Precision imaging for risk stratification and personalized therapy of oropharyngeal cancer

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ABSTRACT During the past few decades, there has been a rapid increase in the incidence of oropharyngeal cancer (OPC), which is attributable to the epidemic of oral human papillomavirus (HPV) infection. Patients with HPV-positive OPC respond well to concurrent chemoradiotherapy and have a more favorable prognosis than HPV-negative patients. However, standard treatment is associated with significant toxicity and likely represents over-treatment for many patients with HPV-positive disease. Several randomized clinical trials have tested novel deintensification strategies with the goal to reduce toxicity and improve patients’ quality of life while preserving the high cure rate. These trials enroll patients based on cancer stage and smoking history. However, current clinical prognostic factors are rather crude and do not accurately predict disease progression on an individual level. Reliable prognostic models are critically needed for personalized risk-adaptive therapy of OPC. To address this unmet need, we propose quantitative CT features to characterize intratumoral spatial heterogeneity and disease invasion/spread, which are known drivers of treatment resistance and disease progression. In addition to knowledge-based image features, we will develop complementary data-driven deep learning models to predict disease progression by using a retrospective multi- institutional dataset of 1771 patients. Further, we will integrate imaging with clinical data to improve prediction and establish their validity by rigorous prospective validation in 1780 patients enrolled in 3 randomized clinical trials. Finally, we will employ a radiogenomic approach to elucidate biological basis of the imaging signatures. If successful, the proposed models will allow more accurate prediction of prognosis and improve risk stratification of OPC. This has significant therapeutic implications by optimizing the selection of patients for treatment deintensification, which will increase the likelihood of success of future clinical trials and pave the way for precision medicine in OPC. Because the information is derived from standard CT scans, this would be readily integrated into current clinical workflow, widely applicable to underserved populations in low-resource settings, and therefore would help reduce health disparity in the US.