A mixed-integer linear programming optimization model framework for capturing expert planning style in low dose rate prostate brachytherapy

Abstract

Low dose rate (LDR) brachytherapy is a minimally invasive form of radiation therapy, used to treat prostate cancer, and it involves permanent implantation of radioactive sources (seeds) inside of the prostate gland. Treatment planning in brachytherapy involves a decision making process for the placement of the sources in order to deliver an effective dose of radiation to cancerous tissue in the prostate while sparing the surrounding healthy tissue. Such a decision making process can be modeled as a mixed-integer linear programming (MILP) problem. In this paper, we introduce a novel MILP optimization model framework for interstitial LDR prostate brachytherapy designed to explicitly mimic the qualities of treatment plans produced manually by expert planners. Our approach involves incorporating a unique set of clinically important constraints, called spatial constraints, into the optimization model. Computational results for an initial model reflecting clinical practice at our cancer center show that the treatment plans produced largely capture the spatial and dosimetric characteristics of manual plans created by expert planners.