Sci—Sat AM(1): Planning — 04: PARETO: A Novel Evolutionary Optimization Approach to Multi‐Objective Radiotherapy Planning

Intensity modulated radiation therapy(IMRT) aims to deliver a uniform prescribed dose of radiation to a planning target volume (PTV), while also minimizing the dose to each nearby organ at risk (OAR). IMRTtreatment planning is therefore a multi‐objective optimization problem, which can be addressed by powerful multi‐objective optimization techniques from the field of evolutionary computing. We introduce a software package called PARETO (Pareto‐Aware Radiotherapy Evolutionary Optimization), under development at the University of Manitoba and CancerCare Manitoba, which uses a sophisticated multi‐objective genetic algorithm called Ferret to find Pareto‐optimal beam orientations and fluence maps for IMRTtreatment planning. We discuss our fitness functions and a novel geometric method for parameterizing fluence maps for use with the Ferret Genetic Algorithm. We show that our method replaces manual iterative optimization methods currently used with a faster navigation of a pre‐optimized database of solutions. We present an illustrative example that applies the code to a geometric phantom with three OARs, and show that the PARETO finds two distinct classes of solutions.