# A multicenter study in bronchoscopy combining Stimulated Raman Histology with Artificial intelligence for rapid lung cancer detection - The ON-SITE study

> **NIH NIH R44** · INVENIO IMAGING INC. · 2023 · $945,279

## Abstract

Project Summary & Abstract
Lung cancer accounts for about 25% of all cancer deaths in the US, as it is often caught at an
advanced stage when treatment options are limited. This has led to the institution of screening
programs with low-dose CT, resulting in ~1.6 million pulmonary nodules detected every year,
most of them (~80%) in the periphery of the lung.
Tissue biopsy is the standard of care for establishing a definitive diagnosis for treatment
planning. Rapid on-site evaluation (ROSE) of tissue biopsies in the procedure room by
cytopathologists or cytotechnicians can be used to establish that:
 1. Diagnostic quality tissue has been procured; identifying malignancy by ROSE has been
 shown to reduce the number of biopsy tools and repeat procedures
 2. An adequate number of cells have been obtained to allow molecular profiling by next-
 generation sequencing and molecular testing
 3. The absence of metastatic disease in the lymph nodes justifies the more-invasive
 peripheral biopsy.
Nevertheless, ROSE is not the standard of care despite its advantages, because its quality is
highly variable across sites, it can increase procedure times, and its costs are unlikely to be fully
reimbursed. We propose the development of an FDA-cleared medical device that allows
microscopic imaging of fresh, unprocessed tissue biopsies in the treatment room and provides
accurate, near real-time diagnosis based on deep learning.
Specifically, the proposed system uses Stimulated Raman Histology (SRH), which was
pioneered by the PI, translated for intraoperative diagnosis in brain tumors, shown to be suitable
for automated diagnosis via deep learning with a performance that is non-inferior to
pathologists, and resulted in the first CE-certified device for identification of brain tumor margins
by the company. Here, we address an urgent clinical need in lung cancer, giving doctors
confidence in their intraoperative decisions, and reducing unnecessary biopsies/procedures for
patients and payers.
Further, we will investigate the potential to provide accurate intraoperative diagnosis of
molecular markers that could enable local delivery of pharmaceuticals, ablation, and other
therapies, in the biopsy procedure.

## Key facts

- **NIH application ID:** 10698382
- **Project number:** 1R44CA281581-01
- **Recipient organization:** INVENIO IMAGING INC.
- **Principal Investigator:** Allen Cole Burks
- **Activity code:** R44 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $945,279
- **Award type:** 1
- **Project period:** 2023-03-01 → 2025-02-28

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10698382

## Citation

> US National Institutes of Health, RePORTER application 10698382, A multicenter study in bronchoscopy combining Stimulated Raman Histology with Artificial intelligence for rapid lung cancer detection - The ON-SITE study (1R44CA281581-01). Retrieved via AI Analytics 2026-06-12 from https://api.ai-analytics.org/grant/nih/10698382. Licensed CC0.

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