Abstract

Robust optimization of intensity‐modulated proton therapy (IMPT) takes uncertainties into account during spot weight optimization and leads to dose distributions that are resilient to uncertainties. Previous studies demonstrated benefits of linear programming (LP) for IMPT in terms of delivery efficiency by considerably reducing the number of spots required for the same quality of plans. However, a reduction in the number of spots may lead to loss of robustness. The purpose of this study was to evaluate and compare the performance in terms of plan quality and robustness of two robust optimization approaches using LP and nonlinear programming (NLP) models. The so‐called “worst case dose” and “minmax” robust optimization approaches and conventional planning target volume (PTV)‐based optimization approach were applied to designing IMPT plans for five patients: two with prostate cancer, one with skull‐based cancer, and two with head and neck cancer. For each approach, both LP and NLP models were used. Thus, for each case, six sets of IMPT plans were generated and assessed: LP‐PTV‐based, NLP‐PTV‐based, LP‐worst case dose, NLP‐worst case dose, LP‐minmax, and NLP‐minmax. The four robust optimization methods behaved differently from patient to patient, and no method emerged as superior to the others in terms of nominal plan quality and robustness against uncertainties. The plans generated using LP‐based robust optimization were more robust regarding patient setup and range uncertainties than were those generated using NLP‐based robust optimization for the prostate cancer patients. However, the robustness of plans generated using NLP‐based methods was superior for the skull‐based and head and neck cancer patients. Overall, LP‐based methods were suitable for the less challenging cancer cases in which all uncertainty scenarios were able to satisfy tight dose constraints, while NLP performed better in more difficult cases in which most uncertainty scenarios were hard to meet tight dose limits. For robust optimization, the worst case dose approach was less sensitive to uncertainties than was the minmax approach for the prostate and skull‐based cancer patients, whereas the minmax approach was superior for the head and neck cancer patients. The robustness of the IMPT plans was remarkably better after robust optimization than after PTV‐based optimization, and the NLP‐PTV‐based optimization outperformed the LP‐PTV‐based optimization regarding robustness of clinical target volume coverage. In addition, plans generated using LP‐based methods had notably fewer scanning spots than did those generated using NLP‐based methods.

Highlights

  • Intensity-modulated proton therapy (IMPT) is potentially one of the most effective ways to treat cancer because it can deliver highly conformal and homogenous dose distributions to a target with a complex shape while maximally sparing adjacent healthy tissues.[1]

  • The linear programming (LP)-based model with conventional planning target volume (PTV)-based optimization provided brainstem and spinal cord sparing robustness in terms of the V25 inferior to that provided by the NLPPTV-based model, but we found no marked variations in the robustness of organs at risk (OARs) sparing among the different robust optimization methods

  • Our results demonstrated that the robust optimization methods created more robust intensity-modulated proton therapy (IMPT) plans in terms of target coverage and OAR sparing than did the PTV-based method

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Summary

Introduction

Intensity-modulated proton therapy (IMPT) is potentially one of the most effective ways to treat cancer because it can deliver highly conformal and homogenous dose distributions to a target with a complex shape while maximally sparing adjacent healthy tissues.[1]. The high potential of IMPT owes to the fact that protons have a finite range and a sharp dose falloff at the end of the range and that IMPT can control the range (energy) and intensity of individual beamlets. The two most important sources of uncertainty in IMPT are the beam range and patient setup uncertainties. These uncertainties can result in deviation of the delivered IMPT dose distribution from the planned distribution, which may lead to suboptimal treatment decisions and unforeseen outcomes. These uncertainties must be considered during IMPT plan optimization

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