Clinical Translation of Stimulated Raman Histology

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Molecular classification has transformed the diagnosis and treatment of brain tumors and created promising avenues for targeted therapies. However, the clinical impact of molecular classification for brain tumor patients has been blunted by long turnaround times (days-weeks), as well as the complex infrastructure and workflow required to access genetic and epigenetic data from clinical specimens. A system for rapid (<2 minute) molecular diagnostic screening would redefine the surgical and non-surgical treatment of diffuse gliomas. Rapid intraoperative molecular classification would identify the patients who would benefit most from radical resection from those with chemo- and/or radio-sensitive tumors where a more conservative surgical approach, relying more heavily on adjuvant treatment, might be best. Immediate molecular classification would also facilitate the use of targeted therapeutics and transform the way clinical trials in the glioma field are designed by enabling rapid identification of potential study subjects based on molecular criteria during or shortly after biopsy. Through our academic-industrial partnership with Invenio Imaging Inc, we developed, implemented and validated an accurate bedside system for rapid morphologic diagnosis through label-free stimulated Raman histologic (SRH) imaging paired with an artificial intelligence-based algorithm (Hollon et al. Nature Medicine 2020). Here, we intend to leverage and enhance our unique AI-based platform for intraoperative diagnosis to predict the molecular alterations that define diffuse gliomas within minutes of biopsy in the operating room without the need for a pathology lab or specialized analytic facility. This proposal is expected to yield an autonomous diagnostic workflow for human brain tumor molecular diagnostics. The work proposed here represents the development of a platform approach by which molecular diagnosis is unlocked through SRH and artificial intelligence, thus creating a new standard of accessibility for molecular diagnosis in human cancer.