NIH R01 · 2024
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…
From the public funding record at NIH RePORTER. Describes the funded project, not the reviews below.