InHANCE: Imaging Innovation for Head And Neck Cancer Evaluation & Treatment Delivery

ABSTRACT: Overall Head and neck cancer afflicts nearly 65,000 persons annually in the United States, with an explosive growth, in what has now reached epidemic levels, human papilloma virus associated (HPV+) squamous carcinoma. Advances in therapy and changes in etiology have resulted in long-term survival for the majority of head and neck cancer patients; however, nearly all patients report therapy-related side effects from locoregional therapy (i.e., surgery or radiotherapy) due to normal tissue injury, such as xerostomia, osteoradionecrosis and deglutitive muscle damage, with a majority reporting one or more moderate-severe symptoms that impair quality of life. For highly curable HPV+ cancers, competing risk of therapy associated aspiration morbidity may match or exceed cancer-specific mortality, for example. Consequently, innovative strategies to identify, monitor, and prevent therapy-related normal tissue injury are a significant unmet need in public health. Because classic staging and therapy selection have focused necessarily on tumor characteristics, strategies for normal tissue injury reduction via surgical or radiotherapy modification have largely not been patient specific. Similarly, assessment and diagnosis of normal tissue damage has been symptomatic-driven and qualitative, as a reactive post-therapy measure. Using advanced imaging and computational image analysis, how best to execute image-guided interventions to reduce normal tissue injury and quantitative monitoring of spatial patient- specific normal tissue injury, remains a knowledge gap to be answered in the current proposal. Our central hypothesis is that through computationally assisted analysis of advanced imaging, we can better predict, detect, prevent, and manage normal tissue injury, and simultaneously reduce normal tissue injury from image-guided surgery/radiotherapy. This will be investigated by leveraging novel functional and anatomic imaging of spatially distinct normal tissue injury, using state-of-the art computational approaches to improve patient risk stratification and extending post-therapy mitigation of adverse late effects through image-guided surgical and chemoprevention efforts.