This project is supported by NSF CMMI 2016571: High-Fidelity Radiotherapy Treatment Planning via Dimension-Free Zeroth-Order Algorithms
Radiotherapy has long been used as a prevalent mode of cancer treatment; its effectiveness lies in using high-energy radiation to eradicate cancer cells while sparing the surrounding normal tissue. Underlying the delivery of radiotherapy are complex optimization algorithms that determine safe and effective treatment plans. The creation of accurate treatment plans is difficult due to the high dimensionality of the problems as well as to uncertainties in individual responses to radiation dosage. This project develops methods to improve algorithms that guide the delivery of precise amounts of radiation to target cells. The research results will be integrated into a continuing medical education program to facilitate collaborations between academics and medical practitioners. To attract recent high school graduates, especially those from under-represented communities, into STEM majors, the project team will participate in the STEPUP outreach program at the University of Florida.
This project aims to create fundamentally new zeroth-order algorithmic paradigms that are provably capable of mitigating the challenge of high dimensionality. The research plan will study variations of randomized gradient-free algorithms that exploit computation-facilitating structures such as sparsity and its generalizations. The project will also derive and analyze algorithms that combine optimization and deep learning methods in solving problems without the knowledge of closed-form formulations. In theory, the computational efficiency of these algorithms is expected to be almost independent of problem dimensionality, up to a logarithmic term. These algorithms will be integrated with the Monte Carlo simulators deemed the gold standard in providing accurate modeling of radiotherapy outcomes. The resulting new treatment planning engines are expected to improve plan fidelity without increasing the computational cost. Extensive experiments and comparisons of the methods will be conducted on realistic cancer treatment data.