# Oral Dysplasia and Oral Cavity Cancer Risk in Dental and Medical Surveillance Settings Using a Chairside Chip-Based Cytopathology Tool

> **NIH NIH R01** · NEW YORK UNIVERSITY · 2022 · $767,048

## Abstract

ABSTRACT
 In the US, approximately 50,000 oral and pharyngeal cancers (OPCs) are diagnosed annually
(10/100,000 incidence). Further, oral epithelial dysplasia (OED) is about 15 times more common than OPC.
Patients diagnosed with OED are known to be at risk for malignant transformation (MT), and those treated for
oral squamous cell carcinoma (OSCC) are known to be at elevated risk for cancer recurrence (CR). There is
little consensus about the optimal clinical surveillance pathways for these patients. Individuals with a history of
OSCC and potentially malignant oral lesions (PMOLs) harboring OED/OSCC can have widely variable clinical
presentation that overlaps with oral lesions of no malignant potential. Thus, clinicians may be reluctant to perform
serial scalpel biopsies on these patients. Commercially available diagnostic adjuncts lack adequate clinical
validation across the lesion disease spectrum. When OSCC or high-grade OED is diagnosed early, there is an
opportunity to provide appropriate timely treatment, and patient outcomes can improve dramatically. Thus, there
is a compelling need for new highly effective non-invasive precision oral lesion diagnostic technologies that can
be tailored for the needs of individual patients.
 This multi-institution prospective cohort study seeks to utilize and optimize first Point-of-Care Oral
Cytopathology Tool (POCOCT), a microfluidics ensemble and single cell image-based data acquisition system
employing artificial intelligence with interpretation of >100 image features including nuclear F-actin for precision
oral lesion diagnostics to be completed. Portable diagnostic tools and embedded algorithms will be optimized for
secondary and tertiary care settings for the first time. In this R01 study, POCOCT-derived OSCC CR and OED
MT models will be developed to elucidate population and patient-specific dynamic changes in numerical index
that yield key information related to CR and risk of MT. While past efforts focused on a single time point, this
same multimodal chip-based approach will be used to sample repeatedly during surveillance to identify the value
of speed of change to MT and CR. The overarching goals of this R01 study are: (1) to determine whether
cytological signatures, when examined serially over time, can lead to better risk prediction for CR, (2) to
determine if the same signatures can lead to earlier detection of local recurrence than the traditional clinical
pathway, and (3) to further optimize the POCOCT for precision lesion diagnostics of MT and CR using newly
identified biomarkers, including nuclear F-actin, and rare cell phenotypes identified by deep learning.
 This R01 will leverage unique NIDCR-Grand Opportunity databases for a new paradigm of precision
diagnostics. High risk patients will be longitudinally monitored in secondary and tertiary care settings at intervals,
and their risk trajectory will be established over time using personalized multivariate cytological signatures as
well a...

## Key facts

- **NIH application ID:** 10344966
- **Project number:** 1R01DE031319-01
- **Recipient organization:** NEW YORK UNIVERSITY
- **Principal Investigator:** JOHN T MCDEVITT
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $767,048
- **Award type:** 1
- **Project period:** 2022-04-07 → 2027-02-28

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10344966

## Citation

> US National Institutes of Health, RePORTER application 10344966, Oral Dysplasia and Oral Cavity Cancer Risk in Dental and Medical Surveillance Settings Using a Chairside Chip-Based Cytopathology Tool (1R01DE031319-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10344966. Licensed CC0.

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