Impact of respiratory motion on worst-case scenario optimized intensity modulated proton therapy for lung cancers

Wei Liu, Zhongxing Liao, Steven E. Schild, Zhong Liu, Heng Li, Yupeng Li, Peter C. Park, Xiaoqiang Li, Joshua Stoker, Jiajian Shen, Sameer Keole, Aman Anand, Mirek Fatyga, Lei Dong, Narayan Sahoo, Sujay Vora, William Wong, X. Ronald Zhu, Martin Bues, Radhe Mohan

Research output: Contribution to journalArticlepeer-review

46 Scopus citations


Purpose: We compared conventionally optimized intensity modulated proton therapy (IMPT) treatment plans against worst-case scenario optimized treatment plans for lung cancer. The comparison of the 2 IMPT optimization strategies focused on the resulting plans' ability to retain dose objectives under the influence of patient setup, inherent proton range uncertainty, and dose perturbation caused by respiratory motion. Methods and materials: For each of the 9 lung cancer cases, 2 treatment plans were created that accounted for treatment uncertainties in 2 different ways. The first used the conventional method: delivery of prescribed dose to the planning target volume that is geometrically expanded from the internal target volume (ITV). The second used a worst-case scenario optimization scheme that addressed setup and range uncertainties through beamlet optimization. The plan optimality and plan robustness were calculated and compared. Furthermore, the effects on dose distributions of changes in patient anatomy attributable to respiratory motion were investigated for both strategies by comparing the corresponding plan evaluation metrics at the end-inspiration and end-expiration phase and absolute differences between these phases. The mean plan evaluation metrics of the 2 groups were compared with 2-sided paired Student t tests. Results: Without respiratory motion considered, we affirmed that worst-case scenario optimization is superior to planning target volume-based conventional optimization in terms of plan robustness and optimality. With respiratory motion considered, worst-case scenario optimization still achieved more robust dose distributions to respiratory motion for targets and comparable or even better plan optimality (D95% ITV, 96.6% vs 96.1% [P = .26]; D5%- D95% ITV, 10.0% vs 12.3% [P = .082]; D1% spinal cord, 31.8% vs 36.5% [P = .035]). Conclusions: Worst-case scenario optimization led to superior solutions for lung IMPT. Despite the fact that worst-case scenario optimization did not explicitly account for respiratory motion, it produced motion-resistant treatment plans. However, further research is needed to incorporate respiratory motion into IMPT robust optimization.

Original languageEnglish (US)
Pages (from-to)e77-e86
JournalPractical Radiation Oncology
Issue number2
StatePublished - Mar 1 2015
Externally publishedYes

ASJC Scopus subject areas

  • Oncology
  • Radiology Nuclear Medicine and imaging


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