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February 2, 2026Stroke

Abstract DP364: Association of deep learning segmented ischemic core hypodensity on non-contrast CT with endovascular treatment benefit

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Authors

HVHenk van VoorstVYVignan YogendrakumarHJHannah Johns

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Overview

This post-hoc analysis evaluates hypodensity volume's role in EVT outcomes for patients with large ischemic cores, suggesting implications for treatment decisions.

Key Points

  • This study aims to determine how deep learning segmentation of ischemic core hypodensity relates to treatment outcomes in patients undergoing endovascular treatment.
  • Post-hoc analysis of SELECT2 trial data
  • Measured hypodense volumes on NCCT using DL and manual segmentation
  • Assessed agreement between DL and manual segmentations with concordance correlation coefficients
  • Used Zou's Modified Poisson regression to analyze associations with ambulation outcomes
  • Total hypodense volume agreement between DL and manual segmentation was 0.73 (CCC), while severely hypodense volume was 0.90 (CCC)
  • Both total and severely hypodense volumes were linked to lower independent ambulation rates after EVT
  • Severely hypodense volumes were identified as significant indicators of reduced EVT benefit

Cite This Study

Voorst et al. (2026) studied this question.

synapsesocial.com/papers/6980fc17c1c9540dea80ded0https://doi.org/10.1161/str.57.suppl_1.dp364
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