Radiomics signatures and patient outcomes in intracerebral hemorrhage

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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 advanced neuroimaging techniques and analysis aimed at improving the care of patients with cerebrovascular disease.