Generalized inverse optimization with application to radiation therapy

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This research program will develop new tools in the field of optimization and demonstrate the application of those tools to pioneer a new paradigm in radiation therapy treatment planning. Radiation therapy is one of the primary ways to treat cancer. The current treatment planning process is complex and inefficient, typically relying on manual trial-and-error effort to produce a treatment plan. The challenge stems from the difficulty in determining appropriate parameters in the planning software that will result in an acceptable treatment.In this research program, we will develop new inverse optimization methods that can analyze historical treatments and reverse-engineer appropriate treatment parameters. Then, machine learning methods will be developed to predict these treatment parameters from an individual’s anatomy. Our new knowledge-based treatment planning framework, built on a foundation of optimization and machine learning, will be able to simultaneously improve standardization of the treatment planning process without sacrificing personalization of the treatments.