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

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 for the model (Aim 3). This work will produce an analytic engine to guide clinical translation of artificial intelligence- aided diagnostics for oral lesion detection and characterization, to overcome insufficient screening reliability. Project Number: 7R01DE030169-05 | Fiscal Year: 2025 | NIH Institute/Center: National Institute of Dental and Craniofacial Research (NIDCR) | Principal Investigator: Stella Kang | Institution: COLUMBIA UNIVERSITY HEALTH SCIENCES, NEW YORK, NY | Award Amount: $465,582 | Activity Code: R01 | Study Section: Clinical Data Management and Analysis Study Section[CDMA] View on NIH RePORTER: https://reporter.nih.gov/project-details/11299287