# Radiomics signatures and patient outcomes in intracerebral hemorrhage

> **NIH NIH K23** · YALE UNIVERSITY · 2024 · $1

## Abstract

PROJECT SUMMARY / ABSTRACT
The following K23 proposal is for Dr. Sam Payabvash, a Neuroradiologist and Assistant Professor of Radiology
at Yale University. Dr. Payabvash is a physician-scientist with specialized expertise at the intersection of
neuroscience, neuroimaging, and computer vision. His career goal is to find new treatment targets and to provide
personalized care for patients with cerebrovascular disease. Intracerebral hemorrhage (ICH) is one of the most
devastating cerebrovascular diseases with no effective treatment. To date, imaging markers of ICH risk-
stratification and outcome prediction have been subjective and descriptive in nature, leaving a large gap for
automated assessment of imaging feautres embedded in medical images. Preliminary results by Dr. Payabvash
have demonstrated the feasibility of a research plan to apply automated feature extraction pipelines and machine
learning algorithms to harness the information in medical images for early risk-stratification and identification of
potential treatment targets in ICH. In this proposal, Dr. Payabvash will use detailed clinical and imaging data of
3,991 patients from NIH-funded clinical trials, online archives, and institutional registries at Yale, Tufts, and
University College of London. He will apply machine-learning algorithms to identify those imaging features of
brain hemorrhage on baseline head CT scan that are related to symptom severity at presentation (aim 1). Then,
he will use imaging features of hemorrhage to identify those patients who are at risk for early expansion of
hematoma (aim 2a), or surrounding edema (aim 2b). These two “modifiable” indicators of poor outcome are
considered potential treatment targets in ICH patients. Finally, he will combine admission clinical information and
imaging features to build a risk-stratification tool for long-term outcome prediction (aim 3). Under the expert
mentorship of Dr. Kevin Sheth (Chief of Neurocritical Care), Dr. Todd Constable (Director of MRI Research), and
Dr. Ronald Coifman (Professor of Mathematics), this K23 award will allow Dr. Payabvash to (1) identify and
address the most pressing issues in cerebrovascular disease with innovative neurogaming tools; (2) gain
expertise in advanced statistical analysis of brain scans; and (3) expand his knowledge in machine learning and
computer vision for assessment of medical images. Dr. Payabvash will receive didactic training in neuroimaging
statistical analysis, machine learning, deep neural networks, and computer vision. The proposed research and
career development plans draw on the wealth of resources available at Yale, including a Regional Coordinating
Center for the NIH StrokeNet, the Center for Research Computing; High Performance Computing services, and
cutting-edge image processing and analysis infrastructure. At the conclusion of this award period, Dr. Payabvash
will be well-positioned to become an independently-funded investigator conducting high-quality research in
...

## Key facts

- **NIH application ID:** 10845374
- **Project number:** 5K23NS118056-04
- **Recipient organization:** YALE UNIVERSITY
- **Principal Investigator:** Seyedmehdi Payabvash
- **Activity code:** K23 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $1
- **Award type:** 5
- **Project period:** 2021-06-01 → 2024-11-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10845374

## Citation

> US National Institutes of Health, RePORTER application 10845374, Radiomics signatures and patient outcomes in intracerebral hemorrhage (5K23NS118056-04). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10845374. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*