Radiomics in Stroke Neuroimaging: Techniques, Applications, and Challenges.

Area of Science:

• Neurology
• Medical Imaging
• Radiomics

Background:

• Stroke is a major cause of global disability and mortality, incurring significant healthcare costs.
• Neuroimaging is crucial for early stroke management and treatment decisions.
• Radiomics, extracting quantitative features from images using machine learning, is established in oncology.

Purpose of the Study:

• To explore the application of radiomics in stroke management.
• To highlight radiomics' potential in advancing personalized precision medicine for stroke.
• To review radiomics' contributions, methods, challenges, and future directions in stroke research.

Main Methods:

• Utilizing machine learning to convert neuroimaging data into quantitative features (radiomics).
• Applying radiomic analysis to stroke for diagnosis, outcome prediction, and prognosis evaluation.
• Reviewing existing literature on radiomics in stroke.

Main Results:

• Radiomic analysis demonstrates potential in diagnosing stroke lesions.
• Radiomics shows promise for early prediction of stroke outcomes.
• Radiomics aids in the evaluation of long-term prognosis after stroke.

Conclusions:

• Radiomics offers valuable applications in stroke care, including diagnosis and prognosis.
• The integration of radiomics could enhance personalized medicine approaches in neurology.
• Further research and overcoming implementation challenges are needed for routine clinical use.