Post-resection prognostication for oral cavity cancers (OCC) is qualitative and potentially ambiguous. A significant subset (25-37%) of Stage I/II patients still develop local recurrence after treatment with surgery alone. The long-term goal of this proposal will be to create a Quantitative Risk Model (QRM) using machine learning and artificial intelligence to predict recurrence risk for Stage I/II patients using image-based biomarkers of aggression. The objective is to develop and validate state-of-the-art systems for biomarker imaging, quantification, and modeling to accurately predict risk of recurrence in cancer patients based on image analytics. The central hypothesis is that a quantitative, artificial intelligence approach to pathology will result in significantly greater prognostic value compared with manual microscope-based analysis. The rationale for this work is that tumor aggression can be predicted from patterns present in pathology images, given the existence of histological risk models that have been clinically validated in the past; however, these risk models are not in widespread use because they are less accurate, robust, and transportable to the larger community of pathologists. This proposal will test the central hypothesis through three specific aims: (1) Develop an analysis pipeline that can accurately predict recurrence risk for Stage I/II OCC patients and identify treatment targets (e.g. adaptive local immune response and angiogenesis); (2) Demonstrate robust performance across a multi-site data cohort collected from seven national and international centers; and (3) Distil the results of QRM analysis to synoptic pathology reporting, demonstrating the ability of QRM to interface with standard clinical reporting tools. The innovation for addressing these aims comes from a unique application of active learning for training artificial intelligence to recognize tissue structures, new features for quantifying tissue architecture based on the interface between tumor and host, and a novel approach for large cross-site validation. Moreover, this proposal develops a unique mapping between computational pathology and commonly-used synoptic reporting variables, enabling rapid uptake of this work into existing clinical workflows. This research is significant because it provides personalized outcome predictions for a niche group of undertreated patients with limited options and can serve as the foundation for designing future clinical trials through identification of treatment targets. Multi-site training and evaluation, combined with AI-to-report mapping, will be broadly applicable to a large group of computational approaches, bridging the gap between engineering research labs and clinical application. The expected outcome of this work is a trained model for predicting Stage I/II OCC recurrence, identification of treatment targets, and mapping to synoptic reports, as well as a broadly-applicable workflow for the broader computational pathology community. This project will have a large positive impact on patients and surgical pathologists by enabling rapid, accurate prognosis and directed treatment plans in an easy-to-use pipeline that integrates seamlessly into existing clinical workflows.

Public Health Relevance

We aim to develop a quantitative risk model for oral cavity cancer patients, 25-37% of whom will experience debilitating post-treatment recurrence. Using state-of-the-art machine learning and artificial intelligence methods, we will develop and validate our risk model on a large multi-site cohort of patients, and develop an AI-assisted synoptic report-filling tool for integrating into clinical practice. A computational pathology approach to characterizing disease will help identify patients for whom aggressive multimodality therapy will improve outcomes and post-treatment quality of life.

National Institute of Health (NIH)
National Institute of Dental & Craniofacial Research (NIDCR)
Research Project (R01)
Project #
Application #
Study Section
Cancer Biomarkers Study Section (CBSS)
Program Officer
Wang, Chiayeng
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
State University of New York at Buffalo
Schools of Medicine
United States
Zip Code