The research objective of this Faculty Early Career Development (CAREER) project is to mathematically develop a novel cancer radiotherapy paradigm that optimally adapts treatment to the spatiotemporal evolution of a tumor's biological condition. This will be accomplished by formulating dynamic optimization models that account for uncertainty in tumor-response to radiation and allow treatment planners to modulate radiation beam intensities depending on the tumor's condition, as observed in functional images acquired prior to each treatment session. The goal will be to minimize the number of tumor cells remaining at the end of the treatment course, while limiting toxic effects of radiation on nearby healthy tissue. Efficient algorithms will be designed to solve the resulting computationally challenging stochastic optimization problems. For truly patient-specific treatment, these will be integrated with statistical learning methods that estimate response-uncertainty using information gathered over the treatment course. Computer-generated test cases will be built to validate treatment strategies and to compare them from computational efficiency and treatment efficacy perspectives.
If successful, the project will result in a rigorous mathematical foundation for a new individualized cancer treatment planning method that delivers the right radiation dose to the right location at the right time - potentially leading to improved health outcomes. The methodological ideas will also be applicable while planning treatment for other diseases. The PI will mentor graduate students, incorporate research findings into simulation workshops for pre-engineers, provide research opportunities for underrepresented undergraduates, and develop a short course for health professionals. Results will be disseminated through journal publications and scientific conferences.
