Multimodal CT-based imaging biomarkers for mechanical reperfusion in acute ischemic stroke

Area of Science:

• Neurology
• Radiology
• Biomedical Engineering

Background:

• Mechanical thrombectomy (MT) is the leading treatment for acute ischemic stroke (AIS) with large vessel occlusion (LVO).
• The focus has shifted from "time is brain" to "tissue is brain," emphasizing the role of multimodal imaging.
• Computed tomography (CT) is currently used to assess infarct core and ischemic penumbra for MT candidate selection.

Purpose of the Study:

• To review stroke imaging biomarkers for predicting AIS pathogenesis, treatment response, and prognosis.
• To explore the potential of artificial intelligence (AI) in analyzing these biomarkers.
• To provide physicians with enhanced decision-making support for AIS management.

Main Methods:

• Comprehensive review of existing literature on stroke imaging biomarkers.
• Analysis of CT signatures related to infarct core, penumbra, and thrombus.
• Discussion of AI applications and future directions in stroke imaging.

Main Results:

• Identified key imaging biomarkers for ischemic core, thrombus composition, stroke etiology, and reperfusion.
• Critically compared CT signatures, highlighting controversies and limitations.
• Synthesized evidence for clinical translation and discussed AI's emerging role.

Conclusions:

• Stroke imaging biomarkers hold significant potential for improving AIS patient management.
• Further research and AI integration are crucial for clinical translation and optimizing MT outcomes.
• A deeper understanding of imaging features can enhance diagnostic accuracy and prognostic prediction.