# Clinical Translation of Stimulated Raman Histology

> **NIH NIH R01** · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · 2022 · $492,852

## Abstract

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.

## Key facts

- **NIH application ID:** 10445765
- **Project number:** 2R01CA226527-05
- **Recipient organization:** NEW YORK UNIVERSITY SCHOOL OF MEDICINE
- **Principal Investigator:** Daniel Orringer
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $492,852
- **Award type:** 2
- **Project period:** 2018-08-01 → 2027-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10445765

## Citation

> US National Institutes of Health, RePORTER application 10445765, Clinical Translation of Stimulated Raman Histology (2R01CA226527-05). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10445765. Licensed CC0.

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