# Optimizing Oral Cancer Screening and Precision Management of Potentially Malignant Oral Lesions

> **NIH NIH R01** · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · 2021 · $641,960

## Abstract

Project Summary
 Despite treatment advances over the past several decades, cancer-specific survival for oral cancers
remains bleak, mostly due to the majority of cases being diagnosed at late stages. Early-stage detection of
cancers (most often oral squamous cell carcinoma (OSCC)) would enable less disfiguring, less costly therapy
with curative intent. However, limitations of traditional visual-tactile examination for oral cancerous and pre-
cancerous lesions have hindered cancer detection and support for screening. Visual inspection for separation
of benign from precancerous or cancerous lesions is inaccurate, and therefore standard practice entails
referral and scalpel biopsy of most potentially malignant oral lesions. Furthermore, approximately 20% of
potentially malignant oral lesions contain some degree of epithelial dysplasia or carcinoma, and therefore early
identification could allow curative treatment as the majority of OSCC typically starts as dysplasia, and the
degree of dysplasia is correlated with the rate of malignant transformation. Detractors of oral screening cite the
high prevalence of benign oral lesions and mild dysplasia as circumstances placing patients at risk of harms
from over-testing and over-treatment. Thus, screening efforts could be transformed by adjunctive diagnostic
tests that offer highly accurate cytopathologic information at the point of care, such as the NIDCR-supported
Point-of-Care Oral Cytopathology Tool. Computer vision-assisted precision imaging tests have recently shown
strong diagnostic performance for oral lesion characterization, but their potential pitfalls and promises must be
thoroughly investigated before clinical application. Similarly, machine learning could bolster optical tests for
visualizing potentially malignant lesions. If successful, these artificial intelligence devices could aid decision-
making, preventing unnecessary scalpel biopsies for low-risk lesions and enabling risk-stratified surveillance or
treatment. Our team of experts in computer disease simulation modeling, machine learning, oral medicine, and
economic evaluation will transform a disease simulation model to provide analysis at the point of care, and
evaluate the different potential uses of precision imaging diagnostics for translation to clinical care. We will
expand our existing disease model of potentially malignant oral lesions to represent lesion characteristics and
clinical risk categories (e.g. based on tobacco and alcohol use) through incorporation of large longitudinal
datasets (Aim 1), in order to evaluate whether artificial intelligence-assisted cytologic testing can improve the
effectiveness and cost-effectiveness of screening for low, moderate, or high risk categories (Aim 2). Finally, we
will evaluate whether adjuncts for lesion visualization render favorable effectiveness and cost effectiveness of
screening across risk categories, with or without artificial intelligence support, and develop a user interface fo...

## Key facts

- **NIH application ID:** 10298437
- **Project number:** 1R01DE030169-01A1
- **Recipient organization:** NEW YORK UNIVERSITY SCHOOL OF MEDICINE
- **Principal Investigator:** Stella Kang
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $641,960
- **Award type:** 1
- **Project period:** 2021-08-01 → 2026-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10298437

## Citation

> US National Institutes of Health, RePORTER application 10298437, Optimizing Oral Cancer Screening and Precision Management of Potentially Malignant Oral Lesions (1R01DE030169-01A1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10298437. Licensed CC0.

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