The Stroke Neuro-Imaging Phenotype Repository: An Open Data Science Platform for Stroke Research

Area of Science:

• Neurology
• Medical Imaging
• Big Data in Healthcare

Background:

• Stroke is a leading global cause of death and disability.
• Understanding stroke pathophysiology and factors influencing outcomes is crucial for reducing disease burden.
• Current knowledge gaps hinder effective drug discovery and patient outcome evaluation.

Purpose of the Study:

• To establish the Stroke Neuroimaging Phenotype Repository (SNIPR) for large-scale stroke research.
• To develop automated pipelines for extracting imaging phenotypes from brain scans.
• To integrate imaging, clinical, and genomic data for comprehensive stroke analysis.

Main Methods:

• Development of SNIPR, a multi-center repository using XNAT for storing and managing stroke patient imaging data (CT and MRI).
• Implementation of containerized, automated computational methods for image analysis and feature extraction.
• Extension of the XNAT data model to include clinical data (demographics, stroke severity, subtype, outcome).

Main Results:

• SNIPR currently hosts data from 2,246 subjects across multiple international collaborations.
• Automated pipelines, including a deep learning scan classifier and image registration/segmentation, are deployed for feature extraction.
• The system enables quantification of cerebral edema progression after ischemic stroke.

Conclusions:

• SNIPR provides a collaborative platform for data scientists and clinicians to analyze stroke.
• Automated extraction and integration of imaging and clinical data advance the understanding of stroke progression.
• This approach is vital for improving stroke patient outcomes and guiding future research.