, and mean transient time). The accuracy of segmenting the ischemic core and critically hypoperfused tissue on baseline imaging was compared between the DL approach and the traditional thresholding approach implemented in RAPID. Results: In the 46 patients included for analysis, 24 were in the medical arm and 22 in the thrombectomy arm. Compared to a traditional thresholding method, the DL model segmented the ischemic core more accurately (AUC of 0.92 vs 0.72, p=0.0001 and volume difference of -8ml vs -21ml, p=0.001). Similarly, the DL model segmented critically hypoperfused tissue more accurately (AUC of 0.93 vs 0.80, p<0.0001; volume difference 14ml vs. 55ml, p=0.0005). However, great heterogeneity in final infarct was noticed in medical arm. See tables and figures. Conclusions: The DL-based critical hypoperfusion and ischemic core prediction provides more accurate prediction on final infarct than a commonly used thresholding method in this external validation." @default.
- W3136288632 created "2021-03-29" @default.
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- W3136288632 date "2021-03-01" @default.
- W3136288632 modified "2023-10-18" @default.
- W3136288632 title "Abstract P325: Validation of Deep Learning Based Critical Hypoperfusion and Ischemic Core Prediction in a Multicenter External Randomized Controlled Trial" @default.
- W3136288632 doi "https://doi.org/10.1161/str.52.suppl_1.p325" @default.
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