Multimodal CT Perfusion–Based Deep Learning for Predicting Stroke Lesion Outcomes in Complete and No Recanalization Scenarios
Our approach offered both successful reperfusion and unsuccessful reperfusion estimations for potential treatment outcomes, enabling clinicians to better evaluate treatment eligibility for reperfusion therapies and assess potential treatment benefits. This advancement facilitates more personalized treatment recommendations and has the potential to substantially enhance clinical decision-making in acute ischemic stroke management by providing more accurate tissue outcome predictions than conventional single-technique threshold-based approaches.
Authors
- Yasmeen George (ORCID: https://orcid.org/0000-0002-0977-0656)
- Zongyuan Ge (ORCID: https://orcid.org/0000-0002-5880-8673)
- Chushuang Chen (ORCID: https://orcid.org/0000-0001-6014-846X)
- Deval Mehta (ORCID: https://orcid.org/0000-0002-2738-2120)
- Chris Bain (ORCID: https://orcid.org/0000-0002-3142-5410)
- Kenneth K. Lau (ORCID: https://orcid.org/0000-0002-7424-9947)
- Hongxi Yang (ORCID: https://orcid.org/0000-0002-9545-5715)
- Qing Yang
- David T. Yang (ORCID: https://orcid.org/0000-0002-9488-1704)
- Jiacheng Sun (ORCID: https://orcid.org/0000-0001-5289-3451)
- Mark Parsons
- Longting Lin
Institutions
- University of Technology Sydney (AU)
- The University of Sydney (AU)
- Liverpool Hospital (AU)
- UNSW Sydney (AU)
- Apollo Medical Imaging Technology (Australia) (AU)
- Ingham Institute (AU)
- Monash University (AU)
Publication Details
- Journal
- American Journal of Neuroradiology
- Published
- 2025-09-19
- DOI
- https://doi.org/10.3174/ajnr.a9016
- Primary Topic
- Acute Ischemic Stroke Management
- Type
- article
- Field-Weighted Citation Impact
- 0.00