The research interests of W. Oliver Tobin, M.B., B.Ch., B.A.O., Ph.D., focus on the diagnosis and treatment of inflammatory central nervous system (CNS) disorders, with an emphasis on multiple sclerosis (MS) and its differential diagnosis.
- Tumefactive MS A current example of Dr. Tobin's research involves using separation of tumefactive MS from other causes of brain mass lesion, such as glioma, brain metastasis and central nervous system lymphoma. Dr. Tobin, in collaboration with Jeanette E. Eckel Passow, Ph.D., and colleagues, is testing a germline polygenic risk score and a machine-learning tool for the evaluation of brain imaging to avoid brain biopsy in patients who present with brain mass lesions.
- Histiocytic disorders. Dr. Tobin is a founding member of Mayo Clinic's Histiocytosis Working Group (HWG), a multidisciplinary group focused on the diagnosis and management of histiocytic disorders. In collaboration with Ronald S. Go, M.D., and the HWG, Dr. Tobin has described the neurological spectrum of patients with histiocytic disorders and novel treatments for patients with these disorders, which can often be misdiagnosed as inflammatory CNS disorders.
- Chronic lymphocytic inflammation with pontine perivascular enhancement responsive to steroids (CLIPPERS). Dr. Tobin builds on prior work by B. Mark Keegan, M.D., and Sean J. Pittock, M.D., to publish diagnostic criteria for CLIPPERS, a steroid-responsive inflammatory brain disorder. Dr. Tobin maintains an ongoing repository for evaluation and management of patients with this disorder.
Significance to patient care
Dr. Tobin's research is primarily aimed at the accurate diagnosis and management of inflammatory brain disorders. As MS is commonly misdiagnosed, there is critical health need for modalities and tools to aid with accurate diagnosis.
- Multiple Sclerosis Fellowship director, Mayo Clinic College of Medicine, 2020-present
- Vice chair of practice operations, Adult Neurology, 2018-present
- Co-principal investigator, Diagnosis of Indeterminate Brain Lesions Using MRI-Based Machine Learning and Polygenic Risk Models (1R01NS113803), National Institutes of Health, 2020-2025