# Impact of Clot Histological and Physical Properties on Revascularization Strategies in Acute Ischaemic Stroke - Administrative Supplement

> **NIH NIH R01** · MAYO CLINIC ROCHESTER · 2021 · $315,871

## Abstract

PROJECT SUMMARY
In the last four years, our group collected >1800 stroke emboli from centers across the US and Canada as part
of the Stroke Thromboembolism Registry of Imaging and Pathology (STRIP). The STRIP registry was our
current R01 award’s Aim 1 and is very fruitful. We have further elucidated the association between clot
histology and revascularization outcomes, as well as clot histology and imaging characteristics. The STRIP
registry has also allowed us to uncover novel mechanisms in stroke thrombosis, catalyzing thrombectomy
device research and thrombolysis related research in our lab.
We believe that the most impactful finding from our registry will be uncovering associations between
histopathology of retrieved emboli and stroke etiology. Developing tools to predict stroke etiology is important
because nearly 40% of strokes are of unknown etiology. Determine stroke etiology (i.e. cardiac source versus
large artery atherosclerosis) is important as secondary stroke prevention strategies are highly dependent on
determination of stroke etiology. However, when we performed superficial quantitative analyses examining the
relationships between fibrin, platelet, WBC and RBC density, our results were unrevealing. Still, we
hypothesize that it remains feasible to predict stroke etiology based of analysis of retrieved stroke emboli
through deep learning and machine learning approaches. Machine learning and deep learning approaches can
also aid in uncovering histological features associated with device and pharmacological failure related to stroke
revascularization.
Thus, this administrative supplement’s goal of is to 1) allow for complete digitization and online archiving of our
database of over 1800 retrieved clot specimens as well as all available anonymized clinical data from Aim 1 of
our current R01 to facilitate deep learning and machine learning and 2) to perform deep learning on the whole
slide specimens from these patients to determine if various deep learning and machine learning algorithms can
be used to predict stroke etiology based solely off of the histological appearance of retrieved stroke emboli.

## Key facts

- **NIH application ID:** 10410193
- **Project number:** 3R01NS105853-04S1
- **Recipient organization:** MAYO CLINIC ROCHESTER
- **Principal Investigator:** Waleed Brinjikji
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $315,871
- **Award type:** 3
- **Project period:** 2021-09-01 → 2022-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10410193

## Citation

> US National Institutes of Health, RePORTER application 10410193, Impact of Clot Histological and Physical Properties on Revascularization Strategies in Acute Ischaemic Stroke - Administrative Supplement (3R01NS105853-04S1). Retrieved via AI Analytics 2026-05-29 from https://api.ai-analytics.org/grant/nih/10410193. Licensed CC0.

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