March 3rd, 2017 - February 29th, 2020
Head and neck squamous carcinomas affect all ethnicities and age groups, accounting for significant mortality and therapy-related
side effects. Over 50,000 new cases are diagnosed each year in the United States, leading to large, rich repositories of patient data. For each of these cases, oncologists need to anticipate survival, oncologic, and side effect outcomes associated with treatment strategies in order to select a treatment which balances efficacy and toxicity. However, despite the wealth of data available, risk prediction algorithms for cancers are rudimentary and incorporate only a handful of patient characteristics, largely due to a lack of computational methodologies.
This project will develop a computational methodology that supports the construction of a validated precision model of patient-specific outcomes for head and neck cancers, based on demographics, toxicity, and complex imaging data. The methodology will be the first in the field to include both complex imaging and non-imaging data, while taking into account large-scale biological and clinical correlates. A web-based environment will further allow practitioners to interactively explore and communicate the model mechanics and outcomes to stakeholders. The approach is innovative through its leverage of big imaging data repositories and through its unique blend of computational modeling principles from bioengineering, statistics, and computer science (medical imaging and visualization).
From a clinical perspective, this integrative approach is novel in the field of cancer therapy. The resulting model and web-based environment will mark a significant advance in biomedical computing because it will be able to identify, for the first time, specific subgroups who are at risk for distinct oncologic, toxicity, and survival profiles. Furthermore, the currently clinical practice paradigm for head and neck treatment is stage-driven. With validation, this novel approach can identify patients based not on clinical staging, but on precision modeling using cohort data and similar sets of patients. The methodology is applicable to clinical practice involving human subjects and has the potential to change the standard of care and outcomes of treatment in the field.