Evolutionary Optimization for Breast Cancer Brachytherapy Treatment Planning using BRIGHT
MO-RV-GOMEA in Optimizing Treatment Plans for Internal Irradiation of Breast Tumors
D. Toader (TU Delft - Electrical Engineering, Mathematics and Computer Science)
Peter Bosman – Mentor (TU Delft - Algorithmics)
Annibale Panichella – Graduation committee member (TU Delft - Software Engineering)
T. Alderliesten – Graduation committee member (TU Delft - Algorithmics)
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Abstract
This thesis utilizes Evolutionary Algorithms (EAs) within the BRIGHT framework for developing breast cancer brachytherapy treatment plans. We use expert knowledge and state-of-the-art EAs to formulate treatment planning as a multi-objective optimization problem whose solutions can be applied to actual patients. We propose four novel 2- and 3-objective formulations of this problem, which we implement within BRIGHT and empirically validate against anonymized data from 9 real-world patient cases. We demonstrate that all four formulations, under reasonable computational and time budgets, are capable of generating plans that match or exceed the properties of reference treatment plans. To verify the clinical relevance of our contributions, we rely on the expertise of a clinical expert who assesses whether the generated plans can be used in clinical treatment planning. The results of our empirical analysis show that 17 of the 18 plans presented to the expert are clinically acceptable and of immediate value to practitioners within the field of breast brachytherapy today.