Robust Management of Motion Uncertainty in Intensity-Modulated Radiation Therapy

Thomas Bortfeld (), Timothy C. Y. Chan (), Alexei Trofimov () and John N. Tsitsiklis ()
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Thomas Bortfeld: Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts 02114
Timothy C. Y. Chan: Operations Research Center, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139
Alexei Trofimov: Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts 02114
John N. Tsitsiklis: Laboratory for Information and Decision Systems, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139

Operations Research, 2008, vol. 56, issue 6, 1461-1473

Abstract: Radiation therapy is subject to uncertainties that need to be accounted for when determining a suitable treatment plan for a cancer patient. For lung and liver tumors, the presence of breathing motion during treatment is a challenge to the effective and reliable delivery of the radiation. In this paper, we build a model of motion uncertainty using probability density functions that describe breathing motion, and provide a robust formulation of the problem of optimizing intensity-modulated radiation therapy. We populate our model with real patient data and measure the robustness of the resulting solutions on a clinical lung example. Our robust framework generalizes current mathematical programming formulations that account for motion, and gives insight into the trade-off between sparing the healthy tissues and ensuring that the tumor receives sufficient dose. For comparison, we also compute solutions to a nominal (no uncertainty) and margin (worst-case) formulation. In our experiments, we found that the nominal solution typically underdosed the tumor in the unacceptable range of 6% to 11%, whereas the robust solution underdosed by only 1% to 2% in the worst case. In addition, the robust solution reduced the total dose delivered to the main organ-at-risk (the left lung) by roughly 11% on average, as compared to the margin solution.

Keywords: linear programming; applications; health care; treatment (search for similar items in EconPapers)
Date: 2008
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Citations: View citations in EconPapers (15)

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