Matthew Pease, MD, joined the University of Pittsburgh Department of Neurological Surgery residency program in July of 2015 after graduating from the University of Southern California’s Keck School of Medicine. He received his undergraduate degree in economics from Duke University in 2010.
Prior to matriculating to medical school, Dr. Pease explored a variety of research topics including animal models of addiction through a Howard Hughes research fellowship, learning modules through fellowship at the National Institutes of Health, and game theory models of group conflict. During medical school, Dr. Pease earned an American Association Medical Student Research fellowship to investigate the epigenetics of pituitary adenomas. He continues his interests in economics and brain tumor research during residency.
Outside of neurosurgery, Dr. Pease enjoys hiking, college basketball and football, and theater.
Specialized Areas of Interest
Professional Organization Membership
Education & Training
- BA, Economics, Duke University, 2010
- MD, University of Southern California, 2015
Honors & Awards
- Natus Trauma Best Resident Clinical Abstract, American Association of Neurological Surgeons, 2021
- Congress of Neurological Surgeons Data Science Fellowship, 2020
- Runner-Up Presentation Award, Stuart Rowe Society Lectureship Day, 2017
This past year, Dr. Pease was the Congress of Neurological Surgeons Data Science Fellow. He completed two large research projects using machine learning in imaging analysis. First, he developed a model to predict long-term outcomes in severe TBI patients using a multi-institutional cohort from TRACK-TBI. This model performed well, with an area under the receiver operating curve of 0.85. For this project, Dr. Pease won the Natus Award for best resident abstract from the American Association of Neurological Surgeons and will be giving a plenary talk at the AANS 2021 annual meeting in Orlando. Dr. Pease is also creating an MRI-based model to predict tumor pathology to distinguish between GBM, CNS lymphoma, and brain metastatic disease. This model was very successful and could lead to the prevention of invasive biopsies.