Oropharyngeal cancer (OPC) poses a complex therapeutic dilemma for patients and oncologists alike, made worse by the epidemic increase in new cases associated with oncogenic human papillomavirus (HPV) infection. OPC incidence in the Veteran population is increasing at a rate 3 times greater than the general US population and survival lags the US population by >20% due to a disproportionate burden of aggressive, treatment-resistant disease. These factors combine to make rapid development of precision oncology approaches to OPC a categorical imperative for the VHA. In order to safely match treatment intensity to OPC biology, biomarkers of treatment response must be developed and tailored to US Veterans with OPC. Using artificial intelligence (AI) and machine learning (ML) approaches over the last 5 years we have shown the ability to discriminate risk of recurrence following treatment in OPC, with a sensitivity and specificity which surpasses that of conventional stratification approaches. We hypothesize that ML models based on multidimensional data (pathomic and radiomic) and standard clinical-pathologic features can be integrated to generate a robust risk stratification algorithm for Veterans with OPC that can be rapidly deployed across the VHA for optimization of treatment algorithms. We will develop a predictive algorithm of chemo-radiation response in Veterans with OPC (Aim 1). We will use a 1,000 OPC patient cohort from 6 tertiary VHA institutions and the VA Hub for Computer Vision and Machine Learning in Precision Oncology (CoMPL) to curate, categorize, and integrate multidimensional data inputs of Veterans with OPC suitable for machine learning to develop the Artificial Intelligence for Risk Stratification of Oropharyngeal Carcinoma (AIROC) algorithm. AIROC will be designed to predict response to conventional chemo-radiation with a sensitivity of >95% and a specificity of >95%. We will further refine AIROC to maximize sensitivity and specificity on a secondary 400 OPC patient cohort generated from completed cooperative group trials. We will then validate the predictive potential of AIROC in a prospective cohort of Veterans with OPC (Aim 2). AIROC will be utilized to make a priori predictions of chemo-radiation response in a blinded fashion for Veterans with OPC slated for curative intent chemo-radiation using locoregional recurrence as the primary outcome measure. AIROC will be considered accurate if it correctly predicts response in 99% of low-risk patients and 90% of high-risk patients. Impact and Criteria for Success. AIROC will represent a unique chemo-radiation response algorithm built using clinically generated data readily available across the VHA that can be easily deployed across VHA facilities. In addition, the multimodal dataset will become the gold standard database for OPC in the VHA and the US broadly and will serve as a valuable hub for future discovery and validation within the cooperative group clinical trial network.