A robust approach to IMRT optimization

Abstract

Managing uncertainty is a major challenge in radiation therapy treatment planning, including uncertainty induced by intrafraction motion, which is particularly important for tumours in the thorax and abdomen. Common methods to account for motion are to introduce a margin or to convolve the static dose distribution with a motion probability density function. Unlike previous work in this area, our development does not assume that the patient breathes according to a fixed distribution, nor is the patient required to breathe the same way throughout the treatment. Despite this generality, we create a robust optimization framework starting from the convolution method that is robust to fluctuations in breathing motion, yet spares healthy tissue better than a margin solution. We describe how to generate the data for our model using breathing motion data and we test our model on a computer phantom using data from real patients. In our numerical results, the robust solution delivers approximately 38% less dose to the healthy tissue than the margin solution, while providing the same level of protection against breathing uncertainty.

Adaptive and robust radiation therapy optimization for lung cancer

“…There are many robust optimization approaches to IMRT treatment planning. In this thesis, we build on the method developed in Bortfeld et al [2008] and Chan et al [2006], which is specifically designed to manage the instantaneous breathing motion uncertainty that may be realized during treatment sessions. Although robust optimization has been shown to be a valuable methodology for managing uncertainty due to breathing motion, it can be further improved by leveraging the fact that treatments are typically fractionated.…”

“…How can we combine the robust optimization methodology of Chan et al [2006] and Bortfeld et al [2008] with off-line ART?…”

“…In the first part of this thesis, we present a method that combines off-line adaptive radiation therapy and the robust optimization methodology of Bortfeld et al [2008] and Chan et al [2006] to treat lung cancer in the presence of breathing motion uncertainty. This method is based on adaptively updating an uncertainty set that models the breathing motion uncertainty and then solving a robust optimization problem with the new uncertainty set prior to each fraction.…”

“…In Chapter 2, we review the literature on uncertainty in IMRT treatments, adaptive radiation therapy and dose-reactive methods. In Chapter 3, we review the robust IMRT optimization methodology of Chan et al [2006] and Bortfeld et al [2008] on which this thesis builds. In Chapter 4, we develop an adaptive and robust IMRT optimization method for lung cancer.…”

Efficacy of robust optimization plan with partial‐arc VMAT for photon volumetric‐modulated arc therapy: A phantom study

“…There are limited reports of robust optimization plans for photon treatment planning systems (TPSs) 13, 14, 15. Chan, et al demonstrated the potential of using a robust optimization methodology in IMRT treatment planning to improve the sparing of healthy tissue while maintaining tumor coverage in the intrafraction breathing motion uncertainty.…”