I am an assistant professor in the Department of Neurosurgery at the University of Michigan. I grew up in a small town in west Michigan and attended the University of Michigan for my undergraduate education. I then moved a short distance directly south for medical school at OSU, where I developed my passion for neurosurgery and studied new ways to improve the treatment of malignant brain cancer. I then came back home to Michigan Medicine for my neurosurgery residency under the training of our chair, Dr. Karin Muraszko. In addition, I completed a postdoctoral research fellowship investigating how to combine intraoperative imaging and computer science to improve the surgical treatment of brain tumors. I completed my clinical training with a fellowship in skull base surgery and neurosurgical oncology at the University of Utah with chair Dr. William Couldwell. I returned to Michigan Medicine as a neurosurgeon and research scientist because it is a premier clinical and research institution with a long history of producing surgeon-scientists who excel in academic medicine.

The objective of my research program is to improve the surgical management of skull base and malignant brain tumors by developing methods that detect, diagnose, and interpret brain tumor tissue at the time of surgery. Surgical goals are divergent depending on the underlying brain tumor diagnosis; unfortunately, we do not know the tumor diagnosis prior to surgery in the majority of patients. A major problem with our current techniques for diagnosing brain tumors during surgery is that they are time-, resource-, and labor-intensive. To address this problem, our laboratory has developed a rapid optical imaging method, called stimulated Raman histology (SRH), that can acquire high-resolution images during surgery. Moreover, my group focuses on developing algorithms to detect tumor tissue and diagnose the type of tumor in an automated fashion. We use the latest advances in artificial intelligence (AI) and machine learning to improve the speed and accuracy of intraoperative brain tumor diagnosis. Our work culminated in a multicenter prospective clinical trial, where we demonstrated that an AI-based diagnosis of SRH images was equivalent to pathologist-based interpretation of conventional histologic images for the 10 most common brain tumor types (94.6% versus 93.9% accuracy, respectively). This research laid the foundation for our efforts to improve the surgical management of brain tumor patients using state-of-the-art biomedical imaging and AI algorithms.

The future direction of our research is in two related and parallel directions. Firstly, and as an extension of our previous results, we aim not only to identify specific tumor types, but also to identify specific gene mutations present in the tumor types at the time of surgery. The current brain tumor classification system from the World Health Organization uses both histologic features and genetic mutations to classify brain tumors into diagnostic groups. Our aim is to provide surgeons with the best diagnostic data possible so that they are able to provide optimal surgical care. Secondly, we hope to use our expertise in SRH, AI, and computer science in order to discover the complex relationships between gene mutations (i.e., genotype) and the growth pattern/clinical behavior of brain tumors (i.e., phenotype). These genotype-phenotype relationships have been challenging to elucidate because they are inherently multifactorial without simple one-to-one mappings.